.

The lem n

cookbook

John McCrae1, Guadalupe Aguado-de-Cea2, Paul Buitelaar3,

Philipp Cimiano1,Thierry Declerck4, Asunción Gómez Pérez2,

Jorge Gracia2, Laura Hollink5, Elena Montiel-Ponsoda2,

Dennis Spohr1 and Tobias Wunner3

1 CITEC, Universität Bielefeld

2 Universidad Politécnica de Madrid

3 DERI, National University of Ireland, Galway

4 Deutsches Forschungzentrum für künstliche Intelligenz

5 Delft University of Technology

m ««« « « ««n

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net

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1

Contents

1 The lemon core

3

1.1 Main elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

1.2 Canonical forms and preferred lexicalizations . . . . . . . . . . . . . . . . .

5

2 Modules

9

2.1 Linguistic Description Module . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

2.1.1 Linguistic properties

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

10

2.1.2 Describing phonetics . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

2.1.3 Topics and contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12

2.2 Variation Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

2.2.1 Lexicosemantic relationships . . . . . . . . . . . . . . . . . . . . . . .

14

2.2.2 Lexical variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

2.2.3 Subphrases as variation . . . . . . . . . . . . . . . . . . . . . . . . . .

16

2.2.4 Form variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

2.2.5 Translation as variation . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

2.3 Phrase Structure Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20

2.3.1 Decomposition of terms . . . . . . . . . . . . . . . . . . . . . . . . . .

20

2.3.2 Phrase structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

2.3.3 Dependency relations . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24

2.3.4 Noun phrase chunks . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

2.4 Syntax and Mapping Module

. . . . . . . . . . . . . . . . . . . . . . . . . . .

27

2.4.1 Frames

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

2.4.2 Phrase structure and frames . . . . . . . . . . . . . . . . . . . . . . . .

29

2.4.3 Predicate mapping

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30

2.4.4 Conditions

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31

2.4.5 Mapping to more complex representations . . . . . . . . . . . . . . .

32

2.4.6 Mapping adjectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

2.4.7 Correspondence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36

2.5 Morphology Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38

2.5.1 Inflection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

2.5.2 Agglutination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

3 Advanced Issues

45

3.1 Annotations and Global Restrictions . . . . . . . . . . . . . . . . . . . . . . .

45

3.1.1 Annotation schemes

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

3.1.2 Global Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46

3.2 The semantics of the lemon model . . . . . . . . . . . . . . . . . . . . . . . .

48

3.2.1 Formal model of lemon senses . . . . . . . . . . . . . . . . . . . . . . .

48

4 Relation to existing systems

51

4.1 LMF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

4.2 SKOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52

4.3 TBX

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53

A FAQ

54

B LMF comparison

58

C lemon model diagram

60

2

Introduction

lemon is a model developed in the Monnet project to be a standard for sharing lexical

information on the semantic web. lemon draws heavily from earlier work of the Monnet

members, in particular from LexInfo (Cimiano et al., 2011), LIR (Montiel-Ponsoda et al. ,

2008) and LMF (Francopoulo et al., 2006). lemon in contrast to these systems aims to be

• Concise: As few classes and definitions as needed.

• Descriptive not prescriptive: lemon uses external sources for the majority of its definitions. As such the lemon system can be extended in different ways to handle different purposes, i.e., terminological variation, morpho-syntactic description,

translation memory exchange.

• Modular: lemon divides into a number of modules, meaning it is not necessary to implement the entire model to create a functional lexicon.

• RDF-native: In order to enable sharing on the semantic web, and for interface with tools lemon is based on RDF. This also allows for greater representation of

linking between different sections of the lexicon.

1 The lemon core

LexicalForm

writtenRep:String

canonicalForm

form

otherForm

abstractForm

Word

Lexicon

entry

LexicalEntry

Phrase

language:String

isSenseOf

sense

Part

LexicalSense

reference

prefRef

altRef

isReferenceOf

hiddenRef

Ontology

1.1 Main elements

The lemon core module is intended to have a similar expressive power to that of SKOS (Miles

and Bechhofer, 2009), while providing distinctions that allow for more powerful linguis-3

tic modeling. For example the simplest version of a lemon entry is as follows:1

@base <http://www.example.org/lexicon>

@prefix ontology: <http://www.example.org/ontology#>

@prefix lemon: <http://www.monnetproject.eu/lemon#>

:myLexicon a lemon:Lexicon ;

lemon:language "en" ;

lemon:entry :animal .

:animal a lemon:LexicalEntry ;

lemon:form [ lemon:writtenRep "animal"@en ] ;

lemon:sense [ lemon:reference ontology:animal ] .

Example 1

Please note, for the course of this book we will use Turtle notation2 as it is the quick-est and most legible way to write lemon (and RDF in general). Furthermore we will

mostly omit the type declarations (e.g., myLexicon is a lemon:Lexicon), as they are gen-

erally not necessary and can in all cases be inferred from the RDF Schema document.

Also we shall assume in all cases that the prefixes lemon, ontology and the base pre-

fix are as in the first example. For the sake of completion we will present the above

example in RDF/XML.

<?xml version="1.0"?>

<rdf:RDF xmlns="http://www.example.org/lexicon#"

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:lemon="http://www.monnetproject.eu/lemon#">

<lemon:Lexicon rdf:about="myLexicon" lemon:language="en">

<lemon:entry>

<lemon:LexicalEntry rdf:about="animal">

<lemon:form rdf:parseType="Resource">

<lemon:writtenRep xml:lang="en">animal</lemon:writtenRep>

</lemon:form>

<lemon:sense rdf:parseType="Resource">

<lemon:reference rdf:resource="http://www.example.org/ontology#animal"/>

</lemon:sense>

</lemon:LexicalEntry>

</lemon:entry>

</lemon:Lexicon>

</rdf:RDF>

Example 2

This example defines the following entities

1. Lexicon: This is the lexicon containing all elements in the lexicon. This approximately corresponds to a SKOS scheme. Each lexicon is marked with a language

in the form of an ISO 639 tag.

1We include a language tag on the literals even though it can be inferred, for stylistic reasons 2http://www.w3.org/TeamSubmission/turtle/

4

2. Lexical Entry: This represents the given lexical entry.

3. Lexical Sense: Represents the relationship between the lexical entry and the

ontology entity.

4. Reference: The reference to the resource that can be described by this lexical

entry. In the examples above the reference is in a separate ontology, with its own

name space.

5. Form: A surface realisation of a given lexical entry, typically a written representation.

A second example showing two lexical entries referring to the same ontology entity

is as follows:

:myLexicon lemon:entry :animal , :creature ;

lemon:language "en" .

:animal lemon:form [ lemon:writtenRep "animal"@en ] ;

lemon:sense [ lemon:reference ontology:animal ] .

:creature lemon:form [ lemon:writtenRep "creature"@en ] ;

lemon:sense [ lemon:reference ontology:animal ] .

Example 3

This indicates that “animal” and “creature” can refer to the same ontology concept

and as such they can be considered to be “synonymous” in a sense. The semantics of

the lemon model are further discussed in section 3.2.

1.2 Canonical forms and preferred lexicalizations

lemon allows syntactic variants to be differentiated from preferred forms by sub-properties of form.

:animal lemon:canonicalForm [ lemon:writtenRep "animal"@en ] ;

lemon:otherForm [ lemon:writtenRep "animals"@en ] .

Example 4

This allows lemon to differentiate between different forms of a word and different

words. For example consider we have two labels for an ontology entity, “animal” and

“creature”. This is modeled in lemon as follows:

:animal lemon:canonicalForm [ lemon:writtenRep "animal"@en ] ;

lemon:otherForm [ lemon:writtenRep "animals"@en ] ;

lemon:sense [ lemon:reference ontology:animal ] .

:creature lemon:canonicalForm [ lemon:writtenRep "creature"@en ] ;

lemon:otherForm [ lemon:writtenRep "creatures"@en ] ;

lemon:sense [ lemon:reference ontology:animal ] .

Example 5

5

It is also possible to state the lexicon-ontology relationship in the reverse direction because reference and sense have inverse properties isReferenceOf and isSenseOf. This

allows example 5 to be stated as follows

ontology:animal

lemon:isReferenceOf [

lemon:isSenseOf [

lemon:canonicalForm [ lemon:writtenRep "animal"@en ] ;

lemon:otherForm [ lemon:writtenRep "animals"@en ]

]

] ;

lemon:isReferenceOf [

lemon:isSenseOf [

lemon:canonicalForm [ lemon:writtenRep "creature"@en ] ;

lemon:otherForm [ lemon:writtenRep "creatures"@en ]

]

] .

Example 6

It is also possible to state the lexicon without any senses or references and then

introduce an ontology mapping layer by creating links such as

:animal_sense lemon:isSenseOf :animal ;

lemon:reference ontology:animal .

Example 7

In lemon we assume that each lexical entry is not semantically disambiguated, and

that the reference provides the semantics of the term. We introduce sense to represent

those occurrences when the lexical entry is used with the given meaning. As such it

is assumed that “feline” and “cat” would not share a sense, even though they can be

considered as synonyms. Similarly “he is a cool cat” and “cats are mammals” are as-

sumed to have the same lexical entry as they exhibit the same morphological/syntactic

behaviors. This is summarized in the following diagram

Forms

cat

feline

a cool “cat”

cats

Ontology

Lexical

Sense

Element

Element

One of the most important aspects of lemon is that senses should be unique to a

given lexical entry/ontology reference pair, this means that “creature” and “animal”, should not refer to the same sense entity, but can be related using the equivalent

property. If two lexical entries do share a sense, then it is assumed that they are lexically equivalent, which may be appropriate for example for an initialism or acronym and its

full form. Similarly it should be understood that if a sense has two references then

these references are equivalent (for example by OWL’s equivalentClass) property. For

more details of this see sections ?? , 3.2 and 4.1.

In lemon for each lexically different element, a different lexical entry and sense is

used. This means that both lexical entries are considered possible representations of

the ontology entity.

6

Also like SKOS, we allow the inclusion of partial terms, which we refer to as “ab-

stract”, this is useful for representing stems, affices and other morphological units.

These are implemented by three sub-properties of form can also be used to describe

linguistically relevant differences.

• canonicalForm: The standard (“dictionary”) citation form the entry.

• otherForm: A morphological variant of the words.

• abstractForm: A stem or other non-realizable morpheme, or other non-realizable forms.

As lemon defines a form as being invariant across different orthographies, different

spellings of a word are represented by deriving a sub-property of representation. Here

it is important to include the xml:lang tag to indicate the particular usage of a given

term. For example the representation of “color” in US English spelling and “colour” in

British English spelling.

:color lemon:canonicalForm [

lemon:writtenRep "color"@en-us ;

lemon:writtenRep "colour"@en-gb ] .

Example 8

It is also important to note that SKOS’s prefLabel, altLabel and hiddenLabel do not

distinguish between syntactic preference (like canonicalForm etc) and pragmatic pref-

erence, that is whether the term is preferred for terminological reasons. To cover this

pragmatic preference lemon has three sub-properties of isReferenceOf, that cover this,

namely, prefRef, altRef, hiddenRef. prefRef represents the preferred term of an ontol-

ogy reference (there should be only one such entry), hiddenRef represents a term that

is not used for various reason (for example it is antiquated) and altRef represents any

other term. For example the following shows “tuberculosis”, with an alternative “TB”

and an antiquated term “phthisis”.

ontology:tuberculosis

lemon:prefRef [

lemon:isSenseOf [ :tuberculosis ]

] ;

lemon:altRef [

lemon:isSenseOf [ :tb ]

] ;

lemon:hiddenRef [

lemon:isSenseOf [ :phthisis ]

] .

Example 9

Between the sub-properties of form and isReferenceOf we can more precisely capture

the same semantics as SKOS’s prefLabel, altLabel and hiddenLabel. The conversion is

as follows:

7

Canonical Form

Other Form

Abstract Form

Preferred Reference of

prefLabel

altLabel

hiddenLabel

Alternative Reference of

altLabel

altLabel

hiddenLabel

Hidden Reference of

hiddenLabel

hiddenLabel

hiddenLabel

Summary of vocabulary introduced in this section

lemon element

Description

entry

States that a lexical entry is in a given lexicon

form

States a realization of a lexical entry

↳canonicalForm

Gives the dictionary form of a lexical entry

↳otherForm

Gives an alternative form of a lexical entry (In-

flectional variant)

↳abstractForm

Gives a non-display form of a lexical entry

(Non-realizable variant)

language

The language of a lexicon, specified as an ISO

639 code.

sense

Specifies the lexical entry a sense refers to

reference

Specifies a connection from a sense to a re-

source

isReferenceOf

Specifies the sense referred to by an ontology

reference. Inverse of reference

↳prefSem

Indicates the preferred entry and sense for an

ontology reference.

↳altSem

Indicates an non-preferred but valid entry and

sense for an ontology reference.

↳hiddenSem

Indicates a generally incorrect entry and

sense for an ontology reference.

isSenseOf

Indicates the lexical entry for a given sense.

Inverse of sense.

representation

Specifies a specific representation of a form

↳writtenRep

Specifies a specific written representation of

a form

Word

Indicates the lexical entry is a word

Phrase

Indicates the lexical entry is composed of mul-

tiple words

Part

Indicates the lexical entry is a part of a word

(e.g., an affix)

8

2 Modules

Linguistic

depends

Description

may use

Phrase

Structure

Morphology

Core

may use

Syntax

and

Variation

Mapping

The core of lemon represents a sufficient model for representing a simple lexicon,

however for most lexica more sophisticated description is required. This is provided by

the following modules:

• Linguistic Description: This allows linguistic properties, that are commonly found in dictionaries, may be added to elements in the lexicon

• Variation: This allows relationships between different elements of a lexicon to be described

• Phrase structure: This describes the representation of multiple word expressions within lexica

• Syntax and mapping: This concerns the representation of syntactic frames and mapping these frames to logical predicates in an ontology

• Morphology: This module provides compact mapping for a inflected and agglutinative forms of entries.

9

2.1 Linguistic Description Module

Lemon

property

Element

Thing

PartOfSpeech

Gender

...

Lexical

Lexical

Lexicon

Sense

Entry

Adjective

...

Noun

context

topic

topic

CommonNoun

ProperNoun

...

Lexical

Lexical

Linguistic Description Ontology

Context

Topic

value:String

isocat:partOfSpeech

isocat:aspect

isocat:case

isocat:degree

isocat:grammaticalGender

isocat:grammaticalNumber

isocat:grammaticalTense

isocat:voice

isocat:termProvenance

isocat:termType

...

2.1.1 Linguistic properties

One of the key challenges that lemon attempts to solve is the attachment of linguistic

information to an ontology, which is essential to providing linguistic annotations to the text. This is achieved by using a linguistic taxonomy also known as a linguistic description ontology or data category registry (Romary, 2010). Examples of these resources include the GOLD ontology or the ISOcat data category registry. For example, indicating that “cat” has a singular canonical form “cat” and a plural form “cats” can be done

as follows.3

:cat lemon:canonicalForm [ lemon:writtenRep "cat"@en ;

lemon:property isocat:singular ] .

:cat lemon:otherForm [ lemon:writtenRep "cats"@en ;

lemon:property isocat:plural ] .

Example 10

In practice it is neither feasible nor desirable to impose a complete set of all possible annotations, thus lemon does not attempt to do so, and is intended to be used with a

source of linguistic categories. Also lemon is used to define the structure and relations of the lexicon, and properties from data categories are introduced by asserting them as

sub-properties of existing lemon properties (in particular lemon:property). For example

we will now show an example that marks “ICBM” as a noun and an initialism, using

sub-properties of lemon’s property4

@prefix rdfs: <"http://www.w3.org/2000/01/rdf-schema#">

:ICBM isocat:partOfSpeech isocat:noun ;

3We base examples on ISOcat and use the “identifier” property as the basis of the URI, so we have the readable description isocat:noun instead of isocat:DC-1333

4We introduce the RDFS name space with its usual form.

10

isocat:termType isocat:initialism .

isocat:partOfSpeech rdfs:subPropertyOf lemon:property .

isocat:termType rdfs:subPropertyOf lemon:property .

Example 11

These sub-property declarations only need to be made once per lexicon. An alter-

native is to use a controlled vocabulary for lemon, such as LexInfo 25 which defines a practical set of data categories for general NLP tasks.

It is of course possible to define a lexical entry or form as having several linguistic

properties. For example, in many cases we might wish to introduce this set of data

categories again from a source such as ISOcat or GOLD. The use of multiple properties

makes it much simpler for applications to query different properties.

:eat lemon:otherForm [ lemon:writtenRep "eats"@en ;

isocat:person isocat:thirdPerson ;

isocat:grammaticalNumber isocat:singular ;

isocat:tense isocat:present ] .

isocat:person

rdfs:subPropertyOf lemon:property .

isocat:grammaticalNumber rdfs:subPropertyOf lemon:property .

isocat:tense rdfs:subPropertyOf lemon:property .

Example 12

It may also be used to define the linguistic properties of forms, e.g. to indicate

whether they are roots or stems etc. For example, the Spanish verb “pescar” has

alternative stems “pesc-” and “pesqu-” that may be useful for generating inflectional

variants.

:pescar lemon:abstractForm

[ lemon:writtenRep "pesc"@es ;

isocat:morphologicalUnit isocat:stem ] ;

lemon:abstractForm

[ lemon:writtenRep "pesqu"@es ;

isocat:morphologicalUnit isocat:stem ] .

Example 13

Morphology can also be partly handled by assigning the morphological pattern as

a type of linguistic annotation. For example the Latin verb “amare” may be stated to

have the “first conjugation” morphological pattern as such.

:amare isocat:morphologicalPattern :first_conjugation .

Example 14

This means that it is possible to use this modelling to avoid stating all inflected forms of a word. The implication for implementation however should still be that stated forms

override forms generated from a morphological pattern. This is useful as for example

the verb “speak” has a regular third person singular present form “speaks” but irregular

simple past and past participle forms “spoke” and “spoken”.

2.1.2 Describing phonetics

For representing transliterations and pronounciations, extra subproperties of representation (the super-property of writtenRep) can be introduced. For example we model the Japanese

word “日本語”, which can be transliterated to either “にほんご” in the phonetic Japanese

script Hiragana or “nihongo” in the Latin alphabet

5http://www.lexinfo.net/ontology/2.0/lexinfo

11

:nihongo lemon:canonicalForm [

lemon:writtenRep "日本語"@ja-Jpan ;

isocat:transliteration "にほんご"@ja-Hira ;

isocat:transliteration "nihongo"@ja-Latn ] .

isocat:transliteration rdfs:subPropertyOf lemon:representation .

Example 15

This sub-property relation also then allows us to develop specific versions of translit-

eration, for example “神保町” is transliterated as “jimbōchō” in the Hepburn romanization scheme (the most widely used method for transliterating Japanese) and “zinbôtyô”

in the Kunrei-shiki (ISO 3602) romanization scheme. This could be represented as follows.

:jimbocho lemon:canonicalForm [

lemon:writtenRep "神保町"@ja-Jpan ,

:hepburnTransliteration "jimbōchō"@ja-Latn ;

:kunreiTransliteration "zinbôtyô"@ja-Latn ] .

:hepburnTransliteration rdfs:subPropertyOf isocat:transliteration .

:kunreiTransliteration rdfs:subPropertyOf isocat:transliteration .

Example 16

2.1.3 Topics and contexts

As the size of a lemon lexicon grows it is necessary to manage and organize it according

to some principles. One of the first steps to this is the lexicon object, which allows

vocabulary to be grouped by creating multiple lexicon objects. This is similar to the role of schemes in SKOS, which allow different topics to be grouped. As such lemon provides

topic to indicate the topic that is shared between all lexical entries. For example the

following shows two lexicons one for animals and one for veterinary anatomy

:animal_lexicon lemon:topic :animals ;

lemon:entry :cat , :mongoose , :seahorse .

:anatomy_lexicon lemon:topic :veterinary_anatomy ;

lemon:entry :lung , :fin , :swim_bladder .

Example 17

Note that the object of topic is again a URI value, which presupposes a set of defined

topics (i.e., a topic ontology). It is often the case that this does not exist or for some reason it is better to represent topics with literal strings. In such cases, lemon’s value property can be used. Hence the above example can be rewritten as:

:animal_lexicon lemon:topic [ lemon:value "animals" ] ;

lemon:entry :cat , :mongoose , :seahorse .

:anatomy_lexicon lemon:topic [ lemon:value "veterinary anatomy" ] ;

lemon:entry :lung , :fin , :swim_bladder .

Example 18

It is not recommended that this pattern is used if topics are reused across multiple

lexicon objects, as it is more difficult to group lexicons on the same topic and using a

string may cause spelling issues.

The lemon topic can also be applied to lexical entries to give a term level description

of the topic of an entry. Of course, as with all RDF systems, many topics can be attached to a single entry. For example

12

:cat lemon:topic :domestic_animals , :cosmopolitan_species , :felidae_family .

Example 19

It is important not to confuse the topic of a lexical entry with the context of its senses.

Another important feature lemon has for terminological management is the ability to

add definitions with the definition property. For this the value property must always

be used to state the value of the definition. For example

:cat lemon:sense [

lemon:reference ontology:cat ;

lemon:definition [

lemon:value "The cat is a small domesticated carnivorous animal"@en

]

] .

Example 20

These definitions are generally intended to be used primarily in terminology man-

agement rather than for NLP systems.

In addition a particular meaning of a term may be described with a context, which

refers to pragmatic/discourse restrictions on the mapping that may be implied by either

the linguistic context. An example of a pragmatic context is given here by the usage

of the term “cat” in general discourse and “Felis Catus” in scientific discourse.

:felis_catus lemon:sense [ lemon:context isocat:technicalRegister ;

lemon:reference ontology:Cat ] .

:cat lemon:sense [ lemon:context isocat:neutralRegister ;

lemon:reference ontology:Cat ] .

Example 21

In addition contexts are useful for indicating temporal or geographic usage, and this

can be done by introducing subproperties to indicate these values. For example we

may indicate that the term “blog” has been used since 1998 as follows:

:blog lemon:canonicalForm [

lemon:writtenRep "blog"@en ]

lemon:sense [

lemon:reference foaf:weblog ;

:usedSince [ lemon:value "1998"^^xsd:gYear ] ].

:usedSince rdfs:subPropertyOf lemon:context .

Example 22

Summary of vocabulary introduced in this section

Property

Description

property

Provides a linguistic annotation. If the subject of this property is a lexical entry

this refers to all instances of the lexical entry. If the subject is a form, then it de-

scribes the inflection of the given form. If the subject is a component it describes

the morphological property of this component within a multi-word expression.

topic

Indicates the topic of a lexicon or lexical entry.

context

Indicates the context in which a given lexical sense is used

13

2.2 Variation Module

formVariant

pluralFormOf

...

LexicalForm

writtenRep:String

form

abbreviationFor

lexicalVariant

fullFormFor

...

Lexical

translationOf

Entry

closeSense

related

meronym

senseRelation

sense

...

equivalent

Lexical

hypernym

Sense

...

broader

hyponym

narrower

...

antonym

incompatible

...

Term Variation

Ontology

The variation module is used to describe relationships between objects in a lemon

lexicon. This is done primarily by three relations senseRelation, lexicalVariant and

formVariant, which describe relationships between senses, entries and forms respec-

tively.

2.2.1 Lexicosemantic relationships

It is possible to relate the sense objects using the predicate lemon:senseRelation. This

has the following sub-properties defined in lemon:

• equivalent

• incompatible

• broader

• narrower6

These can be used to state the similarity of the terms “animal” and “creature” as

follows

6Like SKOS, broader and narrower are not transitive

14

:animal lemon:canonicalForm [ lemon:writtenRep "animal"@en ] ;

lemon:sense :animal_sense .

:animal_sense lemon:reference ontology:animal ;

lemon:equivalent :creature_sense .

:creature lemon:canonicalForm [ lemon:writtenRep "creature"@en ] ;

lemon:sense :creature_sense .

:creature_sense lemon:reference ontology:animal ;

lemon:equivalent :animal_sense .

Example 23

For a further example of the usefulness of these similarities consider the case of

the French words “rivière” and “fleuve”, these refer to rivers that flow into other rivers and the sea respectively. As such this distinction can be modeled by the following

sub-graph.

:fleuve lemon:sense :fleuve_sense .

:riviere lemon:sense :riviere_sense .

:river lemon:sense :river_sense .

:fleuve_sense lemon:incompatible :riviere_sense .

:riviere_sense lemon:incompatible :fleuve_sense .

:river_sense lemon:narrower :riviere_sense ,

:fleuve_sense .

Example 24

2.2.2 Lexical variants

Another important factor of lemon is the ability to model the differences between var-

ious words and forms of a word. This is performed by the properties lexicalVariant,

formVariant and senseRelation. Again here we handle the large number of potential

annotations by referring to a data category registry to define these relations. 7

7Note, we make the technical distinction here between an “initialism” (e.g., USA) and a “acronym” (e.g., NASA)

15

:hiv lemon:canonicalForm [ lemon:writtenRep "HIV"@en ] ;

isocat:termType isocat:initialism ;

isocat:initialismFor :human_immunodeficiency_virus .

:human_immunodeficiency_virus

lemon:canonicalForm [ lemon:writtenRep "Human immunodeficiency virus"@en ] ;

isocat:termType isocat:fullForm ;

isocat:fullFormFor :hiv .

isocat:initialismFor rdfs:subPropertyOf lemon:lexicalVariant .

isocat:fullFormFor rdfs:subPropertyOf lemon:lexicalVariant .

Example 25

It is also possible to state lexical entries to be syntactic variants of another, if one

is somehow derived from another. For example, the words “lexicon”, “lexical” and

“lexicalize” are derived from a single root. This is done by means of the lexicalVariant

relation. E.g.,

:lexicon :adjectivalVariant :lexical ;

:verbalVariant :lexicalize .

:adjectivalVariant rdfs:subPropertyOf lemon:lexicalVariant .

:verbalVariant rdfs:subPropertyOf lemon:lexicalVariant .

Example 26

2.2.3 Subphrases as variation

One particular modelling that should be performed as subphrase variation is indicating

subphrases. For example we may indicate that “New York” is a subphrase of “New York

City” by introducing a property subphrase as a subproperty of lexical variant. We may

also use this modelling to indicate some syntactic properties such as the head of a

phrase.

:new_york_city lemon:canonicalForm [ lemon:writtenRep "New York City"@en ] ;

:subphrase :new_york ;

:head :city .

16

:new_york lemon:canonicalForm [ lemon:writtenRep "New York"@en ] .

:city lemon:canonicalForm [ lemon:writtenRep "City"@en ] .

:subphrase rdfs:subPropertyOf lemon:lexicalVariant .

:head rdfs:subPropertyOf lemon:lexicalVariant .

Example 27

Note that this modelling is not intended to be used for deducing the syntactic struc-

ture of a multiple word expression, as such it is not easy to deduce which words com-

pose the sub-phrase. Modelling that can provide this information is described in section

4.

2.2.4 Form variants

As we have already seen senseRelation to relate different senses of a lexical entry, and

lexicalVariant to relate different lexical entries, we will also introduce a third property formVariant, which relates different forms of the same lexical entries. This property

can often be ignored, as the preferred way to represent form variants is by attaching

an appropriate set of linguistic properties. However an alternative way to represent the

entry “animal” with a plural “animals” is using a formVariant link.

:animal lemon:canonicalForm :animal_sing_form ;

lemon:otherForm :animal_plural_form .

:animal_sing_form lemon:writtenRep "animal"@en ;

:pluralFormOf :animal_plural_form .

:animal_plural_form lemon:writtenRep "animals"@en .

:pluralFormOf rdfs:subPropertyOf lemon:formVariant .

Example 28

We leave it to the implementation of software using the lemon model to choose which

method to use, however we observe that the property/value modelling is generally

more compact than form relations.

2.2.5 Translation as variation

Translated variants are for the most part naturally included within the lemon model by

the use of references, as translated variants should share the same reference. Hence

the ontology should act as an intermediate layer linking lexical entries of different languages. However, in some cases the reference may not be available, or it may be of

interest to mark lexical entries as translations if they do not share the same reference.

In such cases, sub-properties of senseRelation can be used. It should be clear that this

translation link is between senses of words as the term should be disambiguated, even

if there is not a clear reference for the translation. For example, we will consider the

case of “cat” translated into German, and French as “Katze” and “chat” respectively.

:lexicon_en lemon:entry :cat ;

lemon:language “en” .

:lexicon_de lemon:entry :katze ;

lemon:language “de”.

:lexicon_fr lemon:entry :chat ;

lemon:language “fr”.

:cat lemon:canonicalForm [ lemon:writtenRep “cat”@en ] ;

lemon:sense :cat_sense .

17

:chat lemon:canonicalForm [ lemon:writtenRep “chat”@fr ] ;

lemon:sense [ isocat:translationOf :cat_sense ] .

:katze lemon:canonicalForm [ lemon:writtenRep “katze”@de ] ;

lemon:sense [ isocat:translationOf :cat_sense ] .

isocat:translationOf rdfs:subPropertyOf lemon:senseRelation .

Example 29

As can be seen the translation arc does not in itself state the language pairs in the

translation, however the language can be extracted from either the lexica containing

the term or from the xml:lang special property.

Modeling translation as variation may be particularly useful if you wish to indi-

cate that a word is a translation of another word. For example, consider the French

words “rivière” and “fleuve” which are both translations of the English word “river”.

However following the definitions of these words (these are given in section 2.4.7) it may be sensible to map the French words to the anonymous subclasses WaterCourse⊓∃flowsInto.WaterCourse and WaterCourse⊓∃flowsInto.Sea respectively, hence

we get the following modeling:

:river lemon:canonicalForm [ lemon:writtenRep “river”@en ] ;

lemon:sense :river_sense .

:river_sense lemon:reference ontology:WaterCourse .

:riviere lemon:canonicalForm [ lemon:writtenRep “rivière”@fr ] ;

lemon:sense [ lemon:reference [ a owl:Class

owl:equivalentTo [ owl:intersectionOf (

ontology:WaterCourse ;

owl:Restriction [

18

owl:onProperty ontology:flowsInto ;

owl:allValuesForm ontology:WaterCourse

]

)

isocat:translationOf :river_sense

]]] .

:fleuve lemon:canonicalForm [ lemon:writtenRep “fleuve”@fr ] ;

lemon:sense [ lemon:reference [ a owl:Class

owl:equivalentTo [ owl:intersectionOf (

ontology:WaterCourse ;

owl:Restriction [

owl:onProperty ontology:flowsInto ;

owl:allValuesForm ontology:Sea

]

)

isocat:translationOf :river_sense

]]] .

Example 30

Summary of vocabulary introduced in this section

Property

Description

formVariant

Describes a variant (inflected form) of a writ-

ten representation of a lexical entry

lexicalVariant

Describes a variant or derived form of a lexical

entry

senseRelation

States a relations between two senses

↳equivalent

States two senses are equivalent (symmetric)

↳incompatible

States two senses are disjoint (symmetric)

↳broader

States one sense is broader than another. In-

verse of narrower

↳narrower

States one sense is narrower than another. In-

verser of broader

19

2.3 Phrase Structure Module

decomposition

Lexical

Argument

element

Component

Entry

phraseRoot

Node

leaf

Phrase

constituent:Resource

Element

edge

2.3.1 Decomposition of terms

The base method for representing multi-word lexical entries is by decomposing them

into their component words, this is done through the usage of RDF lists of component

objects. This works as follows.

:siamese_cat lemon:decomposition (

[ lemon:element :siamese ]

[ lemon:element :cat ] ) .

Example 31

Note that the above example uses the RDF list mechanism, which, while relatively

compact in Turtle syntax, can get quite large in N-Triples form, e.g.,

:siamese_cat lemon:decomposition :siamese_cat_LoC .

:siamese_cat_LoC rdf:first :siamese_cat_C1 .

:siamese_cat_LoC rdf:rest :cat_LoC .

:siamese_cat_C1 lemon:element :siamese .

:cat_LoC rdf:first :siamese_cat_C2 .

:cat_LoC rdf:rest rdf:nil .

:siamese_cat_C2 lemon:element :cat .

20

Example 32

Compound words may also be broken up by the use of components for example, the

German word “Schweineschnitzel” is composed of “Schwein” and “Schnitzel”.8

:schweineschnitzel lemon:decomposition (

[ lemon:element :schwein ]

[ lemon:element :schnitzel ] ) .

Example 33

lemon indicates the difference between compound words and phrases by the use of

the classes Word and Phrase. As such it is possible to indicate that the composition is

a separation of a multi-word expression into words, by adding type statements for the

elements.

:siamese_cat a lemon:Phrase ;

lemon:decomposition (

[ lemon:element :siamese ]

[ lemon:element :cat ] ) .

:siamese a lemon:Word .

:cat a lemon:Word .

Example 34

A third subclass of lexical entry, Part, is provided for “part of words”, e.g., affices,

which cannot be realised by themselves but may be stored in the lexicon. For example:

:preordain lemon:decomposition (

[ lemon:element :pre ]

[ lemon:element :ordain ] ) .

:pre a lemon:Part .

:ordain a lemon:Word .

Example 35

We must note here the decomposition property is not intended for modelling phrase

structure, lemon contains a separate mechanism for modelling this described in sections

2 and 4.

2.3.2 Phrase structures

The multi-word expression extensions of lemon are intended to model phrase structures

of multi-word expressions. These are done through the use of the properties phraseRoot,

edge and leaf, which allows arbitrary graphs to be created and related. For example, the

decomposition of “human immunodeficiency virus” into “[human [immunodeficiency

[virus]]]”, can be represented as follows.

:human_immunodeficiency_virus

lemon:decomposition ( :human_component

:immunodeficiency_component

:virus_component ) ;

lemon:phraseRoot [

lemon:edge [ lemon:leaf :human_component ] ;

lemon:edge [

lemon:edge [ lemon:leaf :immunodeficiency_component ]

lemon:edge [

lemon:edge [ lemon:leaf :virus_component ]

8Note the extra “e” is a dative inflection so not modelled as a component

21

]

]

] .

:human_component lemon:element :human .

:immondeficiency_component lemon:element :immunodeficiency .

:virus_component lemon:element :virus .

Example 36

It is important to note here that the phrase structure tree is not itself ordered, but

the decomposition is, hence the order is obtained this way. It is further possible to

name the arcs and nodes, by means of the property constituent. For example, we

could extend the above example by denoting the (sub)phrases as follows:

:human_immunodeficiency_virus

lemon:decomposition ( :human_component

:immunodeficiency_component

:virus_component ) ;

lemon:phraseRoot [ lemon:constituent :NP ;

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :human_component ]

;

lemon:edge [ lemon:constituent :NP ;

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :immunodeficiency_component ] ;

lemon:edge [ lemon:constituent :NP ;

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :virus_component ]

]

]

] .

:human_component lemon:element :human .

:immondeficiency_component lemon:element :immunodeficiency .

:virus_component lemon:element :virus .

Example 37

Note that in the example all the constituents are marked as resources in the model.

This means that there must exist some description already of the valid phrase con-

stituents that exist for a certain grammar. Hence, for each grammar, there must be a

grammar description ontology.

22

It is of course possible for multiple lexical entries to share the same phrase structure

and this provides a more principled modelling of the decomposition than in section 1.

This is as follows:

:new_york_city

lemon:decomposition ( :comp1

:comp2

:comp3 ) ;

lemon:phraseRoot [

lemon:edge :new_york_node ;

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :comp3 ]

] .

:new_york

lemon:decomposition ( :comp1

:comp2 ) ;

lemon:phraseRoot :new_york_node .

:new_york_node

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :comp1 ] ;

lemon:edge [ lemon:constituent :NP ;

lemon:edge [ lemon:constituent :NN ;

lemon:leaf :comp2 ] ] .

:comp1 lemon:element :new .

:comp2 lemon:element :york .

:comp3 lemon:element :city .

Example 38

Here :new york node is reused from the “New York City” tree as the root of the “New

York” tree.

23

2.3.3 Dependency relations

Dependency grammars focus on the links between different words and so lemon is a

very suitable representation. In fact all a dependency grammar needs to do is create

sub-properties of lemon:edge in its grammar description ontology. For example we could

create a dependency parse of “big red bus” as follows:

:big_red_bus

lemon:decomposition ( :big_component

:red_component

:bus_component ) ;

lemon:phraseRoot [

:main [ lemon:leaf :bus_component ;

:attr [ lemon:leaf :red_component ]

:attr [ lemon:leaf :big_component ]

] ] .

:main rdfs:subPropertyOf lemon:edge .

:attr rdfs:subPropertyOf lemon:edge .

Example 39

It is of course possible to include both the dependency and the phrase structure

parse simultaneously.

These parses can also be useful for identifying elements in frames. For example

phrasal verbs in English, such as “switch off” can have multiple frames, namely “X

switches Y off” and “X switches off Y”. To perform this we need to be able to state a

decomposition of the phrase, which could be done as follows.

:switch_off lemon:decomposition ( :switch_component

:off_component ) .

lemon:phraseRoot [ lemon:leaf :switch_component ;

:particle [ lemon:leaf :off_component ] ] .

:particle rdfs:subPropertyOf lemon:edge .

Example 40

We will present an alternative approach to this in the next section that uses the full

parse tree (example 43).

24

2.3.4 Noun phrase chunks

It is often case that the full parse tree is not needed and only a section is required, for example the noun phrase chunks within the entry. This is modelled the same way as

other phrase structure analyses in lemon, however such trees are not complete, i.e.,

they do not have leaf relations. An example of this is given for the phrase “Finan-

cial assets at amortized cost”, which is chunked into “[Finanical Assets] [at amortized

cost]”:

:FinancialAssetsAtAmortizedCost a lemon:LexicalEntry ;

lemon:sense [

lemon:reference ifrs:FinancialAssetsAtAmortizedCost ] ;

lemon:canonicalForm [

lemon:writtenRep "finanical assets at amortized cost"@en ] ;

lemon:phraseRoot [

lemon:constituent :NP ;

lemon:edge :NPChunk , :PPChunk ] .

:FinancialAssets a lemon:LexicalEntry ;

lemon:sense [

lemon:reference ifrs:FinancialAssets ] ;

lemon:canonicalForm [

lemon:writtenRep "finanical assets"@en ] ;

lemon:phraseRoot :NPChunk .

:AtAmortizedCost a lemon:LexicalEntry ;

lemon:sense [

lemon:reference ifrs:ValueAtAmortizedCost ] ;

lemon:canonicalForm [

lemon:writtenRep "at amortized cost"@en ] ;

lemon:phraseRoot :PPChunk

.

:NPChunk lemon:constituent :NP .

:PPChunk lemon:constituent :PP .

Example 41

25

Summary of vocabulary introduced in this section

Property

Description

decomposition

Defines a list of components of a lexical entry

element

Defines the lexical entry of the given compo-

nent

phraseRoot

A pattern describing the phrase structure of a

lexical entry

edge

An edge between some nodes

leaf

A node’s reference to a component of a lexical

entry

constituent

The phrase type of a node

26

2.4 Syntax and Mapping Module

Lexical

synBehavior

Frame

Entry

isSenseOf

sense

subsense

synArg

LexicalSense

semArg

context:Resource

Argument

propertyDomain

condition:Resource

propertyRange

definition:Resource

marker

reference

isReferenceOf

subjOfProp

objOfProp

Syntactic

Ontology

isA

Role

Marker

2.4.1 Frames

lemon’s approach to syntax builds on the notion of a “frame”, which represents a stereo-

typical occurrence of a lexical entry, with a set of arguments. The frame is indicated

with the property synBehavior, and each of the argument with the property synArg. For

example, modeling a simple transitive frame in English (X eats Y) is as follows:

:eat lemon:synBehavior [ lemon:synArg :arg1 ;

lemon:synArg :arg2 ] .

Example 42

It is of course possible to relate these instances of by a subcategorization ontology

such as LexInfo9. For example, it is possible to create subproperties of synArg to represent the syntactic functions of these arguments. Hence, the above example could be

represented as follows.

:eat lemon:synBehavior [ a lexinfo:TransitiveFrame ;

lexinfo:subject :eat_subject ;

lexinfo:object :eat_object] .

lexinfo:TransitiveFrame rdfs:subClassOf lemon:Frame .

lexinfo:subject rdfs:subPropertyOf lemon:synArg .

lexinfo:object rdfs:subPropertyOf lemon:synArg .

Example 43

In many cases frames require a marker for the syntactic argument, this is generally

an adposition, particle or case inflection. These can be modeled as follows.

English: “X is the capital of Y”

:capital_of lemon:synBehavior [ lemon:synArg :subject ;

lemon:synArg :pobject ] .

:noun_pp_pobject lemon:marker :of .

9http://www.lexinfo.net/ontology/2.0/lexinfo

27

Example 44

German: “X bedarf Y [genitive case]” 10

:beduerfen lemon:synBehavior [ lemon:synArg :subject ;

lemon:synArg :object ] .

:genitiv_verb_object lemon:marker isocat:genitiveCase .

Example 45

Japanese: “XはYが好きだ” (double subject construction) (“X ha Y ga suki da” = “X

likes Y” lit. “X ( primary subject) Y ( secondary subject) is pleasing”)

:suki lemon:synBehavior [ lemon:synArg :ha_subject ;

lemon:synArg :ga_subject ] .

:ha_subject lemon:marker :ha .

:ga_subject lemon:marker :ga .

Example 46

lemon also allows for modeling using optional arguments in the frame, this is often

the case with, for example, prepositionally marked arguments in English. For example

the verb “move” can have prepositional arguments such “from”,“to”,“by”,“for”,“past”,“away”,“along”

etc. These are modeled as extra arguments to the frame but are not necessary for the

frame to give a valid syntactic realization. This is performed as follows.

:move_frame lemon:synArg :move_frame_subject ,

:move_frame_to_obj ,

:move_frame_from_obj ,

:move_frame_by_obj .

:move_frame_to_obj

lemon:marker :to ;

10PropertyValue is the class for the range of property

28

lemon:optional "true"^^xsd:boolean .

:move_frame_from_obj

lemon:marker :from ;

lemon:optional "true"^^xsd:boolean .

:move_frame_by_obj

lemon:marker :by ;

lemon:optional "true"^^xsd:boolean .

Example 47

2.4.2 Phrase structure and frames

lemon frames may also be combined with the multi-word expressions extension to give

valid parse trees for a particular frame, creating something similar to the substitu-

tion trees used in Tree-Adjoining Grammars. Here the trees are attached to the frame

instead of to the lexical entry, and use the property tree, in addition we need to in-

dicate the order of the components in the phrase structure, which is done by adding

a decomposition to the frame. Note that this decomposition unlike decompositions of

lexical entries may (and in fact must) include the arguments of the frame. For example

“X switches Y off” may generate a parse structure as follows:

:switch_off

lemon:synBehavior

[ lemon:synArg :switch_off_subject ,

lemon:synArg :switch_off_object

lemon:tree [

lemon:edge [ lemon:leaf :switch_off_subject ] ;

lemon:edge [

lemon:edge [ lemon:leaf :switch_comp ] ;

lemon:edge [

lemon:edge [ lemon:leaf :switch_off_object ] ;

lemon:edge [

lemon:edge [ lemon:leaf :off_component ]

]

]

]

] ;

lemon:decomposition (

[ lemon:element :switch_off_subject ]

[ lemon:element :switch_comp ]

[ lemon:element :switch_off_object ]

[ lemon:element :off_comp ]

)

] .

29

Example 48

2.4.3 Predicate mapping

The mapping part of this module is primarily focussed on linking the entities in the

ontology with the linguistic descriptions. This consists of primarily two tasks: describing how the arguments of an ontology predicate map to those of a syntactic frame and

adding extra conditions for the applicability of a given mapping.

We shall tackle the first part of this, which requires reifying the arguments of a prop-

erty. Although RDF supports the reification of statements, this is not a reification over all triples using a given property, rather over a single property. We instead introduce

the idea of a semantic argument, which can be related to the model via three proper-

ties, namely subjOfProp, objOfProp and isA. The former two are used if a lexical sense

has a reference to a property in the ontology, for example

:capital lemon:sense [ lemon:reference ontology:capital ;

lemon:subjOfProp :capital_sem_subj ;

lemon:objOfProp :capital_sem_obj ] .

Example 49

Similarly lemon views classes as unary predicates like in other formalisms such as

SWRL, the argument is then specified with the property isA

:cat lemon:sense [ lemon:reference ontology:Cat ;

lemon:isA :isa_cat ] .

Example 50

In fact, lemon does not use separate elements for the syntactic and semantic ar-

guments. This allows for much more economical encoding of arguments than in LMF,

without any loss in expressivity (see section 4.1).

:capital lemon:sense [ lemon:reference ontology:capital ;

lemon:subjOfProp :subject ;

lemon:objOfProp :object ] ;

30

lemon:synBehavior [ lemon:synArg :subject ;

lemon:synArg :object ] .

:capital_obj lemon:marker :of .

Example 51

2.4.4 Conditions

Conditions are another mechanism applied to senses to restrict the mapping between

the syntactic and semantic layers. Conditions are something that can be generally

tested to check whether a given lexical entry is appropriate to an ontology reference or

vica versa. One of the main usage of conditions is to describe the necessary constructs

that are beyond the true “sense” of the lexical entry, but are essential to understanding the mapping. For example the German verb “essen” is generally used for humans,

whereas “fressen” is generally used when the subject is an animal. This can be modeled

by saying the two verbs are restricted with the propertyDomain and propertyRange. This

is a condition that applies only if the object/subject of the triple in question is in the appropriate class. In fact, as lemon is based on RDF(S) and OWL and thus makes an open world assumption, such conditions are only false if it is provable that the object/subject is not in the appropriate class.

:essen lemon:sense [ lemon:reference ontology:eat ;

lemon:propertyDomain ontology:Human ] .

:fressen lemon:sense [ lemon:reference ontology:eat ;

lemon:propertyDomain ontology:NonHuman ] .

Example 52

lemon only contains these two properties however we may also extends the condition

property to new conditions as is required. For example, we take the Spanish “párajo”,

which means “bird” but is used only for small birds that can fly. So we may introduce

a property notUsedFor to indicate particular subclasses that the term may not be used

for, e.g., here we indicate that it is not used for ratites (order struthioniformes), the taxon containing ostriches, emus, rheas and kiwis:

31

:parajo lemon:canonicalForm [

lemon:writtenRep "bird"@es ];

lemon:sense [

lemon:reference ontology:Bird ;

:notUsedFor ontology:Ratite

] .

:notUsedFor rdfs:subPropertyOf lemon:condition .

Example 53

We can even include full logical conditions that can be used to test if a sense should

be used by specifying a rule in some logic that can be evaluated. We present such an

example below; it should be read as “does there exist a ?y such that for some individual

?x, flowsInto(?x,?y) and River(?y) hold)/

:riviere lemon:sense [ lemon:reference ontology:River ;

lemon:condition [ lemon:value "exists ?y : flowsInto(?x,?y), River(?y)" ] ] .

:fleuve lemon:sense [ lemon:reference ontology:River ;

lemon:condition [ lemon:value "exists ?y : flowsInto(?x,?y), Sea(?y)" ] ] .

Example 54

Readers should note we use a new property value here with a blank node. This is

because we require that the range of condition are individuals in order to guarantee

the model is OWL DL compatible. We discuss value more in section 2.1.3.

There is some overlap between conditions and contexts, however as a rule of thumb,

the use of a lexical entry for an ontology entity that violates the context would be

considered “inappropriate” by a reader, where as if the condition is violated it would

be considered “incorrect.”

2.4.5 Mapping to more complex representations

As OWL only supports unary (class), and binary (property) predicates, it is not possible

to simply map more complex syntactic structures to OWL predicates. This requires

mapping these complex predicates to structures in the ontology composed over several

predicates. For example, consider we had the following simple ontology to describe a

giving event, and a lexical entry for ”give” in the lexicon as below.

ontology:giver a owl:ObjectProperty ;

rdfs:domain ontology:GivingEvent ;

rdfs:range ontology:Giver .

ontology:givenObject a owl:ObjectProperty ;

rdfs:domain ontology:GivingEvent ;

rdfs:range ontology:Given .

ontology:givenTo a owl:ObjectProperty ;

rdfs:domain ontology:GivingEvent ;

rdfs:range ontology:Recipient .

ontology:Giver a owl:Class .

ontology:GivingEvent a owl:Class .

ontology:Given a owl:Class .

ontology:Recipient a owl:Class .

Example 55

:give a lemon:Word ;

lemon:synBehavior [ lemon:synArg :give_subj ;

lemon:synArg :give_dir_obj ;

lemon:synArg :give_ind_obj ] .

32

Example 56

lemon solves such mapping by creating a compound sense, which is composed of

atomic senses which we have described above. As such the lexical entry has a single compound sense, which is composed of atomic senses that map to the ontology reference. The composition of these senses is given by the subsense property. We will now

demonstrate how this is used to represent the “giving” ontology represented above.

:give

lemon:sense [

lemon:subsense [

lemon:reference ontology:giver ;

lemon:objOfProp :give_subj ;

lemon:subjOfProp :event ] ,

[ lemon:reference ontology:givenObject ;

lemon:objOfProp :give_dir_obj ;

lemon:subjOfProp :event ] ,

[ lemon:reference ontology:givenTo ;

lemon:objOfProp :give_ind_obj ;

lemon:subjOfProp :event ]

].

Example 57

Here the lexical entry “give” has a single sense composed of three senses, one for

each property in the ontology. Each of these sub-senses has an argument mapped to

the syntactic frame and one to a new argument element :event, which is not bound to

any syntactic frame, but instead indicates that the subject of all properties should be

the same individual.

2.4.6 Mapping adjectives

Adjectives present particular challenges in mapping and while the lemon vocabulary pre-

sented so far is suitable for most mappings, we will present design patterns explaining

33

how mapping with adjectives should be performed. For the most part adjectives are

involved in unary syntactic frames (that is a frame with a single argument), except for

the case where they have a prepositional complement, e.g., “X is similar to Y” or are

used postpositively, e.g., “X ist Y ähnlich” in German. We shall leave these binary cases as an example to the user as they function like other examples presentend above. As

such we shall only handle the case where adjectives are unary. Given that we have

assumed adjectives are unary then it is clear that on the ontology side they correspond

to classes. For example assume Red is a class in our ontology then we can map the

lexical entry “red” as follows

:red lemon:canonicalForm [ lemon:writtenRep "red"@en ] ;

lemon:sense [ lemon:reference ontology:Red ;

lemon:isA :attr ] ;

lemon:synBehavior [ lemon:synArg :attr ] .

Example 58

In fact the inclusion of adjectives as classes in the ontology is rare in general, but

lemon cannot semantically map lexical entries to semantics that is not extant in the

ontology. As such it is often necessary to the ontology vocabulary, i.e., OWL, to include these semantics to map to a lexicon. For example consider the example of an ontology

with a property color with values { green, red, blue }. As the color words are individuals it is not possible to map the syntactic predicates of “red”, “green” and “blue“ to them

without first making them into classes. This is simply done as follows

ontology:Green a owl:Class ;

owl:equivalentTo [ a owl:Restriction ;

owl:onProperty ontology:color ;

owl:hasValue ontology:green

] .

DL Syntax:

Green≡∋ color.green

Example 59

Then these classes are mappable like the previous example. An even more complex

example exists if you wish to map an adjective to a datatype property, for example

“big” to a property size. As of OWL2 there exists vocabulary to define classes in terms

of data type properties, so we may define a class of big cities, BigCity, as all cities of size greater than 500,000.

ontology:BigCity a owl:Class

owl:equivalentTo [ owl:intersectionOf (

ontology:City

[ a owl:Restriction ;

owl:onProperty ontology:size ;

owl:someValueFrom [ a rdfs:Datatype ;

owl:onDatatype xsd:integer ;

owl:withRestrictions ( [ xsd:minExclusive 500000 ] )

]

] )

] .

34

DLSyntax:

BigCity≡ City⊓∃ size.integer[ > 500000]

Example 60

Another aspect of adjectives is that they are frequently used comparatively or su-

perlatively, that is with “more” and “most.” Again this behavior can only be analyzed

if the appropriate semantics exist within the ontology. For example, consider we have

“big” and we wish to define “bigger”, this clearly gives a binary frame “X is bigger

than Y” and hence must be matched to a property in the ontology as follows, the frame

should then be marked in someway as a comparative frame, for example in LexInfo 2

this is done by the AdjectiveComparativeFrame. So we could map as follows.

:big lemon:canonicalForm [ lemon:writtenRep "big"@en ] ;

lemon:otherForm [ lemon:writtenRep "bigger"@en ;

isocat:degree isocat:comparative ] ;

lemon:synBehavior [ a lexinfo:AdjectiveComparativeFrame ;

lexinfo:copulativeSubj :subj ;

lexinfo:comparativeComplement :comp ]

lemon:sense [ lemon:reference ontology:biggerThan ;

lemon:subjOfProp :subj ;

lemon:objOfProp :comp ] .

lexinfo:AdjectiveComparativeFrame rdfs:subClassOf lemon:Frame .

lexinfo:copulativeSubj rdfs:subPropertyOf lemon:synArg .

lexinfo:comparativeComplement rdfs:subPropertyOf lemon:synArg .

Example 61

This comparative property is not currently representable in OWL, however it is pos-

sible to express it in SWRL, for example we could define the biggerThan property in the

following way

:y rdf:type swrl:Variable .

:x rdf:type swrl:Variable .

:s1 rdf:type swrl:Variable .

:s2 rdf:type swrl:Variable .

[ rdf:type swrl:Imp ;

swrl:body ( [ rdf:type swrl:ClassAtom ;

swrl:classPredicate :City ;

swrl:argument1 :x

]

[ rdf:type swrl:ClassAtom ;

swrl:classPredicate :City ;

swrl:argument1 :y

]

[ rdf:type swrl:DatavaluedPropertyAtom ;

swrl:argument2 :s1 ;

35

swrl:propertyPredicate :size ;

swrl:argument1 :x

]

[ rdf:type swrl:DatavaluedPropertyAtom ;

swrl:argument2 :s2 ;

swrl:propertyPredicate :size ;

swrl:argument1 :y

]

[ rdf:type swrl:BuiltinAtom ;

swrl:builtin swrlb:greaterThan ;

swrl:arguments ( :s1

:s2 )

]

) ;

swrl:head ( [ rdf:type swrl:IndividualPropertyAtom ;

swrl:propertyPredicate :biggerThan ;

swrl:argument1 :x ;

swrl:argument2 :y

]

) ] .

SWRL Rule:

City(?x) , City(?y) , size(?x, ?s1) , size(?y, ?s2) , greaterThan(?s1, ?s2) → biggerThan(?x,

?y)

Example 62

2.4.7 Correspondence

The key understanding of a sense is as a correspondence betwen an ontology entity

and a lexical entry and as such the set of senses in the lexicon constitute a many-to-

many mapping between lexical entries and ontology entities. This means that each

sense must apply to exactly one lexical entry and one ontology element. Thus, for any

ontology-based disambiguation task the challenge can be simply specified as selecting

the correct sense for the task. For this reason lemon provides a number of features that

should prove useful for this task, which are detailed in section ?? .

There are several reasons for defining the correspondence as an element of the lexi-

con. Firstly, to allow further description of pragmatic usage between the two terms: for

example, “cats and dogs” would be considered a natural usage whereas “Felis Catus

and dogs” would be considered odd as the pragmatic register of the two terms is dif-

ferent. Although lemon also includes features to assign words to a pragmatic context,

it does require the definition of a pragmatic taxonomy, which must be done for any

particular application of the model.

The second case is where some terms are closely lexically bound, i.e., they gen-

erally occur together. An example from French is “rivière et fleuve”, which are often

used together to express the same concept in English as “river”. It is natural that we

should wish to connect these two terms by indicating they have disjoint senses that

together constitute the idea of “river”. As the meaning of “rivière” in French is defined by Larousse as

Cours d’eau de faible ou moyenne importance qui se jette dans un autre

cours d’eau (Watercourse of small size or little importance, which flows into

another watercourse)

This can be considered in a semantic sense to be nearly equivalent to the French

term “affluent”, defined as

Un cours d’eau qui se jette dans un autre. (A watercourse which flows into

another)

36

However, a system generating “fleuve et affluent” as the equivalent concept to that of “river” would generally not be considered to be correct, due to lexical collocations.

In fact for reference resolution it may be better to assert “affluent” as a broader term

for “rivière”, as every usage of “rivière” is necessarily an “affluent”.

The final case is where terms are used in a metaphoric or metonymic manner. For

example,

The White House said today, ...

Here “the White House” does not refer to the literal building but the spokesperson

and other members of the organization in that building. As such the correspondence

would give an ontology entity with the properties of being a human or organization;

although reasoning using this information, may generate odd statements such as

*The White House is a member of the species Homo Sapiens

Thus we need to define the lexical semantics as clearly separate from those of

the ontological element. This could be done for example by asserting something as

a metonymic raising of another sense e.g.,

:white_house

lemon:canonicalForm [ lemon:writtenRep "The White House"@en ] ;

lemon:sense :white_house_sense .

:white_house_spokesperson

lemon:canonicalForm [ lemon:writtenRep

"White House Spokesperson"@ en ] ;

lemon:sense :white_house_spokesperson_sense .

:white_house_sense :metonymicRaisingOf :white_house_spokesperson_sense.

:metonymicRaisingOf rdfs:subPropertyOf lemon:senseRelation.

Summary of vocabulary introduced in this section

Property

Description

synBehavior

The frame of a lexical entry.

synArg

The syntactic slots of a frame.

marker

The case or lexical element that marks a se-

mantic argument.

optional

Indicates an argument is not required in order

to realize a frame.

tree

Indicates the a lexicalized grammar tree at-

tached to a frame.

semArg

A semantic argument

↳subjOfProp

The subject of property

↳objOfProp

The object of a property

↳isA

The argument of a class predicate

context

A contextual parameter on the mapping

condition

A condition that can be checked to see if the

given lexical entry and reference correspond

in the given context

↳propertyDomain

A condition on the domain of the property be-

ing mapped to

↳propertyRange

A condition on the range of the property being

mapped to

subsense

Indicates an atomic sense that composes a

compound sense

37

2.5 Morphology Module

The usual lemon method for representing different forms of the same lexical entry

is to indicate all non-canonical forms by the use of the otherForm property. While this

may be reasonably practical for English as there are only four, three and two forms for

verbs, adjectives and nouns respectively, there can be many forms for items of more

synthetic languages. For example, a regular verb in Italian, such as “amare”, has 43

distinct forms, and encoding all of these as seperate forms for every word would greatly

increase the size of lexicon. This problem is even more acute for languages exhibiting

polysynthesis, where multiple inflections may be combined. For example in Japanese the following (among other) inflections of the regular verb exist (here “taberu”, to eat)

.

Positive

Negative

Present

taberu

tabenai

Past

tabeta

tabenakatta

The same verb also can be inflected to give a passive form, “taberareru”, and a

causative form, “tabesaseru” 11. These forms are in fact verbs, that have the same inflections as the regular verb. To further complicate things, both the passive and

causative may be combined to give a passive-causative form “tabesaserareru”. As

such the following form of the verb exists, which may be decomposed as follows (the

meaning is “(he) was not made to eat”).

tabe

-

sase

-

rare

-

naka

-

tta

tabe

saseru

rareru

nai

ta

stem

causative

passive

negative

past

Example 63

This leads to a very large number of inflections and as such it is difficult to state how many word forms exist for a Japanese verb, although in practice it is unlikely that verbs composed of more than 5 morphemes occur. Therefore, a minimal encoding is needed

that allows regular and semi-regular verbs to be represented economically.

In difference to the majority of the vocabulary in lemon, this morphology is not spe-

cific to a given lexicon, in fact the same morphological rules can be used for any lexicon within the same language. As such, it should not be necessary for most lexica to represent the morphology of their given language, and for most major language bespoke

systems will often in practice be more useful than the modules described here. How-

ever, there are use cases where this is useful, for example for resource-poor languages

it may be preferable to have a common representation of morphology. Furthermore,

bespoke systems are often opaque about the methods they use for generating inflec-

tional morphology and this can make it difficult with words that have unusual forms (a

11The meaning of the causative is approximately “to cause to do”

38

simple example in English is that nouns ending in “y” normally have a plural in “ies”, e.g., “cherries”, but in some odd cases has a plural in “ys”, e.g., “the two Germanys”).

2.5.1 Inflection

The morphology of a word is given by a pattern, which is composed of a set of transforms, that is in turn composed of rules and produces a form described as a prototype.

To give a simple example we will look at how we represent that English nouns form their

plural in ”s”

:english_noun a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~s" ;

lemon:generates [

isocat:number isocat:plural

]

] .

Example 64

This gives a pattern, with a single rule defined as "˜s" that generates a prototype with the property number with value plural. The rules are specified in a simple manner where the tilde symbol represents the canonical form and then the “s” is simply

appended. A lexical entry may be indicated to have a given morphological pattern by

the pattern property as follows.

:cat lemon:pattern :english_noun

Example 65

The rule above is clearly not sufficient: for example for the word “cherry” it would

generate the erroneous plural “cherrys.” Another rule can be added to handle this case,

for example

:english_noun a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~s" ;

lemon:rule "~y/~ies" ;

lemon:generates [

isocat:number isocat:plural

]

] .

Example 66

In this example the slash indicates the difference between the matcher and the

replacer as such for the rule to applies the canonical form must first match the matcher and then the matched text is replaced with the replacer. The first rule has no matcher

and so is assumed to match all canonical forms that aren’t matched by a more specific

rule. 12 This rule is still not correct as it generates for “play” the form “plaies”. As such we should further modify the rule to check for a preceding vowel this is done as follows

:english_noun a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~s" ;

lemon:rule "~(<?![aeiou])y/~ies" ;

lemon:generates [

isocat:number isocat:plural

]

] .

12It is up to the lexicon author to ensure no two matchers can match the same string

39

Example 67

In fact, as this example may have hinted the underlying representation of the rules

used by this module are in fact Perl-like regular expressions13. In fact as such an alternative rule that performs the same task "˜([âeiou])y/˜$1ies"; note that here the penultimate constonant must be reintroduced as it gets removed by being matched,

and this is done with "$1". We think it is clearer to state rules using the look-ahead and look-behind assertions (namely (?=X), (?!X), (<?=X), and (<?!X)), as they do not cause vowels to be lost. Another reason for prefering these zero-width assertions is that in

implementation the tildes must be translated to (.*) and $1 respectively, and it makes

it easier to do this if there are no groups on the left hand side.

The use of the tilde is useful is as it makes it easier to represent both prefices and

suffices, for example consider the case of the German perfect participle, this is formed

for regular weak verbs by prefixing “ge” and changing the inflectional suffix to “t”. We

can model this as follows

:german_weak_verb a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~e?n/ge~t" ;

lemon:generates [

isocat:tense isocat:past ;

isocat:aspect isocat:perfective ;

isocat:verbFormMood isocat:participle

]

] .

Example 68

Of course it is natural it is possible for a pattern to have multiple transforms and even for a transform to have multiple generations. For example, we shall show an example

for Italian generating the present singular first and third person forms and then using

the third person singular to get the singular imperative

:italian_are_verb a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~are/~o" ;

lemon:generates [

isocat:tense isocat:present ;

isocat:person isocat:firstPerson ;

isocat:number isocat:singular

]

] , [

lemon:rule "~are/~a" ;

lemon:generates [

isocat:tense isocat:present ;

isocat:person isocat:firstPerson ;

isocat:number isocat:singular

] , [

isocat:verbFormMoode isocat:imperative ;

isocat:number isocat:singular

]

] .

Example 69

13There is no standard for Perl-like regular expressions, here we assume that these rules must support the functionality of POSIX regular expressions, and in addition the + operator, negated character classes, non-greedy quantifiers, shy groups, lookahead and lookbehind assertions, backreferences, directives and conditionals. This condition is satisfied by the standard regular expressions libraries for Java (and other JVM

Languages), the .NET platform, Perl, PHP and Python, the Boost.Regex library for C/C++ and the Unix grep command.

40

Sometimes the canonical form alone is not sufficient to generate all forms of the

word, for this reason other forms must be used as the base for generating some forms,

and these forms should be given in the lexicon. An example is German mixed verbs,

for example “denken”, to think, that has an irregular past “dachte”. This form is the

first (and third) person singular preterite, and the second person singular is created by adding “st” to this form. For these cases the onStem property is provided, for example:

:german_mixed_verb a lemon:MorphPattern ;

lemon:transform [

lemon:onStem [

isocat:tense isocat:past ;

isocat:verbFormMood isocat:indicative ;

isocat:person isocat:firstPerson ;

isocat:number isocat:singular

] ;

lemon:rule "~st" ;

lemon:generates [

isocat:tense isocat:past ;

isocat:verbFormMood isocat:indicative ;

isocat:person isocat:secondPerson ;

isocat:number isocat:singular

]

] .

Example 70

For polysynthesis as in example 60,we introduce a property that indicates how mul-

tiple inflections can be composed called nextScope. So the inflections that compose the

example are as follows14

:japanese_vowel_stem_verb a lemon:MorphPattern ;

lemon:transform :causative_transform ,

:passive_transform ,

:negative_transform ,

:past_transform_on_negative .

:causative_transform lemon:rule "~ru/~saseru" ;

lemon:nextScope :passive_transform, :negative_transform ;

lemon:generates [

isocat:voice isocat:causativeVoice

] .

:passive_transform lemon:rule "~ru/~rareru" ;

lemon:nextScope :negative_tranform ;

lemon:generates [

isocat:voice isocat:passiveVoice

] .

14For readability we will continue to use Hepburn romanization, examples should in fact be represented in native script.

41

:negative_transform lemon:rule "~ru/~nai" ;

lemon:nextScope :past_transform_on_negative ;

lemon:generates [

isocat:negative isocat:yes

] .

:past_transform_on_negative lemon:onStem [

isocat:negative isocat:yes ;

isocat:tense isocat:present

] ;

lemon:rule "~i/~katta" ;

lemon:generates [

isocat:negative isocat:yes ;

isocat:tense isocat:past

] .

Example 71

2.5.2 Agglutination

Many languages uses agglutination where the form of a word is dependent on neighbor-

ing words and we would like to be able to represent this as well. We use agglutination

as a word that covers many distinct phenomena such as assimilation, liasion, sindha

and vowel harmony. For example, in Maltese the definite article “il-” is a proclitic that assimilates with the first phoneme of the following word if it is a coronal constonant or vowel. For example

il- + missier

il-missier

the father

il- + omm

l-omm

the mother

il- + tifel

it-tifel

the boy

Example 72

42

The rules for doing this are similar to inflection rules but include the symbol “+” to indicate the word with which the agglutination occurs. So we describe maltese agglutination as follows

:maltese_il_assimilation a lemon:MorphPattern ;

lemon:transform [

lemon:rule "~l+([ċdnrstxżz])/~$2-$2" ;

lemon:rule "~l+([aeiou])/l-$2"

] .

Example 73

Note that this pattern only applies to one word, it is written in a way that could be

used elsewhere if the pattern occurs differently. Similarly $2 is used as the matcher (as $1 corresponds to ˜).

Hungarian (as well as all Uralic and many Altaic langauges) exhibit an interesting

agglutinative property known as vowel harmony, in which the vowels in a suffix must

agree with the vowels in the preceding word. Hungarian groups the vowels into three

categories back vowels (a, o, u), front vowels (ö, ü) and intermediate vowels (e, i).

Prepositions and cases in Hungarian are prefices which must agree in terms of their

vowel for example

lakás + hoz

lakáshoz

to the house

szem + hoz

szemhez

to the eye

kör + hoz

körhöz

to the circle

Example 74

This can be modeled using different stem forms as in example 67,so we would model

this as follows15

:hungarian_vowel_harmony a lemon:MorphPattern ;

lemon:transform [

lemon:onStem [

dcr:vowelHarmony dcr:back

]

lemon:rule "^([^öőüűeéií]*[aáoóuú].*)+~/$1+~" ;

] , [

lemon:onStem [

dcr:vowelHarmony dcr:intermediate

]

lemon:rule "^([^aáoóuúeéií]*[öőüű].*)+~/$1+~" ;

] , [

lemon:onStem [

dcr:vowelHarmony dcr:front

]

lemon:rule "^([^aáoóuúöőüű]*[eéií].*)+~/$1+~" ;

] .

Example 75

Here the regular expression matches the hole of the agglutination, indicated by the

îndicating the start of string.

15Vowel harmony is currently not a property expressed in ISOcat so we will use the prefix dcr here. We also include the Hungarian long vowels (á, é, í, ó, ú, ő, ű) in this example.

43

Summary of vocabulary introduced in this section

Property

Description

pattern

Indicates the morphological pattern used by a

lexical entry

transform

Indicates a single possible transform used by

a morphological pattern

rule

The rule used by a particular transform

generates

The prototype for a form generated by a trans-

form

onStem

The prototype for a from used as input for a

transform

nextTransform

Indicates a potential sequence in a compound

transform

44

3 Advanced Issues

3.1 Annotations and Global Restrictions

lemon does not implement a model for storing other meta-data and applying global

restrictions on the lexicon, instead reusing the methodologies from RDF and OWL to

achieve this.

3.1.1 Annotation schemes

One of the most important issues in terminology management is the ability to express

the ownership and creation date of a lexicon and its entries. lemon has no mechanism of

its own for performing this task instead, like many other RDF models it uses the Dublin

Core vocabulary for this. The Dublin Core vocabulary allows the following to be stated:

• Creator: The creator of the given resource

• Description: A description of the resource. lemon:definition should be used for defining senses of lexical entries, dublincore:description for describing other parts

of the model e.g., the lexicon object.

• Publisher: The publisher of the data

• Contributor: Any contributors to the given lexicon

• Date: The date a given resource was created

• Format: Generally should be specified as MIME. Use with care, as the format of the ontology may change.

• Identifier: An identifier of the lexicon or entry (useful if converting from another format)

• Source: The source of a given resource

• Coverage: The time and location covered by the resource (if relevant)

• Rights: Any copyright information

In addition, there are several annotations available in the OWL and RDF schemas

that are useful for lexicon management

• owl:backwardCompatibleWith: The lexicon is compatible with some previous lexicon

• rdfs:comment: A comment about a resource

• owl:deprecated: Indicates the resource should not be used any more

• owl:incompatibleWith: The lexicon is incompatible with some previous lexicon

• rdfs:isDefinedBy: The resource is fully defined else where

• owl:priorVersion: Indicates an earlier version of the lexicon

• rdfs:seeAlso: Refer to another web resource

• owl:versionInfo: The version number of the lexicon

This information can of course simply be added as triples

45

@prefix dublincore: <http://purl.org/dc/elements/1.1/>

@prefix rdfs: <http://www.w3.org/2001/02/rdf-schema#>

@prefix owl: <http://www.w3.org/2002/07/owl#>

:myLexicon a lemon:Lexicon ;

dublincore:creator "John McCrae" ;

dublincore:date "2010-07-24"^^xsd:date ;

rdfs:comment "An example lexicon from the lemon cookbook"@en ;

lemon:language "en" ;

lemon:entry :cat .

:cat a lemon:Word ;

dublincore:creator "John McCrae" ;

dublincore:date "2010-07-25"^^xsd:date ;

rdfs:seeAlso "http://en.wikipedia.org/wiki/Cat" ;

lemon:canonicalForm [ lemon:writtenRep "cat"@en ] .

.

Example 76

The use of these annotations is another reason why the ranges of many properties

are resources instead of literals as expected. For example we could now easily state

the source of a definition as follows.

:cat lemon:sense [ lemon:reference ontology:cat ;

lemon:definition [

lemon:value "The cat is a small domesticated carnivorous animal"@en ;

dublincore:source "http://en.wikipedia.org/wiki/Cat" ]

] .

Example 77

3.1.2 Global Information

LMF provides several classes for describing global information about the lexicon and

constraints. lemon, however, does not need to do this as it can use the OWL language

to specify decidable constraints on the lexicon. OWL will not be fully described here

however there are many excellent resources available. A good place to start may be

the W3C documents available at http://www.w3.org/standards/techs/owl#w3c_all. Here we shall show an example of how OWL can be used to specify that French nouns are

only masculine or feminine.

:FrenchLexicon a owl:Class ;

owl:equivalentTo [ owl:intersectionOf (

lemon:Lexicon

[ a owl:Restriction ;

owl:onProperty lemon:language ;

owl:hasValue “fr” ]

) ] ;

rdfs:subClassOf [ a owl:Restriction ;

owl:onProperty lemon:entry ;

owl:allValuesForm :FrenchWord ] .

:Noun a owl:Class ;

owl:equivalentTo [ a owl:Restriction ;

owl:onProperty isocat:partOfSpeech ;

owl:hasValue isocat:noun ] .

:FrenchWord a owl:Class .

46

:FrenchNoun a owl:Class ;

owl:equivalentTo [ owl:intersectionOf (

:FrenchWord :Noun ) ] ;

rdfs:subClassOf [ a owl:Restriction ;

owl:onProperty isocat:grammaticalGender ;

owl:allValuesFrom [ owl:oneOf (

isocat:masculine isocat:feminine ) ] ] .

The OWL restrictions are as follows in DL syntax:

F renchLexicon≡ Lexicon⊓∋ language. ” f r”

F renchLexicon⊑∀ entry.F renchW ord

N oun≡∋ partOf Speech.noun

F renchN oun≡ F renchW ord⊓ N oun

F renchN oun⊑∀ grammaticalGender.{masculine, f eminine}

We first state that FrenchLexicons are those lexica that have the value “fr” for the

language property, and that all entries of FrenchLexicons are FrenchWords. We then define the class Noun as all things that have the value noun for the partOfSpeech property and

then that FrenchNouns are FrenchWords that are also Nouns. Finally the key restriction

is that every FrenchNoun has its grammaticalGender from the set { masculine, feminine }.

(Note we do not assert here that the gender cannot be both masculine and feminine,

as some words may be both, such as “après-midi”, however it is of course possible to

do so).

Property

Description

topic

The topic of a lexicon or lexical entry

definition

The definition of a lexical entry relative to a

sense.

47

3.2 The semantics of the lemon model

One of the most important aspects of a lemon model is the connection between the

semantics contained within the lexicon, and the semantics in the ontology. Before

we can hope to handle this difficult task we divide the tasks that are intended to be

performed into three main groups

• Lexical: These are tasks that belong purely to the lexical layer of lemon and can be processed without strong semantic knowledge

– Tokenization: Identifying words and components

– Chunking: Identifying multi-word elements

– POS-Tagging: Identifying labels for each token/chunk.

– Lemmatization: Decomposing terms into inflectional components.

– Parsing: Building a parse tree over a sentence.

– Coreference: Deducing which elements in a sentence share a reference. (N.B.,

this task can often be considered to include weak semantic knowledge as

well).

– Inflection: Inverse of lemmatization

– Grammar Generation: Inverse of parsing.

• Correspondence: Involves tasks that primarily focus on the task of connecting lexical realizations to conceptual objects.

– Annotation/Word Sense Disambiguation: Converting the lexical representa-

tion to a semantic (ontological) relation

– Selection: Inverse of grounding

• Reasoning: Making higher level deductions based on an ontological representation without lexical information.

3.2.1 Formal model of lemon senses

The primary model that we use to describe the semantics of lemon is inspired by this

and views these tasks is that of an aligned semantic interpretation. Assume we have

a language, L, on a vocabulary, Σ, i.e., L ⊆ Σ ∗. Say we have a lexicon, X , and we can define a language, X ⊆ X ∗, that constitutes all description of sentences in terms of the lexical entries it uses and the dependencies between the entries. Furthermore, we

assume we have a lexicalization function l that maps a sentence in the language to a list of lexical representations taken from a lexicon, X , that is that l is a function with the signature:

l : L → X

We call such an l a lexical interpretation and represents the result of the lexical parsing stages applied above, we simplify the definition here by assuming that this

results in a single unambiguous representation relative to the lexicon.

Similarly assume we have an ontology O, which can be used with a logic, L, such that we have a language O ⊆ ( O ∪ L) ∗. Again, we define a function s that maps a sentence in the language to its semantic representations, this function is called the semantic interpretation and has the signature:

s : L → P( O)

This function is indicated to input the set of correct results from all the lexical, cor-

respondence and reasoning based analysis processes.

For example we take a simple sentence “The man bites the dog,” we assume that the

lexicon contains the entities { man , dog , bite( ·, ·) } and the ontology contains two classes 48

M an and Dog and a property bite. Now, for example, we could have the folllowing interpretations:

l(“The man bites the dog”) = bite(man , dog)

s(“The man bites the dog”) = ∃x, y : M an( x) ∧ Dog( y) ∧ bite( x, y) We may then define an aligment, A ⊆ ( X × O) ∗ such that if we have µ ∈ X, then for all σ ∈ L such that l( σ) = µ, we have aµ,p ∈ A for each p ∈ s( σ). Furthermore we require that if µ = λ 1 . . . λn, p = o 1 . . . om then we have aµ,p = α 1 . . . αn such that αi = ( λi, oj) for some j : 1 ≤ j ≤ m.

We then define the set of senses in the lexicon, S ⊆ ( X × O), as the set satisfying ( λ, o) ∈ S ⇔ ∃α 1 . . . αn ∈ A, i such that 1 ≤ i ≤ n ∧ αi = ( λ, o) As such our example may be explained by the following:

aµ,p = (bite( ·, ·) , bite)(man , M an)(dog , Dog)

{(man , Man) , (dog , Dog) , (bite( ·, ·) , bite) } ⊆ S

This can be illustrated as follows:

We view the sense as having three aspects:

• From the lexical point of view s indicates a disambiguated usage of a term, we call this a Disambiguated Lexical Entry or DALE. We can formally define the set of DALEs, Dλ for a lexical entry λ as follows:

Dλ = {dλ,o|( λ, o) ∈ S}

• From the semantic view, s represents a sub-entity representing only the usage of a given class or property or individual when the given lexical entry is used to

describe it. We call this an projected ontology sub-entity or POSE. We can formally define this as an ontology entity πs such that:

πs v o

πs ≡ ⊥ ⇔ o ≡ ⊥

• Finally we can view S as a pair representing a possible alignment between the lexicographic and the semantic representations. We call this a correspondence.

We define the lemon senseRelations, equivalent, narrower, broader and incompatible

and their relationship to the ontology as follows:

s 1 = s 2 ⇔ πs ≡ π

(1)

1

s 2

s 1 < s 2 ⇔ πs < π

(2)

1

s 2

s 1 > s 2 ⇔ πs = π

(3)

1

s 2

s 1 ⊗ s 2 ⇔ πs u π

= ⊥

(4)

1

s 2

Theorem

If we have o 1 , o 2 ∈ O and some corresponding senses s 1 , s 2 ∈ S then the following hold:

1. o 1 u o 2 ≡ ⊥ ` s 1 ⊗ s 2

49

2. s 1 = s 2 ò 1 u o 2 6≡ ⊥ ∨ o 2 ≡ o 1 ≡ ⊥

3. s 1 > s 2 ò 1 u o 2 6≡ ⊥ ∨ o 2 ≡ ⊥

Proof

1. We have πs v o

v o

u π v o

u π ≡ ⊥

1

1 and πs 2

2 hence πs 1

s 2

1 u o 2 hence we have πs 1

s 2

hence s 1 ⊗ s 2

2. Assume o 2 6≡ ⊥ hence it follows that πs 6≡ ⊥, but we have π ≡ π

hence o

2

s 2

s 1

1 u o 2 w

πs u π

≡ π 6≡ ⊥. Similarly for o

1

s 2

s 2

1 6≡ ⊥.

3. (As 2.)

As a motivating example for making this distinction consider the example of “stu-

dent” and “person”, these are asserted to be hypernyms in WordNet however in a

strict ontology sense are not in a subsumption (subclass) relation. In fact “student” is a role of a “person” and would not be modeled the same in a well-reasoned ontology (i.e.,

DOLCE, which would distinguish “student” as anti-rigid and “person” as rigid). However

for certain tasks, such as coreference resolution, it is necessary to have these “lexical”

semantics, and as such we include them within the lexicon. As such it is possible to

state the “student“ is a narrower sense of “person”, without affecting the correctness

or the ontology.

In the context of coreference this modelling can be used for word sense disambigua-

tion and coreference as it contains lexical semantics that are weaker than those in the

ontology. In particular, there are four relations defined in lemon that are used to aid

these tasks: equivalent, disjoint, broader and narrower. Each of these can be under-

stood in the following manner.

• equivalent: Two DALEs which have equivalent senses can be substituted for each other without changing the semantics of the text. This should be assumed to be

a positive feature for coreference.

• incompatible: Two DALEs which have incompatible (disjoint) senses cannot be

substituted for each other without changing the semantics of the text. This should

be considered to be a negative feature for coreference.

• broader/narrower: If a DALE is asserted to be broader than another DALE, then it can be substituted to give a more general semantics. This should be considered

a positive feature for coreference, if the preceding term is narrower.

50

4 Relation to existing systems

4.1 LMF

LMF (Francopoulo et al. , 2006) is a framework for describing lexicons and it has a strong influence on lemon. LMF was primarily intended for XML representation, and it has some

constraints and factors we do not have in our RDF-based model

• No named links: Arcs in LMF are not named, in contrast to lemon where they must be named.

• Composition/Association: LMF models relationships between elements as either

being compositional or associational, this is not considered important for our RDF

framework

• No ontology: LMF does not have an existing ontological framework to build on like OWL.

• Little external linkage: In contrast to lemon, LMF does not refer to other frameworks, or link to other descriptive frameworks (with one exception see below).

As such, although we declare lemon to be highly compliant with LMF, we need now

to describe the steps in converting a lemon model to a LMF lexicon.

lemon drops many elements

lemon has significantly fewer elements than LMF. This is partly due to the use of RDF

which generally allows for a much more compact representation of complex interlinked

data. We have also chosen to remove all nodes from LMF that are purely structural and

carry no information such as SyntacticBehaviour and PredicativeRepresentation (it also

appears that these elements may be artifacts of the XML representation). Due to the

RDF representation it is not necessary to have nodes that describe links between ele-

ments. This means that elements such as RelatedForm, SenseRelation and MWEEdge

are replaced with properties in lemon (formVariant, senseRelation and edge respec-

tively). Finally, we found that by using RDF the mapping from syntax to semantics could

be represented much more compactly so we have only senses and arguments covering

LMF’s SemanticPredicate, PredicativeRepresentation, SemanticArgument, SyntacticAr-

gument, SynSemArgMap and SynSemCorrespondence.

lemon modifies some element names

Many lemon class names are different from their LMF equivalents. This is primarily

to keep the names short and to avoid close naming between the classes and their

associated properties. lemon also uses US English spelling, so the syntactic behavior

has been respelled appropriately.

lemon

LMF

Frame

SubcategorizationFrame

LexicalSense

Sense

Node

MWENode

OntologyReference

MonolingualExternalRef

synBehavior

SyntacticBehaviour

Senses and semantics

One of the primary differences between LMF and lemon is the handling of semantics.

In LMF, similarly to other resources such as WordNet, there are a fixed set of senses,

however in lemon the sense ties the lexical entry to an ontology. There are several

reasons for this: It is difficult to be certain of a fixed set of senses that fully capture all meanings of a word. Words are frequently used with meanings beyond the dictionary

set of senses (e.g., metonymic raising). Different languages frequently divide senses

51

in ways that are not natural in the other language (e.g., “river” in English and “rivière” and “fleuve” in French). Senses have no inherent meaning, i.e., practical systems

can only understand the meaning of a word by grounding it in some ontologically de-

scribed formalism. Ontology languages provide better modeling of semantics than a

lexicon-based system can. Note that although lemon is intended to and likely to be used

primarily with OWL, it is not necessary that the ontology system is OWL.

For these reasons the semantic modeling in lemon is more lightweight than that of

LMF and so we have far fewer classes, as we do not wish to significantly duplicate the

semantic modeling that should exist in the ontology.

Differences in the linguistic description

Most elements of the LMF vocabulary are defined by data values attached to the given

instances, for example LMF says that a lexical entry may have properties such as partOf-

Speech and grammaticalGender. In lemon such annotations are obtained by use of

linguistic description ontologies, such as ISOcat. LMF’s models ideally use ISOcat to

provide its data categories so alignment in this case should be a trivial matter.

What lemon has that LMF doesn’t

• Description of syntactic frames (subcategorization) by use of phrase structure

• Mapping to ontology predicates

• Pragmatic context

• Sense conditions and contexts

What lemon omits from LMF

• Morphology: lemon does not provide process descriptions of inflection generation.

• Lexeme properties in subcategorization frames: this is replaced by MWE/Frame

links

• Synsets: lemon handles this through the use of ontology reference, i.e., the synset is the set of lexical senses that share a reference.

• Tests of cross-lingual mappings: replaced by sense conditions

• Global lexicon constraints: replaced by OWL restrictions

4.2 SKOS

lemon is designed to subsume most of the features of SKOS (Miles and Bechhofer, 2009), in particular the ability to state preferred labels and represent soft semantic relations.

lemon uses the sub-properties of lexicalForm and isReferenceOf to more precisely cap-

ture the same semantics as SKOS’s prefLabel, altLabel and hiddenLabel. The conver-

sion is as follows:

Canonical Form

Other Form

Abstract Form

Preferred Reference of

prefLabel

altLabel

hiddenLabel

Alternative Reference of

altLabel

altLabel

hiddenLabel

Hidden Reference of

hiddenLabel

hiddenLabel

hiddenLabel

As such it should be easy to convert a lemon model into a SKOS model, but converting

the other way is harder as it involves deducing if the reason for an alternative or hidden label is pragmatic or morphosyntactic.

lemon also allows for SKOS’s semantic properties broader, narrower and related to

be mapped in a one-to-one manner to lemon’s broader, narrower and senseRelation.

Although Like the lemon properties are – like their SKOS counterparts – not transi-

tive, lemon’s concept of senses being related (i.e. senseRelation) is a super property

of broader and narrower.

52

4.3 TBX

TBX (Term Base eXchange, ISO 30042) is a standard that is used for exchanging termi-

nology databases and in particular translation memories. Therefore, a certain degree

of interoperability with lemon would be useful. As with LMF, TBX contains much header

information and this can mostly be either discarded or mapped onto the descriptions

given in 4.7.2. The body of a TBX document consists primarily of a set of termEntrys

each of which is a concept, these roughly correspond to sense/reference pairs. The

conversion is trivial if there is a known ontology referenced by the term base (ideally

indicated by a ref tag), however if this is not the case either the id attribute of termEntry can be used as a URI or some invented URI should be used. Doing this effectively

raises the concepts expressed in the TBX document into a pseudo-ontology, so they

can be used with lemon. In TBX each termEntry is first split into languages and then

into terms, as such each langSet should be mapped to a lexicon in lemon. Finally, the

terms should be mapped to lexical entries with a given form. As TBX does not have

a simple method for indicating the standard form, either the forms should be guessed

from properties such as lemma, or all just marked with the general form.

53

Conclusion

lemon is a model that allows for lexica to be shared on the semantic web and provides

an efficient and simple representation. Nevertheless, the representation is capable of

presenting a very wide range of lexical information through both its own properties

and the easy integration of data categories from other sources. By incorporating other

semantic web standards lemon can gain many basic features for expressing meta-data

about the lexica and for describing constraints on the nature of the lexicon. lemon’s

innovative use of sense allows for lexical semantics to be represented in harmony with

more powerful semantic representation provided by ontology languages such as OWL.

Finally lemon maintains a high degree of interoperability with other standards, most

importantly SKOS and LMF.

Thanks

We would like to thank the following people for their contribution to the model and

discussion leading to its creation: Axel Pollers (DERI), Antoine Zimmermann (DERI),

Dimitra Anastasiou (CNGL), Susan Marie Thomas (SAP), Christina Unger (CITEC), Sue

Ellen Wright (Kent State University), Menzo Windhouwer (Universiteit van Amsterdam)

A FAQ

Q What is a form and what is a lexical entry? For example should initialisms be considered different forms?

A In general a form is considered to be “orthography-invariant”, that is that the form should be a single entity across different orthographies or media. In contrast a lexical entry is “syntax-invariant”, this means that it should always become the same

entity once syntactic processing is complete. Initialism are clearly not “syntax-

invariant” (as they consist of a different number of words), so should be different

lexical entries.

Q Regarding the relations between lexical entries, if I understood it correctly, what are formVariant and lexicalVariant?

A In general this follows from the question of what is a form and what is a lexical entry?

Q Regarding the relations to be established between Senses, I am not so sure of the advantages in using the broarderSense and narrower relations compared to un-derspecification of these relations? Does introducing such relations not duplicate

information already in an ontology?

A In fact the senses are quite underspecified, and the referenced ontology is intended to contain the precise and rigorous semantics. The use of sense relations is intended primarily for lexical processing using the lexicon.

Q Regarding the morphological information, is all this information needed for my application? Do we really need so much detail?

A No, of course not. In fact the minimal lemon model needs use only forms and lexical entries (and senses and references if referring to an ontology). But many applications may need something more than already exists in the model and lemon aims

to be extensible; we do not require that the entire model is always implemented!

Q I do not understand the sentence “Although lemon also includes features to assign words to a pragmatic context, as this requires defining a pragmatic taxonomy it

may not be a wise idea for many applications”. What do you mean by a pragmatic

taxonomy? I personally believe that the pragmatic sense of the word is already

given by the word, but that we may want to make it explicit for practical reasons,

54

in order to be able to ask the ontology to give us the most appropriate vocabulary for a certain context.

A In fact pragmatic context is really about the mapping, it is not even strictly necessary if there is no mapping. There are also examples where the meaning of the word

is defined by its pragmatic context, for example “bitch” refers to a “female dog”

in veterinary context, but has a vulgar meaning in another context.

Q Regarding the ”three-faceted understanding of sense”, is this not too subtle to be incorporated into the model?

A For many applications it may be, but for some applications a strong definition of how the mappings correspond to implementations is useful. Much like any format

different levels of understanding can be used, i.e., RDF manages to be used quite

effectively in contexts where its extensional model semantics are not relevant.

Q Components are used to describe both word tokenization and term decomposition.

How should I represent “deutsches Schweineschnitzel”

A This should generally be represented by decomposing it first into “deutsch(es)” and

“Schweineschnitzel” and marking both of these as lemon:Words, then applying a

decomposition of “Schweineschnitzel” to “Schwein(e)” and “Schnitzel”.

Q Can a property of a lexical entry have multiple values? E.g., can I assign a lexical entry to multiple subject fields?

A Sure, RDF also you to assert multiple triples with the same subject and property Q Could I have a hierachical value for the property of a lexical entry?. E.g., if I say my lexical entry is a proper noun, could I deduce it is also a noun?

A Limitations of RDF/OWL make this very difficult to do in general. There are three solutions

1. Handle it yourself. Especially if you are referring to a lexicon standard in a

non-RDF format (e.g., ISOcat’s DCIF) then this may be the best approach

2. Use punning. OWL2 allows classes to be punned to individuals, this is suit-

able for some modelling (e.g., GOLD), but will not deduce the desired triples

automatically

3. Add OWL axioms to the description ontology. For example the above case

could be handled with

∃ partOf Speech.properN oun⊑∃ partOf Speech.noun

Q You use blank nodes frequently through out this document is this required in lemon?

A No. It just keeps the examples more readable

Q How do I represent how superlatives (e.g., “biggest”) map to an ontology

A Superlatives are difficult to represent well, as they clash with the open world semantics inherent to OWL and RDF. In particular it is difficult to say what the greatest

element of a non-closed set would in fact be. You could of course map it to an

object property, whose range is some finite set (e.g., RDF’s List or an OWL enu-

meration), and then it could be mapped like a comparative.

Q As in multi-word expressions elements are inflected shouldn’t decomposition be by forms not lexical entries?

A We have chosen to place it at the lexical entry level and use properties for several reasons, but in particular that the decomposition should be the same for all

forms of the lexical entry. For example in the case of “number of employees” it is

clear that both that canonical form and the plural form “numbers of employees”

should have the same decomposition, even though they both use the inflected

55

form “employees.” Doing otherwise would require that the decomposition be re-peated (potentially many times). Also it should be noted that this decomposition

is semantically disambiguated with “number” strictly in the sense of an amount

and not as a word or symbol. For general compactness and the ability to inter-

face with the phrase structure model we limit decomposition to the lexical entry

level and then assume that this decomposition can also be soundly applied at the

lexical and semantic level.

B LMF comparison

LMF

lemon

Notes

LexicalResource

Root element of RDF/XML document is

rdf:RDF

GlobalInformation

rdf:RDF

Lexicon

Lexicon

LexicalEntry

LexicalEntry

Form

LexicalForm

Representation

representation

FormRepresentation

TextRepresentation

writtenRep

Definition

definition

Statement

no equivalent

Use rdfs:comment on the definition

Lemma

canonicalForm

WordForm

otherForm

See discussion in section 1

Stem

∼ abstractForm

RelatedForm

formRelation

ListOfComponents

ComponentList

Component

Component

Equivalent

equivalent

Context

context

Subject Field

topic

Syntactic Behaviour

synBehavior

SubcategorizationFrame

Frame

LexemeProperty

no equivalent

Generally should be modelled at the lexical

entry level

Subcategorization-

Instead use the same argument entity with

not necessary

FrameSet

multiple frames to model sets of frames

SynArgMap

SyntacticArgument

synArg

By using the same entity as both syntactic

SemanticArgument

semArg

and semantic argument the encoding in

SynSemArgMap

not necessary

lemon is more economical; see section ??

SynSemCorrespondence

MonolingualExternalRef

reference

Plays a slightly different and significantly

more important role in lemon

Sense

SenseAxis

As predicates are assumed to exist the

TransferAxis

ontology, they are not modelled in the

LexicalSense

PredicativeRepresentation

lexicon. Synsets given implicitly as the set of

SemanticPredicate

senses that have the same reference

Synset

SenseRelation

PredicateRelation

SynsetRelation

senseRelation

SenseAxisRelation

TransferAxisRelation

SourceTest

Condition on the sense instead of on the

condition

TargetTest

transfer axis, as senses are more precise

InterlingualExternalRef

no equivalent

Can be modelled with dublincore:source

ArgumentRelation

no equivalent

It is not clear what argument relations exist

ContextAxis

Should be modelled with an ontology of

no equivalent

ContextAxisRelation

contexts, not in the lexicon

morphological pattern ex-

Replace by lemon morphology extensions,

tensions

section 2.5.

MWEPattern

phraseRoot

MWENode

Node

MWEEdge

edge

MWELex

leaf

constraint expression ex-

no equivalent

Use OWL

tensions

C lemon model diagram

References

P. Cimiano, P. Buitelaar, J. McCrae, and M. Sintek. LexInfo: A Declarative Model for the

Lexicon-Ontology Interface. Web Semantics: Science, Services and Agents on the

World Wide Web, 9(1):29 – 51, 2011.

G. Francopoulo, M. George, N. Calzolari, M. Monachini, N. Bel, M. Pet, and C. Soria.

Lexical markup framework (LMF). In Proceedings of the Fifth International Conference

on Language Resource and Evaluation (LREC’O6), 2006.

A. Miles and S. Bechhofer. SKOS Simple Knowledge Organization System Reference,

2009. URL http://www.w3.org/TR/skos-reference/. Accessed 19 October 2010.

E. Montiel-Ponsoda, G. Aguado de Cea, A. Gómez-Pérez, and W. Peters. Modelling multi-

linguality in ontologies. In Proceedings of the 21st International Conference on Com-

putational Linguistics (COLING), 2008.

L. Romary. Standardization of the formal representation of lexical information for NLP.

In Dictionaries: An International Encyclopedia of Lexicography. Mouton de Gruyter, 2010.

Document Outline

- The Trebuchet MSlemon core

- Main elements

- Canonical forms and preferred lexicalizations

- Modules

- Linguistic Description Module

- Linguistic properties

- Describing phonetics

- Topics and contexts

- Variation Module

- Lexicosemantic relationships

- Lexical variants

- Subphrases as variation

- Form variants

- Translation as variation

- Phrase Structure Module

- Decomposition of terms

- Phrase structures

- Dependency relations

- Noun phrase chunks

- Syntax and Mapping Module

- Frames

- Phrase structure and frames

- Predicate mapping

- Conditions

- Mapping to more complex representations

- Mapping adjectives

- Correspondence

- Morphology Module

- Inflection

- Agglutination

- Advanced Issues

- Annotations and Global Restrictions

- Annotation schemes

- Global Information

- The semantics of the Trebuchet MSlemon model

- Formal model of Trebuchet MSlemon senses

- Relation to existing systems

- LMF

- SKOS

- TBX

- FAQ

- LMF comparison

- Trebuchet MSlemon model diagram

Table of Contents

The Trebuchet MSlemon core

Main elements

Canonical forms and preferred lexicalizations

Main elements

Canonical forms and preferred lexicalizations

Modules

Linguistic Description Module

Linguistic properties

Describing phonetics

Topics and contexts

Linguistic properties

Describing phonetics

Topics and contexts

Variation Module

Lexicosemantic relationships

Lexical variants

Subphrases as variation

Form variants

Translation as variation

Lexicosemantic relationships

Lexical variants

Subphrases as variation

Form variants

Translation as variation

Phrase Structure Module

Decomposition of terms

Phrase structures

Dependency relations

Noun phrase chunks

Decomposition of terms

Phrase structures

Dependency relations

Noun phrase chunks

Syntax and Mapping Module

Frames

Phrase structure and frames

Predicate mapping

Conditions

Mapping to more complex representations

Mapping adjectives

Correspondence

Frames

Phrase structure and frames

Predicate mapping

Conditions

Mapping to more complex representations

Mapping adjectives

Correspondence

Morphology Module

Inflection

Agglutination

Inflection

Agglutination

Linguistic Description Module

Linguistic properties

Describing phonetics

Topics and contexts

Linguistic properties

Describing phonetics

Topics and contexts

Variation Module

Lexicosemantic relationships

Lexical variants

Subphrases as variation

Form variants

Translation as variation

Lexicosemantic relationships

Lexical variants

Subphrases as variation

Form variants

Translation as variation

Phrase Structure Module

Decomposition of terms

Phrase structures

Dependency relations

Noun phrase chunks

Decomposition of terms

Phrase structures

Dependency relations

Noun phrase chunks

Syntax and Mapping Module

Frames

Phrase structure and frames

Predicate mapping

Conditions

Mapping to more complex representations

Mapping adjectives

Correspondence

Frames

Phrase structure and frames

Predicate mapping

Conditions

Mapping to more complex representations

Mapping adjectives

Correspondence

Morphology Module

Inflection

Agglutination

Inflection

Agglutination

Advanced Issues

Annotations and Global Restrictions

Annotation schemes

Global Information

Annotation schemes

Global Information

The semantics of the Trebuchet MSlemon model

Formal model of Trebuchet MSlemon senses

Formal model of Trebuchet MSlemon senses

Annotations and Global Restrictions

Annotation schemes

Global Information

Annotation schemes

Global Information

The semantics of the Trebuchet MSlemon model

Formal model of Trebuchet MSlemon senses

Formal model of Trebuchet MSlemon senses

Relation to existing systems

LMF

SKOS

TBX

LMF

SKOS

TBX

FAQ

LMF comparison

Trebuchet MSlemon model diagram