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This document presents a standard conversion of Princeton WordNet to RDF/OWL. It describes how it was converted and gives examples of how it may be queried for use in Semantic Web applications.
This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.
This document is a First Public Working Draft produced by the Semantic Web Best Practices and Deployment Working Group, part of the W3C Semantic Web Activity.
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At the time of publication the charter of the Semantic Web Best Practices and Deployment Working Group is expiring and no chartered group has been proposed to continue further work on this document. The Working Group does recognize that feedback on this document may lead to suggestions for further work. The current Working Group is not placing this work on the W3C Recommendation Track.
The URIs specified in this document for WordNet terms are served by W3C in accordance with its resource persistence policy. Refer to Appendix I Open Issues for expectations regarding Princeton URIs for these resources.
This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. W3C maintains a public list of any patent disclosures made in connection with the deliverables of the group; that page also includes instructions for disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information in accordance with section 6 of the W3C Patent Policy.
Publication as a Working Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.
The following people have reviewed this document and provided valuable advice: Jeremy Carroll, Brian McBride, John McClure, Benjamin Nguyen and Jacco van Ossenbruggen. The following people have provided valuable advice through the best practices mailing list and personal correspondence with the editors: David Booth, Dan Connolly Jeremy Carroll Kjetil Kjernsmo, Michel Klein, Peter Mika, Alistair Miles, Steve Pepper, Jacco van Ossenbruggen and Ralph Swick. Dan Brickley and Brian McBride have contributed to the WordNet conversion described in this note through their work in the WordNet Task Force and additional comments and suggestions. Special thanks to Ralph Swick for help in generating CBDs and setting up this conversion in W3C webspace. We also thank the MultimediaN e-Culture team, in particular Jan Wielemaker, for important usage comments.
WordNet [Fellbaum, 1998] is a heavily-used lexical resource in natural-language processing and information retrieval. More recently, it has also been adopted in Semantic Web research community. It is used mainly for annotation and retrieval in different domains such as cultural heritage [Hollink et al., 2003], product catalogs [Guarino et al., 1999] and photo metadata [Brickley, 2002]. It is also used to ground other vocabularies such as the FOAF schema [Brickley and Miller, 2005], as background knowledge in ontology alignment tools and other applications (see http://esw.w3.org/mt/esw/archives/cat_applications_and_demos.html for a list). Currently there exist several conversions of WordNet to RDF(S) or OWL.
The WordNet Task Force of the SWBPD WG aims at providing a standard conversion of WordNet for direct use by Semantic Web application developers. Some of the earlier conversions are incomplete and are incompatible with each other, for example because they provide different URIs for the same entity in the original source. By providing a standard conversion that is as complete as possible the TF aims to improve interoperability of SW applications that use WordNet and simplify the choice between the existing RDF/OWL versions. We have based this conversion on examining the commonalities of previous conversions, extending them where necessary and making choices to suit different needs of application developers. This conversion may be used directly in Semantic Web applications, or as a source for modified WordNet versions (e.g. turning WordNet into an ontology). We have focused on staying as close to the original source as possible, i.e. reflect the original data model without interpretation. For example, whether or not (parts of) WordNet actually constitute a proper subclass hierarchy is outside the scope. The W3C hosts the conversion of version 2.0 of Princeton WordNet at the following URI:
http://www.w3.org/2006/03/wn/wn20/See Open Issues for more information on future hosting.
This document is composed of three parts. The first part (Section two) has three subsections. The first subsection provides a Primer to the usage of the WordNet RDF/OWL representation and is intended as a convenient starting point for users and developers that have already worked with Princeton WordNet and have basic knowledge of RDF(S) and OWL, or those who have already worked with another RDF/OWL representation of WordNet. The second subsection provides an Introduction to the WordNet RDF/OWL schema. Those who are not familiar with WordNet should read the third subsection: Introduction to the WordNet datamodel, before reading the Primer. The second part of this document consists of Sections three and four which give more background information for those who are not familiar with WordNet and describe advanced options. It also provides more background to the decisions taken during conversion. The third part (the Appendices) contains detailed information on the RDF/OWL representation, versioning strategy and open issues.
This document is intended to reflect the consensus of the community using WordNet on the Semantic Web and the opinion of the TF on how best to represent the Princeton WordNet datamodel in RDF/OWL.
This document uses the following namespace abbreviations in URIs:
The WordNet schema has three main classes: Synset, WordSense and Word. The first two classes have subclasses for the lexical groups present in WordNet, e.g. NounSynset and VerbWordSense (see Figure one). Each instance of Synset, WordSense and Word has its own URI. There is a pattern for the URIs so that (a) it is easy to determine from the URI the class to which the instance belongs; and (b) the URI provides some information on the meaning of the entity it represents. For example, the following URI
http://www.w3.org/2006/03/wn/wn20/instances/synset-bank-noun-2
is a NounSynset. This NounSynset contains a WordSense which is the first sense of the word "bank". The pattern for instances of Synset is: wn20instances: + synset- + %lexform%- + %type%- + %sensenr%. The %lexform% is the lexical form of the first WordSense of the Synset (the first WordSense in the Princeton source as signified by its "wordnumber", see Overview of the WordNet Prolog distribution). The %type% is one of noun, verb, adjective, adjective satellite and adverb. The %sensenr% is the number of the WordSense that is contained in the synset. This pattern produces a unique URI because the WordSense uniquely identifies the synset (a WordSense belongs to exactly one Synset). The pattern for URIs of WordSenses is the same, except that "synset" is replaced for "wordsense". Example:
http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1
The pattern for Words is: wn20instances: + word- + %lexform%. The %lexform% is the actual lexical form of the Word. For example:
http://www.w3.org/2006/03/wn/wn20/instances/word-bankLastly, the classes and properties of the schema also have a pattern, namely wn20schema: + %ID%, where the %ID% is the name of the property or class. For example, the URI for the participleOf property is:
http://www.w3.org/2006/03/wn/wn20/schema/participleOf
Synset AdjectiveSynset AdjectiveSatelliteSynset AdverbSynset NounSynset VerbSynset WordSense AdjectiveWordSense AdjectiveSatelliteWordSense AdverbWordSense NounWordSense VerbWordSense Word Collocation
Figure 1. The class hierarchy of the WordNet schema
| Property | Domain | Range | Prolog clause |
|---|---|---|---|
| containsWordSense | Synset | WordSense | s |
| word | WordSense | Word | s |
| lexicalForm | Word | xsd:string | s |
| synsetId | Synset | xsd:string | s |
| tagCount | Synset | xsd:integer | s |
| gloss | Synset | xsd:string | g |
| frame | VerbWordSense | xsd:string | fr |
| hyponymOf | Synset | Synset | hyp |
| entails | Synset | Synset | ent |
| similarTo | Synset | Synset | sim |
| memberMeronymOf | Synset | Synset | mm |
| substanceMeronymOf | Synset | Synset | ms |
| partMeronymOf | Synset | Synset | mp |
| classifiedByTopic | Synset | Synset | cls |
| classifiedByUsage | Synset | Synset | cls |
| classifiedByRegion | Synset | Synset | cls |
| causes | Synset | Synset | cs |
| sameVerbGroupAs | Synset | Synset | vgp |
| attribute | Synset | Synset | at |
| adjectivePertainsTo | Synset | Synset | per |
| adverbPertainsTo | Synset | Synset | per |
| derivationallyRelated | WordSense | WordSense | der |
| antonymOf | WordSense | WordSense | ant |
| seeAlso | WordSense | WordSense | sa |
| participleOf | WordSense | WordSense | ppl |
| classifiedBy | Synset | Synset | cls |
| meronymOf | Synset | Synset | mm,ms,mp |
Figure 2. Overview of properties in the WordNet schema. The "Prolog clause" column indicates the Prolog clause(s) used to generate instances of the properties.
Here follow some typical queries that can be posed on the WordNet RDF/OWL once it is loaded into a triple store such as SWI Prolog's Semantic Web library [SWI Prolog, 2006] or Sesame [Broekstra et al., 2002]. The examples are given in SPARQL query language [SPARQL, 2005]. Which query language is available to a user depends on the chosen triple store.
The following queries for all Synsets that contain a Word with the lexical form "bank":
PREFIX wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>
SELECT ?aSynset
WHERE { ?aSynset wn20schema:containsWordSense ?aWordSense .
?aWordSense wn20schema:word ?aWord .
?aWord wn20schema:lexicalForm "bank"@en-US }
Notice the addition of the language tag using "@en-US". This is necessary in all queries for strings. Queries without the correct language tag do not return results.
The following queries for all antonyms of a specific WordSense ("bank"):
PREFIX wn20instances: <http://www.w3.org/2006/03/wn/wn20/instances/>
PREFIX wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>
SELECT ?aWordSense
WHERE { wn20instances:wordsense-bank-noun-1 wn20schema:antonymOf ?aWordSense }
The following queries for all Synsets that have a hypernym that is similar to some other Synset:
PREFIX wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>
SELECT ?aSynset
WHERE { ?aSynset wn20schema:hyponymOf ?bSynset .
?bSynset wn20schema:similarTo ?cSynset}
Although WordNet is not a strict class hierarchy, it is possible to interpret it as such for certain types of applications. This is also possible with this version, see Advanced options.
The above section describes the Full version of RDF/OWL WordNet. There is also a Basic version for users who only require the Synsets for their application and wish to reduce the size of their triple store. See WordNet Basic and WordNet Full for more information.
The schema of the conversion has three main classes: Synset, Word and WordSense. Synset and WordSense have subclasses based on the distinction of lexical groups. For Synset this means subclasses NounSynset, VerbSynset, AdjectiveSynset (in turn subclass AdjectiveSatelliteSynset) and AdverbSynset. For WordSense this means subclasses NounWordSense, VerbWordSense, etcetera. Word has a subclass Collocation used to represent words that have hyphens or underscores in them. Word does not have subclasses such as VerbWord, because a word like "bank" is separate from its function as e.g. a verb or a noun. There is no representation for "all noun word senses with the lexical form 'bank'" and "all verb word senses with the lexical form 'bank'" in the original source, so there is no such class in the class hierarchy.
There are three kinds of properties in the schema. A first set of properties
connects instances of the main classes together. The class Synset
is linked to its WordSenses with the property containsWordSense,
and WordSense to its Word with the property word.
A second set of properties represents the WordNet relations such as hyponymy
and meronymy. There are three kinds of relations: those that relate two
Synsets to each other (e.g. hyponymOf),
those that relate two WordSenses to each other
(e.g. antonymOf) and a miscellaneous set
(gloss and frame).
See Conversion details for an overview of all relations.
A third set of properties gives information on entities (they have XML Schema datatypes
as their range such as xsd:string). Examples are synsetId
that records the original ID given in Princeton WordNet to a synset, and the
tagCount of a wordsense. The actual lexical form of a Word
is recorded with the property lexicalForm.
Each synset has an rdfs:label
that is filled with the lexical form of the first word sense in the synset.
This conversion builds on three previous WordNet conversions, namely by:
The work done at the University of Chile which also resulted in a conversion was done in parallel to the work of this TF. The major differences between this version and the ones listed above are that this version:The core concept in WordNet is the synset. A synset groups words with a synonymous meaning, such as {car, auto, automobile, machine, motorcar}. Another sense of the word "car" is recorded in the synset {car, railcar, railway car, railroad car}. Although both synsets contain the word "car", they are different entities in WordNet because they have a different meaning. More precisely: a synset contains one or more word senses and each word sense belongs to exactly one synset. In turn, each word sense has exactly one word that represents it lexically, and one word can be related to one or more word senses.
There are four disjoint kinds of synset, containing
either nouns, verbs, adjectives or adverbs. There is one
more specific kind of adjective called an adjective satellite.
Furthermore,
WordNet defines seventeen relations, of which ten between
synsets (hyponymy, entailment, similarity, member meronymy,
substance meronymy, part meronymy, classification, cause,
verb grouping, attribute) and five between word senses
(derivational relatedness, antonymy, see also, participle,
pertains to). The remaining relations are "gloss" (between
a synset and a sentence), and "frame" (between a synset and
a verb construction pattern).
There is also a more specific kind of word. Collocations
are indicated by hyphens or underscores (an underscore stands
for a space character), e.g. mix-up and
eye_contact.
There are two ways to query RDF/OWL WordNet. The first option is to download the appropriate WordNet version (see WordNet Basic and WordNet Full) and load it into local processing software such as Sesame [Broekstra et al., 2002] or SWI-Prolog's Semantic Web library [SWI Prolog, 2006]. Query languages such as SPARQL [SPARQL, 2005] and Prolog programs may then be written to query the data.
Example SPARQL query on version 2.0 of RDF/OWL WordNet for all WordSenses which have a Word with the lexical form "bank":
PREFIX wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>
SELECT theWordSense
WHERE { theWordSense wn20schema:word theWord .
theWord wn20schema:lexicalForm "bank"@en-US }
The second option is to query the on-line version of WordNet by doing an HTTP GET on an already known WordNet URI such as http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1. This HTTP GET request returns the Concise Bounded Description of the requested URI, which is an RDF graph that includes all statements in the whole WordNet RDF/OWL which have that URI as its subject (see [CBD, 2004] for details). This is a far less flexible approach because it is not possible to pose queries (e.g. a query for all synsets which contain the word "bank"). However, it does give a sensible set of triples to answer the question "tell me about this resource" if the user has no prior knowledge of this resource.
In version 2.0 of WordNet RDF/OWL, the HTTP GET on http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 returns the following triples (the Concise Bound Description):
wn20instances:wordsense-bank-noun-1 rdf:type wn20schema:NounWordSense wn20instances:wordsense-bank-noun-1 wn20schema:inSynset wn20instances:synset-bank-noun-2 wn20instances:wordsense-bank-noun-1 wn20schema:word wn20instances:word-bank wn20instances:wordsense-bank-noun-1 wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-3 wn20instances:wordsense-bank-noun-1 wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-5 wn20instances:wordsense-bank-noun-1 wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-6 wn20instances:wordsense-bank-noun-1 rdfs:label "bank"@en-US wn20instances:wordsense-bank-noun-1 wn20schema:tagCount "883"@en-US
Because this WordNet version does not have blank nodes and reified triples, the Consice Bounded Description of a the URI http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 is the same as the result of the following SPARQL query:
SELECT http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 ?p ?x
WHERE {<http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1> ?p ?x}
The complete WordNet in RDF/OWL version described here consists of different files and is over 150 MB uncompressed RDF/XML in size. The required memory footprint when loading all files into software such as SWI-Prolog's Semantic Web library may be double that amount (figures vary for different software). To mitigate memory shortage problems and/or improve query response times we have made a separate file for each WordNet relation. The required footprint can be dimished by loading only those files/relations that are required by the application at hand.
WordNet is often used for a task known as sense disambiguation:
the annotation of lexical forms in texts with a synset's ID (or, on the
Semantic Web, its URI) to record the meaning of the lexical form
(cf. [Ide and Véronis, 1998]).
The disambiguation process consists of selecting the appropriate synset.
In the sense disambiguation task (and others in which only the Synsets are of
interest) the WordSenses and Words add memory footprint
which may not be used. To keep the footprint small for such applications we
provide a WordNet Basic version. This version consists of the synset file
of the WordNet Full, an additional data file and a separate schema file.
This last file contains one additional property called senseLabel (domain
Synset and range xsd:string). It also omits classes
and properties that are not used in the Basic version (e.g. WordSense and containsWordSense).
The data file contains
instances of
the new property for each Synset in the synset file. The property value is
filled with the lexical forms
that are attached to Words in the Full version. When selecting
candidate Synsets for a lexical form in a text one queries for
senseLabels matching the lexical forms.
Like for WordNet Full, the Basic users can also limit the relations to those that are required for their task, with the caveat that the following relations are defined between WordSenses and are therefore useless to Basic users: derivational relatedness, antonymy, see also, participle, pertains to.
Each version has a separate RDF/OWL schema file. Although the same schema
could be used for both versions because the Basic schema is a subset of the
Full schema (apart from the additional property senseLabel),
it may be confusing to have classes in the Basic schema which
do not have instances. For clarity two separate schema files were made.
See also WordNet versions for a list of downloadable files.
Below the files to download are listed for version 2.0 of WordNet RDF/OWL. Alternatively, an archive file for both Full and Basic versions are available from the following location: http://www.w3.org/2006/03/wn/wn20/download/.
WordNet 2.0 Full consists of the following three files plus any of the files that contain relations that are listed below.
WordNet 2.0 Basic consists of the following files plus any of the files that contain relations between Synsets
Files that contain relations between Synsets:
Files that contain relations between WordSenses:
Files that provide information on Synsets:
sense from the class WordSense);entails is a transitive relation; hypernymOf is an inverse of hyponymOf.hyponymOf owl:inverseOf hypernymOf.
In other words, querying the hypernymOf relation will return no results
when using software that is not OWL-aware.
Therefore, RDFS users should not use the inverse properties because they
do not yield query results. Because querying for X hypernymOf Y
is just a syntactic variant of querying for Y hyponymOf X
RDFS users do not have less information than OWL users.
See Conversion details for a list of the inverse properties.For some purposes it may be useful to treat WordNet as a class hierarchy, where
each (Noun)Synset is an rdfs:Class and the hyponym relationship
is interpreted as the rdfs:subClassOf relationship. Do note that
this is not a correct interpretation, e.g.
the synset denoting the city "Paris" is a hyponym of the synset denoting "capital",
but "Paris" should be an instance of "capital" instead of a subclass.
Therefore this interpretation was
not added in this version. Users that wish to interpret WordNet as a class
hierarchy may add the following triples to their local triple store:
<rdf:Description rdf:about="&wn20schema;Synset"> <rdfs:subClassOf rdf:resource="&rdfs;Class" /> </rdf:Description> <rdf:Description rdf:about="&wn20schema;hyponymOf"> <rdfs:subPropertyOf rdf:resource="&rdfs;subClassOf" /> </rdf:Description>The first statement makes each instance of Synset an instance of class (effectively, they are both an instance and a class), while the second makes the hyponym property a subproperty of the subclass relationship. This approach has been successfully used in [Wielemaker et al., 2003] for creating a subclass hierarchy so that it can be displayed in standard subclass browsing software.
Requirements that were observed while designing the RDF/OWL conversion are:
The requirement of completeness has been fullfilled by carefully analyzing the conceptual model of WordNet as well as its data. Existing versions and their documentation were used to compare with our analyses. Some results of the analyses are documented here in Introduction to the WordNet datamodel, Relation to previous versions and Conversion details.
To satisfy the requirement of convenience we have provided:
To satisfy the requirement of providing OWL semantics while still being interpretable by pure RDFS tools we have:
owl:disjointFrom statements; rdf:type owl:Class
as well as rdfs:Class;rdf:Property as well as
either owl:DatatypeProperty or owl:ObjectProperty;owl:inverse for each property;owl:TransitiveProperty) for each property.The Prolog distribution consists of eighteen files: one file that represents synsets and then one for each of the seventeen relationships. The file with synsets contains Prolog facts such as:
s(100003009,1,"living_thing",n,1,1). s(100003009,2,"animate_thing",n,1,0).
Each fact denotes exactly one word sense. The word senses with the same synset ID together form a synset. The two facts above together form the synset with the ID 100003009. The arguments of the clause are the following:
Relations are identified by lists of facts like the following:
hyp(100002056,100001740). mp(100004824,100003226). ant(100017087,1,100019244,1).
The first identifies a hyponymy relation between two synsets, the second part meronymy between synsets, the third antonymy between two word senses (second and fourth argument are word numbers). The documentation defines characteristics for each relationship, such as (anti-)symmetry, inverseness and value restrictions on the lexical groups (e.g. nouns, verbs) that may appear in relations. Most of these informally stated requirements can be formalized in OWL and are present in the conversion.
Investigation of the source files and documentation revealed several conflicts between source and documentation. For example, the order of synset arguments of the member meronym relation seems to be different than the documentation asserts. For each conflict we have proposed a solution. Details of the conversion can be found in Conversion details.
The following lists the definition of each Prolog clause as stated in the Prolog distribution's documentation, followed by notes on the meaning of the clause, an example, the mapping to RDF/OWL, OWL characteristics defined for the property, its inverse property and possible conflicts between documentation and source files.
The quotes from the Prolog documentation of Princeton WordNet contain Prolog variables written in lower-case (should start with upper-case letter) and sometimes two variables in one clause that are spelled exactly the same (should have different spelling because two different variables are intended). This has been corrected in the quotes shown below from that documentation for improved clarity.
A s operator is present for every word sense in WordNet. In wn_s.pl, W_num specifies the word number for word in the synset.
The arguments of the clause are the following:
Each s(...) represents one word sense. All s(...) with the same ID together form the whole synset.
Maps to:
The g operator specifies the gloss for a synset.
Gloss is a string.
Maps to: wn:gloss(Synset_ID, Gloss)
The hyp operator specifies that the second synset is a hypernym of the first synset. This relation holds for nouns and verbs. The reflexive operator, hyponym, implies that the first synset is a hyponym of the second synset.
Examples: hyp(100003226,100003009), [organism, living_thing]. hyp(100018827,100017572), [food, substance]. With "reflexive" inverseness is meant.
Maps to: wn:hyponymOf(Synset_ID_A, Synset_ID_B)
Property characteristics: Transitive
Inverse property: wn:hypernymOf
Superproperty: rdfs:comment
The ent operator specifies that the second synset is an entailment of first synset. This relation only holds for verbs.
Example: ent(200001740,200004923) [breathe, inhale], ent(200004701,200004127) [sneeze, exhale]
Maps to: wn:entails(Synset_ID_A, Synset_ID_B)
Property characteristics: Transitive
Inverse property: wn:entailedBy
The sim operator specifies that the second synset is similar in meaning to the first synset. This means that the second synset is a satellite the first synset, which is the cluster head. This relation only holds for adjective synsets contained in adjective clusters.
Maps to: wn:similarTo(Synset_ID_A, Synset_ID_B)
The mm operator specifies that the second synset is a member meronym of the first synset. This relation only holds for nouns. The reflexive operator, member holonym, can be implied.
Example: mm(100006026,107463651). [Person, People]. With "reflexive" inverseness is meant.
Documentation seems to be wrong here. Arguments are the other way around in Prolog source.
Maps to: wn:memberMeronymOf(Synset_ID_A, Synset_ID_B)
Inverse property: wn:memberHolonymOf
Superproperty: wn:meronymOf
The ms operator specifies that the second synset is a substance meronym of the first synset. This relation only holds for nouns. The reflexive operator, substance holonym, can be implied.
Documentation seems to be wrong here. Arguments are the other way around in Prolog source.
Example: ms(102073849,107118730). [oxtail, oxtail soup]. With "reflexive" inverseness is meant.
Maps to: wn:substanceMeronymOf(Synset_ID_A, Synset_ID_B)
Inverse property: wn:substanceHolonymOf
Superproperty: wn:meronymOf
The mp operator specifies that the second synset is a part meronym of the first synset. This relation only holds for nouns. The reflexive operator, part holonym, can be implied.
Documentation seems to be wrong here. Arguments are the other way around in Prolog source.
Example: mp(100004824,100003226). [cell, organism]
Maps to: wn:partMeronymOf(Synset_ID_A, Synset_ID_B)
Inverse property: wn:partHolonymOf
Superproperty: wn:meronymOf
The der operator specifies that there exists a reflexive lexical morphosemantic relation between the first and second synset terms representing derivational morphology.
Documentation seems to be wrong here. The pattern is der(Synset_ID_A,Nr1,Synset_ID_B,Nr2). It seems that the numbers refer to WordSenses within the synsets. "Reflexive" probably means symmetric. Not sure if there are "doubles" in the prolog source like for other predicates (can be excluded when creating triples, but it produces the same triple so does not matter - one could argue whether to create the triple or not when its symmetric counterpart is missing in the source).
Example: der(100002645,3,201420446,4). [unit, unify]
Maps to: wn:derivationallyRelated(WordSense_ID_A, WordSense_ID_B)
Property characteristics: Symmetric
The cls operator specifies that the first synset has been classified as a member of the class represented by the second synset.
Class_type: t:topical, u:usage, r:regional
Examples: cls(100004824,105681603,t), [cell, biology]. cls(100033885,106668368,u), [blind_alley, figure_of_speech]. cls(302439442,108349657,r), [outcaste, India].
Maps to:
Inverse properties: memberInTopic, memberInUsage, memberInRegion
Superproperty: classifiedBy - inverse: memberIn
The cs operator specifies that the second synset is a cause of the first synset. This relation only holds for verbs.
Examples:
cs(200018968,200014429). [cause_to_sleep, sleep/catch_some_Z's]
cs(200020073,200019883). [keep_up, sit_up/stay_up]
cs(200020689,200014429). [anaestesize/put_to_sleep/... ,
slumber/sleep/catch_some_Z's]
Documentation seems to be wrong here. Arguments are the other way around in Prolog source (e.g. anaethesize causes to sleep).
Maps to: wn:causes(A,B)
Inverse property: wn:causedBy
The vgp operator specifies verb synsets that are similar in meaning and should be grouped together when displayed in response to a grouped synset search.
Documentation is unclear. The actual format in the file is vgp(sidA, W_num1, sidB, W_num2). But in wn_vgp.pl the W_num's are always '0'. This seems to mean that the relation holds for all the words in the synset, i.e. the relation holds between synsets.
It seems that the file contains all the symmetric definitions, i.e. vgp(A,0,B,0) means that the file also contains vgp(B,0,A,0). One of the two can be ignored. No problem if the conversion code does not do this, because the asserted double triple is exactly the same. See comment under "der".
Maps to: wn:sameVerbGroupAs(A,B)
Property characteristics: Symmetric
The at operator defines the attribute relation between noun and adjective synset pairs in which the adjective is a value of the noun. For each pair, both relations are listed (ie. each synset_id is both a source and target).
Example: at(101028287,300455926). [mercantilism, commercial]
The inverse version is also listed, so both at(A,B) and at(B,A) are in the source file.
Maps to:
Inverse property: wn:attributeOf
The ant operator specifies antonymous words. This is a lexical relation that holds for all syntactic categories. For each antonymous pair, both relations are listed (ie. each Synset_ID,W_num pair is both a source and target word.)
The synset_id + W_num identifies a word sense.
Maps to: wn:antonymOf(WordSense1, WordSense2)
Property characteristics: Symmetric
The sa operator specifies that additional information about the first word can be obtained by seeing the second word. This operator is only defined for verbs and adjectives. There is no reflexive relation (ie. it cannot be inferred that the additional information about the second word can be obtained from the first word).
The synset_id + W_num identifies a word sense. The statement "no reflexive relation" probably means that the relation is not symmetrical.
Maps to: wn:seeAlso(WordSense1, WordSense2)
The ppl operator specifies that the adjective first word is a participle of the verb second word.
The Synset_ID + W_num identifies a word sense.
Maps to: wn:participleOf(WordSense1, WordSense2)
Inverse property: wn:participle
The per operator specifies two different relations based on the parts of speech involved. If the first word is in an adjective synset, that word pertains to either the noun or adjective second word. If the first word is in an adverb synset, that word is derived from the adjective second word.
Documentation seems to be wrong here. The relation holds between wordsenses, not words. We also split the relation into two properties, as the documentation already indicates.
Maps to:
Inverse property: @@TODO
The fr operator specifies a generic sentence frame for one or all words in a synset. The operator is defined only for verbs.
Example: fr(200610468,8,1).
Maps to: wn:frame(VerbWordSense, xsd:string)
The Synset_ID and W_num together identify a
VerbWordSense that is associated with a particular sentence in
which the verb can be filled in. If the W_num is zero, the sentence
applies to all senses in the Synset. In that case we generate a
wn:frame for each sense in the Synset.
A problem in conversion of this Prolog file is
that the actual sentences are only identified by a number (F_num), and
not present in the actual source. The actual sentences and their number
(F_num) are present in the Unix version of Princeton WordNet, in
a file called frames.vrb. Two example lines (there are 35 lines)
from that file: "26 Somebody ----s that CLAUSE",
"27 Somebody ----s to somebody". We have converted these lines into
a Prolog clause sen(F_Num, String) and stored them in a
file sen.pl, to be able to do the conversion.
Subproperties: partMeronymOf, memberMeronymOf, substanceMeronymOf
Inverse: wn:holonymOf
Subproperties: classifiedByTopic, classifiedByUsage, classifiedByRegion
Inverse: classifies
It is good practice to use the xml:lang attribute
to specify the language in which literals are written.
Currently all the RDF files are given a language tag on the
document level (in RDF tag) for this purpose.
rdfs:labelIt is good practice to give labels to instances, in this case of Word, WordSense and Synset. For Word this is solved by adding wn:lexicalForm rdfs:subpropertyOf rdfs:label. For WordSense the contents for the rdfs:label is chosen by copying the contents of the wn:lexicalForm of the Word. For Synset the first word (according to W_num) is chosen as the label. As there is no preferred status of one WordSense within a Synset (the W_num does not seem to have a specific meaning) this is an arbitrary choice.
The conversion file can be found at: http://www.w3.org/2006/03/wn/wn20/convertwn20.pl. The source code contains instructions for use.
The conversion program makes use of the open-source SWI-Prolog programming language and its Semantic Web library.
The program can be used for conversion of new Princeton WordNet versions to RDF/OWL as long as the format and semantics of the Prolog source files are not changed.
"SKOS Core provides a model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', other types of controlled vocabulary, and also concept schemes embedded in glossaries and terminologies." [SKOS Core Guide, 2005]. Because WordNet may be considered a complex kind of thesaurus, it is natural to try to represent it using SKOS. This version has not concentrated on such a representation, but we list some options for a future version in the SKOS schema.
The central class of SKOS is skos:Concept. Its instances are
connected using the skos:broader/skos:narrower properties. To
each concept one can attach exactly one skos:prefLabel and
zero or more skos:altLabels.
The term "mapping" can have two meanings in this context. In the first meaning,
the schema of WordNet (i.e. its classes and properties) is mapped to the SKOS classes
and properties using rdfs:subClassOf, rdfs:subPropertyOf,
owl:equivalentClass and owl:equivalentProperty.
This is only possible without loss of information if the WordNet schema is equal
to or is a strict specialization of SKOS.
In the
second meaning, a set of rules is specified that converts WordNet into instances
of the SKOS schema. This is a more flexible approach and allows for more complex
mappings (mappings other than property/class equalities and strict specialization).
A first choice concerns what WordNet class(es) to map to skos:Concept.
[@@TODO. See Appendix "Issues"]
This conversion builds on three previous WordNet conversions, namely by:
A fourth conversion by University of Chile was done in parallel with the activities of this TF.
In this document we have not tried to come up with a completely new conversion. Rather, we have studied these existing conversions, filled in some gaps and made a few different decisions. Below we discuss the differences per conversion.
The conversion by Brickley is a partial conversion, as
only the noun-part of WordNet is converted. Of the relations only
the hypernym relation is converted.
Brickley converts the noun hierarchy into rdfs:Classes and
the hyponym relationship into
rdfs:subClassOf. This is an attractive
interpretation, but we argue that not all hyponyms can be
interpreted in that way. For example,
the synset denoting the city "Paris" is a hyponym of the synset
denoting "capital", but "Paris" should be an instance of "capital"
instead of a subclass. An attempt to provide a consistent
semantic translation of hyponymy has been done [Gangemi, 2003], but in this work we
explicitly avoid semantic translation of the intended meaning
of WordNet relations.
The conversion by Decker & Melnik is also a partial one. It does convert all synset types, but only three of the WordNet relations. Another difference is that it attaches word forms as labels to the Synset instances. Hence WordSenses and Words do not have a URI.
The two previous conversions are based on an older version of Princeton WordNet and are not updated as far as the TF can tell. Both provide RDFS semantics, but not OWL semantics.
The conversion of Neuchatel is close to the one in this
document. It has roughly the same class hierarchy, with two
exceptions. Firstly, it contains a class to represent word senses,
but does not have a separate class for words. Secondly,
it defines classes like "Nouns_and_Adjectives" (with subclasses Noun
and Adjective). The "Nouns_and_Adjectives" classes are used
in restriction definitions, where we have chosen to use owl:unionOf,
because it better reflects the actual semantics.
Aonther difference with this conversion is that Neuchatel is in
pure OWL (e.g. all properties are either owl:ObjectProperty or
owl:DatatypeProperty), while the conversion of the TF is
both in RDFS and OWL (e.g. each OWL property is also defined
to be an rdfs:Property).
The conversion by the TF splits some relations into sub-relations,
because their semantics warranted such a separation. For
example, the Prolog relationship per denotes
(a) a relation between an adjective and a noun or adjective
or (b) a relation between an adverb and an adjective. We
convert per into
adjectivePertainsTo and
adverbPertainsTo. The Neuchatel conversion does not
provide sub-relations, and omits relations "derivation" and "classification".
and also does not provide inverses for all relationships. The conversion uses
hash URIs, while the TF's uses slash URIs
(the benefits of the slash approach are described in Hash versus
slash URIs).
The main advantages of the conversion by the TF in comparison to the Neuchatel
conversion is that it is more complete,
uses slash URIs, is interpretable by both RDFS and OWL infrastructure,
and represents Words as first-class citizens.
Representing words as first-class citizens allows fine-grained mappings to WordNets in other languages. Future integration of WordNet with WordNets in other languages can be done on three levels: relating Synsets, relating WordSenses and relating Words from the different WordNets to each other. However, as the other conversions do not provide URIs for words, these only allow integration on the first two levels. For future integration of WordNet with other multilingual resources it is essential that one can refer to two different words with the same lexical form, or two words with a different lexical form but similar meanings.
The conversion by University of Chile was made in parallel to the efforts of this TF (see e.g. this mail on the public-swbp-wg@w3c.org mail archive). It has almost the same class hierarchy as this conversion; only the class Collocation is not present. The schema is modelled in RDFS, so it does not define restrictions, disjointness axioms, property characteristics and inverse properties. It does not have the superproperties for WN relations that we have introduced, and it uses hash URIs. The main technical advantages of the version by this TF is that it includes OWL semantics and that it uses slash URIs.
The previously mentioned conversions do not convert the frame sentences, while the TF's conversion and the conversion of University of Chile include them.
A practical advantage of the TF's conversion over the other conversion is the availability of a Basic and Full version and separate files for the WN relations.
In summary, the advantages of the TF's conversion over other versions are that it is complete, uses slash URIs, provides OWL semantics while still being interpretable by RDFS infrastructure, provides a Basic and Full version, and provides URIs for words.
We have chosen to introduce identifiers for the instances of classes Synset, WordSense and Word. We use the base uri + a locally unique ID. Three kinds of entities need a URI: instances of the classes Synset, WordSense and Word. Instead of generating any unique ID we have tried to use IDs derived from information in the source and also tried to make them human-readable. Because the IDs have distinct syntactic patterns, it is easy to identify the type of the resource (Synset, WordSense or Word) by examining the URI. The patterns are described in Primer to using RDF/OWL WordNet.
We use two different namespaces: one for the schema and one for the instances. This makes it possible to manage the schema separately from the instances.
Some words contain characters that are not allowed in NCNames. In order to generate a correct URI we changed the following characters into underscores: '/', '\','(', ')' and ' ' (space). For example, the URI for the word "read/write_memory" becomes:
http://www.w3.org/2006/03/wn/wn20/instances/word-read_write_memory
The motivation for representing words as instances of a class with their own URIs instead of as labels or blank nodes is discussed in Relation to previous versions.
http://wordnet.princeton.edu/wn/wordsense-bank-noun-1 http://wordnet.princeton.edu/wn#wordsense-bank-noun-1The disadvantage of hash URIs is that when a HTTP GET is done (e.g. for the second example above) the browser will return the whole document located at http://wordnet.princeton.edu/wn. The reason for this is that servers do not receive the fragment identifier. Because WordNet is very large this is not a desirable option. (There is a work-around defined in [URI QA, 2004] that utilizes a special HTTP message header, but this would require a commitment from both client and server to use this special format.) The alternative is to use slash URIs. This choice implies that a decision needs to be made on which statements the server should return when an HTTP GET is done for resources with a URI such as http://www.w3.org/2006/03/wn/wn20/instances/synset-bank-noun-2. [@@REFS for def of hash/slash URIs and the frag id problem] Possible choices are:
rdf:type, rdfs:subClassOf);The difference between the two last ones is that the Symmetric CBD not only includes statements for which the URI is the subject, but also those for which the URI is the object. We have chosen for the CBD of the URI because it "constitutes a reasonable default response to the request 'tell me about this resource'" [CBD, 2005].
Note that a variant of Recipe 5 in [Recipes, 2006] may be used to implement the HTTP GET on these WN URIs.
This section contains two language related topics. First of all, Princeton
WordNet is
a source that documents American English. To reflect this in the conversion, all
RDF documents of this conversion are declared to be written in
American English by adding the
xml:lang='en-US' to the RDF tag of all WordNet files.
Secondly, it is desirable to be able to integrate other existing WordNets
in other languages in the future
(for a list of available WordNets see http://www.globalwordnet.org/gwa/wordnet_table.htm).
Although this
TF does not have the goal of performing such integration, it has the intention
of making such integration possible with this RDF/OWL version of Princeton WordNet.
Integration of WordNets implies creating mappings between entities in the WordNets
to indicate lexico-semantic relationships between them, e.g. a property that
signifies that the
meanings of two Synsets overlap. The entities that represent language concepts
that should be able to map are instances of the classes: Synset, WordSense and Word.
To this end this conversion supplies URIs for instances of all three classes.
We have not given the URIs in this conversion a part that encodes the language,
such as http://www.w3.org/2006/03/wn/wn20/en/synset-bank-noun-2.
The reason is that two WordNets in different
languages require different base URIs. This alone guarantees uniqueness of e.g. the
Word "chat" in an English WordNet and the word "chat" (cat) in a French WordNet.
Identification of the language a particular word belongs to can also be done by using
the xml:lang tag.
This document was originally written as if there will be an RDF/OWL version of each Princeton WordNet edition. Is this feasible? There should be an institute who takes responsibility for not only creating new versions but also making them available for online use. Concerning creating new versions: when new versions by Princeton only differ from previous in its content, then this is just a matter of running the Prolog conversion program and putting the new version online. This document describes the convertion of Princeton version 2.0. In version 2.1 there is at least one structural difference, namely the introduction of a "instanceOf" relationship.
This version is based on the Princeton WordNet 2.0. Is it feasible and desirable to find an institute willing to commit to maintaining WordNet for a longer period, say two years? This also entails bringing out a new RDF/OWL version for each new Princeton version. Without such a commitment the RDF/OWL version presented in this document will be outdated within one or two years because of updates to the original source.
An idea suggested in an earlier version of this Draft was to introduce a new base URI for each new version, and to use redirection from a stable URI which redirects to the newest available version of RDF/OWL WordNet. For example, the URI
http://wordnet.princeton.edu/wn/can redirect to the latest version, e.g. 2.0 as in
http://wordnet.princeton.edu/wn20/Some text suggested in an earlier version of this draft follows below:
When users download WordNet, they download a specific version of WordNet in RDF/OWL that has a version number that corresponds to the Princeton WordNet version on which it is based. To distinguish the different available versions, each version has a version-specific base URI, such as:
http://wordnet.princeton.edu/wn20/
After downloading and loading WordNet in RDF/OWL into a triple store this version-specific base URI should be used when querying. The query examples below use version 2.0 as an example. See WordNet versions for more information.
Notice that if the base URI http://wordnet.princeton.edu/wn/ is used for the HTTP GET, then the base URI of the returned triples is different. This is because the request is forwarded by Princeton to the base URI of the newest WordNet version (see WordNet versions).
There are two choices concerning versioning which any new user of RDF/OWL WordNet has to make. First of all, one has to choose whether to use the Basic or Full version. Secondly, there are different versions published of Princeton WordNet and converted into RDF/OWL (version 2.0, version 2.1 etcetera). It should be prevented that an "old" and a "new" synset are collapsed into one synset by an RDF triple store because they have the same URI when using two versions in one store (e.g. because of legacy data mixed with data indexed with the newest WordNet version). If this does happen, the properties of the old and new synset are mixed, which is not appropriate (it becomes impossible to distinguish which property/value pair belongs to which version of the synset) To prevent this, each conversion published by the TF has a separate namespace. (Currently only version 2.0 is converted into RDF/OWL, but there will be more conversions available in the future.) A service at Princeton automatically redirects from the namespace
http://wordnet.princeton.edu/wn/to the namespace of the newest version, e.g.
http://wordnet.princeton.edu/wn20/
This allows users to keep working with the WordNet version for which they programmed their software (e.g. http://wordnet.princeton.edu/wn20/) regardless of changes in new versions. Therefore we recommend that programmers base their code on the version-specific base URI instead of the general namespace that redirects to the newest version-specific base URI.
When two different versions are to be used in concord, it may be necessary to establish a mapping between the synset for "financial institution" in the older and newer version. This can be a complex task because although the synset itself and its word senses may remain the same, the surrounding synsets may have changed, making it difficult to decide whether the two synsets are "the same" or not. Providing such mappings between synsets in different versions is out of the scope of this TF. It may also be unappropriate to provide such mappings because what constitutes a "correct" mapping may differ between applications.
See also Versioning and redirection strategy for more information on the redirection strategy.URIs can be used as a means of primitive queries. The following URI in WordNet refers to the first NounWordSense of the word "bank", which is an RDF node in WN:
http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1.The current proposal is to return the CBD [CBD, 2005] of the requested RDF node. Many agents would probably like somewhat bigger chunks of data at once, e.g. all WordSenses of "bank". This could be done by returning the set of WordSenses with the Word "bank" on HTTP GETs on e.g. the URI:
http://www.w3.org/2006/03/wn/wn20/instances/wordsenseset-bank.
A full SPARQL service for WN can also address this need, but this is a nice alternative that does not require all agents to understand SPARQL. Another reason is that running a SPARQL service requires more resources from the hosting institute. However, this second (type of) URI does not refer to any RDF node or RDF arc. Is this use of URIs "accepted practice" (or could become such a thing) or "should be avoided at all costs" because the approach mixes the naming of nodes with naming sets of nodes? See also http://lists.w3.org/Archives/Public/public-swbp-wg/2006Mar/0076.html. If it is a good idea, how should the URIs be constructed?
Each WordSense in a Synset has a "W_num" (starting from 1). It seems that this is not essential ordering information (i.e. only used to distinguish between word senses in the prolog source), so it has not been included in the conversion. Similar point for the sense_number in the prolog source.
Have to check with Princeton if indeed this information is not vital and also check with user community if they are not using these numbers.
The Prolog source sometimes contains symmetrical pairs, e.g. the source file for antonyms should contain ant(A,B) but also ant(B,A) according to the documentation. However, the conversion program finds clauses where this is not the case. Currently the program does NOT add an antonym in the RDF for such cases.
Need to check with Princeton if these are either omissions or errors.
There seems to be additional semantics in the frame sentences, and hence could be alternative ways to convert the sentences. For example, the structure of the sentences seems to fall into two parts, plus an optional part. For example, the frame sentence "Somebody ----s that CLAUSE" has a prefix, a postfix and a lexical category. These could be extracted and added as properties to an instance of a class Frame. It should be checked with users if this information is useful and with Princeton about the meaning of the lexical categories.
[Brickley, 1999] D. Brickley. Message to RDF Interest Group: "WordNet in RDF/XML: 50,000+ RDF class vocabulary". http://lists.w3.org/Archives/Public/www-rdf-interest/1999Dec/0002.html See also http://xmlns.com/2001/08/wordnet/.
[Brickley, 2002] Dan Brickley, RDFWeb: co-depiction photo metadata http://rdfweb.org/2002/01/photo/.
[Brickley and Miller, 2005] Dan Brickley and Libby Miller, FOAF Vocabulary Specification Namespace Document 27 July 2005 http://xmlns.com/foaf/0.1/
[Broekstra et al., 2002] Jeen Broekstra, Arjohn Kampman, Frank van Harmelen. Sesame: An Architecture for Storing and Querying RDF and RDF Schema [PDF] In Proceedings of the First International Semantic Web Conference (ISWC 2002), Sardinia, Italy, June 9-12 2002, pg. 54-68. Springer-Verlag Lecture Notes in Computer Science (LNCS) no. 2342.
[CBD, 2005] CBD - Concise Bounded Description W3C Member Submission 3 June 2005; http://www.w3.org/Submission/2005/SUBM-CBD-20050603/
[Decker & Melnik] S. Decker and S. Melnik. WordNet RDF representation. http://www.semanticweb.org/library/
[Fellbaum, 1998] C. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998.
[Gangemi, 2003] A. Gangemi, N. Guarino, C. Masolo, and A. Oltramari. Sweetening WORDNET with DOLCE. AI Magazine, 24(3):13-24, 2003.
[Graves, 2005] Alvaro Graves http://www.dcc.uchile.cl/~agraves/wordnet/
[Guarino et al., 1999] Nicola Guarino, Claudio Masolo, and Guido Vetere. Ontoseek: Content-based access to the web. IEEE Intelligent Systems, 14(3):70--80, May/June.
[Hollink et al., 2003] L. Hollink, A. Th. Schreiber, J. Wielemaker, and B. J. Wielinga. Semantic annotation of image colletions. In S. Handschuh, M. Koivunen, R. Dieng, and S. Staab, (eds), Knowledge Capture 2003 - Proceedings Knowledge Markup and Semantic Annotation Workshop, pages 41--48.
[Ide and Véronis, 1998] Nancy Ide and Jean Véronis. Introduction to the special issue on word sense disambiguation: the state of the art. Computational Linguistics, 24(1):2--40, March.
[OWL Overview, 2004] Deborah L. McGuinness, Frank van Harmelen (eds.). OWL Web Ontology Language Overview, W3C Recommendation 10 February 2004; http://www.w3.org/TR/owl-features/
[RDF Primer, 2004] Brickley D., Guha R.V. (Eds). RDF Vocabulary Description Language 1.0: RDF Schema, W3C Recommendation 10 February 2004; http://www.w3.org/TR/2004/REC-rdf-schema-20040210/
[Recipes, 2006] Miles, A., Baker, T. and Swick, R. (Eds). Best Practice Recipes for Publishing RDF Vocabularies, W3C Working Draft 14 March 2006 http://www.w3.org/TR/2006/WD-swbp-vocab-pub-20060314/
[SKOS Core Guide, 2005] Alistair Miles and Dan Brickley (eds). SKOS Core Guide, W3C Working Draft 2 November 2005; http://www.w3.org/TR/2005/WD-swbp-skos-core-guide-20051102/
[SPARQL, 2005] SPARQL Query Language for RDF W3C Working Draft 23 November 2005; http://www.w3.org/TR/2005/WD-rdf-sparql-query-20051123/
[SWI Prolog, 2006] http://www.swi-prolog.org/
[University of Neuchatel] WordNet OWL Ontology; http://www2.unine.ch/imi/page11291_en.html
[URI QA, 2004] The URI Query Agent Model - A Semantic Web Enabler; http://sw.nokia.com/uriqa/URIQA.html
[Wielemaker et al., 2003] Jan Wielemaker, Guus Schreiber and Bob Wielinga. Prolog-based infrastructure for RDF: performance and scalability. In: D. Fensel, K. Sycara and J. Mylopoulos (eds.) The Semantic Web - Proceedings ISWC'03, Sanibel Island, Florida. Lecture Notes in Computer Science, volume 2870, pp. 644-658. Berlin/Heidelber, Springer-Verlag, 2003.
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