A Multilingual Lexico-Semantic Database andOntology
Francis Bond, Christiane Fellbaum, Shu-Kai Hsieh,
Chu-Ren Huang, Adam Pease and Piek Vossen

Abstract We discuss the development of a multilingual lexicon linked to the SUMO
formal ontology. The ontology as well as the lexicon have been expressed in OWL,
as well as their original formats, for use on the semantic web and in linked data.
We describe the Open Multilingual Wordnet, a multilingual wordnet with twenty
two languages and a rich structure of semantic relations. It is made by exploiting
links from various monolingual wordnets to the English Wordnet. Currently, it contains 118,337 concepts expressed in 1,643,260 senses in 22 languages. It is available
as simple tab separated files, wordnet-LMF or lemon and had been used by many
projects including Babelnet and Google translate. We then discuss some issues inextending the wordnets and improving the multi-lingual representation to cover concepts not lexicalized in English and how concepts are stated in the formal ontology.
Key words: Semantic Lexicon, Multilingual, Wordnet, Open Data, Ontology
Francis Bond
Nanyang Technological University, Singapore e-mail: bond@ieee.org
Christiane Fellbaum
Princeton University, USA e-mail: fellbaum@princeton.edu
Shu-Kai Hsieh
National Taiwan University, Taipei e-mail: shukaihsieh@ntu.edu.tw
Chu-Ren Huang
Hong Kong Polytechnic University e-mail: churen.huang@polyu.edu.hk
Adam Pease
Articulate Software, USA e-mail: apease@articulatesoftware.com
Piek Vossen
Vrije Universiteit, Amsterdam e-mail: piek.vossen@vu.nl
Final version before submission: Appears in: Paul Buitelaar and Philipp Cimaniao (eds), 2014, Towards the Multilingual Web, Springer-Verlag, pp 243–258 DOI
10.1007/978-3-662-43585-4 15 (pagination is different in the final version)

243

244

A Multilingual Lexico-Semantic Database and Ontology

1 Introduction
What do words mean and how are the words in different languages related? We makea start at answering these questions with a large multilingual lexical database andformal ontology. Each formalism captures knowledge about words and language ina different way. Linked together, they form a unified representation of knowledge
suitable for language processing and logical reasoning.
An electronic lexicon is a fundamental resource for computational linguistics
in any language, and Princeton’s English WordNet (PWN) (Fellbaum, 1998) has
become a de facto standard in English computational linguistics. WordNet represents meanings in terms of lexical and conceptual links between concepts and word
senses. This allows us to model how concepts are represented in various languages.
Ontologies offer a complementary representation where concepts are defined more
axiomatically and can be formally reasoned with. The Suggested Upper Merged Ontology (SUMO) model of meaning (Pease, 2011) addresses language-independent
concepts, formalized in first- and higher-order logic. Bringing these two models
together (Niles & Pease, 2003) has resulted in a uniquely powerful resource formulti-lingual computational processes.
There have been a number of efforts to create wordnets in other languages thanEnglish. The EuroWordNet (EWN) project provided a first solution for also connecting these wordnets to each other by introducing a shared InterLingual Index(ILI), (Vossen, 1998). The ILI was based on the English Wordnet (mainly for pragmatic reasons) and was considered as an unstructured fund of concepts for linking
synsets across wordnets.
Most wordnets developed since EWN have used PWN as a common pivot towhich each new wordnet is linked. This has the drawback of making English a privileged language, and creating a certain linguistic bias. Since all languages have a
different set of lexicalized concepts, it is not possible to have an interlingua where
everything is lexicalized in all languages. A solution to this was proposed in the
ILI using the union of synsets from all languages, arranged and related via the semantic links of PWN (Laparra et al., 2012). In this case, wordnets in the individual
languages do not have to lexicalize all synsets but can still be linked together.
Another approach is to use a language-independent formal ontology – SUMO
(Pease, 2006) – as the common hub, which allows for the creation of arbitrary new
concepts that can eventually encompass the union of lexicalized concepts in all languages. This has additional advantages such as a logical language for creating definitions of concepts that can be checked automatically for logical consistency, and amuch larger inventory of possible relations among concepts. Using the ILI as an intermediate approach collects and arranges synsets that are in need of formalization,
while defering that effort to a later time. It is hoped that by cataloging these synsets
it should be possible to have some of the benefits of a common hub, while speeding
construction. This will likely be used as input to full SUMO-based formalizationsin the future.
Currently we are exploring both approaches in parallel — creating an ILI (not yet
released) and extending SUMO (which has been released and is regularly updated).

A Multilingual Lexico-Semantic Database and Ontology

245

A key organizational challenge for a true multilingual lexico-semantic databasehas been the large-scale nature of the effort needed. Each wordnet project has generally had its own funding and processes, even when coordinated in a broad sense withthe original PWN. A variety of formats have proliferated. Wordnets do not all link
to one another or a central ontology. Another challenge has been that some wordnets
have not been released under open licenses and thus cannot be legally redistributed.
This has greatly improved since the initial survey in (Bond & Paik, 2012) with many
more wordnets being made open (Bond & Foster, 2013). Some years ago, we introduced the idea of combining wordnets in a single resource 1 (Pease et al., 2008).
This original vision has now been realized in the Open Multilingual Wordnet described in Section 4. At the time of this writing, there are 22 wordnets that havebeen put into a common database format and linked to SUMO.
In the next section we describe the Princeton Wordnet in more detail. We thenintroduce the linked ontology, SUMO (§ 3). In the next section we describe how
we built and made accessible the open multilingual wordnet: the main new resource
described here (§ 4). Finally we discuss how it can be extended to cover more languages better (§ 5).

2 Princeton English WordNet
Princeton WordNet (PWN: Fellbaum, 1998) is a large lexical database comprising nouns, verbs, adjectives and adverbs. Cognitively synonymous word forms
are grouped into synsets, each expressing a distinct concept. Within each synset,
words are linked by synonymy. Synsets are interlinked by means of lexical relations(among specific word forms) and conceptual relations (among synsets). Examplesof the former are antonymy and the morphosemantic relation; examples of the latterare hyponymy, meronymy and a set of entailment relations. The resulting network
can be navigated to explore semantic similarity among words and synsets. PWN’sgraph structure allows one to measure and quantify semantic similarity by simple
edge counting; this makes PWN a useful tool for computational linguistics and natural language processing.
The main relation among words in PWN is synonymy, as between the words shutand close or car and automobile. A group of synonyms – words that denote the same
concept and are interchangeable in many contexts – is grouped into an unorderedset. Synsets are linked to other synsets by means of a small number of conceptual
relations, such as hyperonymy, meronymy and entailment. Additionally, eachsynset contains a brief definition and, in most cases, one or more short sentences
illustrating the use of the synset members. Word forms with several distinct meanings are represented by appearing in as many distinct synsets as there are meanings.
Thus, each form-meaning pair (or sense) in PWN is unique.
1 http://www.globalwordnet.org/gwa/gwa_grid.html

246

A Multilingual Lexico-Semantic Database and Ontology

3 SUMOThe Suggested Upper Merged Ontology2 (Niles & Pease, 2001; Pease, 2011) began as just an upper level ontology encoded in first order logic. The logic has expanded to include higher order elements. SUMO itself is now a bit of a misnomer
as it refers to a combined set of theories: (1) the original upper level, consisting ofroughly 1,000 terms, 4,000 axioms and some 750 rules. (2) A MId-Level Ontology(MILO) of several thousand additional terms and axioms that define them, coveringknowledge that is less general than those in the upper level. We should note that
there is no objective standard for what should be considered upper level or not. (3)There are also a few dozen domain ontologies on various topics including theories of
economy, geography, finance and computing. Together, all ontologies total roughly
22,000 terms and 90,000 axioms. There are also an increasing group of ontologies
which are theories that consist largely of ground facts, semi-automatically createdfrom other sources and aligned with SUMO. These include YAGO (de Melo et al.,2008), which is the largest of these sorts of resources and has millions of facts.
SUMO is defined in the SUO-KIF language,3 which is a derivative of the original
KIF (Genesereth, 1991). It has been translated automatically, although in what is anecessarily very lossy translation into the W3C Web Ontology Language (OWL).4
The translation also includes a version of PWN in OWL, 5 and the mappings between them.6
SUMO proper has a significant set of manually created language display templates that allow terms and definitions to be paraphrased in various natural languages. These include Arabic, French, English, Czech, Tagalog, German, Italian,
Hindi, Romanian, and Chinese (traditional and simplified characters).
SUMO has been mapped by hand to the entire PWN lexicon (Niles & Pease,2003). The mapping statistics are given in Table 1. There are a number of other approaches for mapping ontologies to wordnets (Fellbaum & Vossen, 2012; Vossen &Rigau, 2010). However these have not involved ontologies that are either comparable in size or degree of formalization to SUMO.
2 www.ontologyportal.org
3
http://sigmakee.cvs.sourceforge.net/viewvc/sigmakee/sigma/
suo-kif.pdf
4 http://www.ontologyportal.org/SUMO.owl
5 http://www.ontologyportal.org/WordNet.owl
6 http://sigma-01.cim3.net:8080/sigma/OWL.jsp?kb=SUMO also provides a”live” generation of OWL one term at a time, where ”&term=name” can be appended to the URL
and the desired term name substituted for ”name”.

A Multilingual Lexico-Semantic Database and Ontology

noun
verb
adj
adverbtotal

247

instance equivalence subsuming
9,837
3,329
68,9190600
13,150724
540
14,77157
99
3,235
10,6184,568

100075

Table 1 SUMO WordNet mappings (115,261 total)

4 Open Multilingual Wordnet
Wordnets have now been made for many languages. The Global Wordnet Association currently lists over 60 wordnets.7 The individual wordnets are the result ofmany different projects and vary greatly in size and accuracy. The Open Multilingual Wordnet (OMW)8 provides access to some of these, all linked to the PWN and
SUMO. The goal is to make it easy to access lexical meaning in multiple languages.
OMW has (i) extracted and normalized the data, (ii) linked it to PWN 3.0 and (iii)
put it in one place. It includes a simple search interface that uses the SQL databasedeveloped by the Japanese Wordnet.
In order to make the wordnets more accessible, we have built a simple serverwith information from those wordnets whose licenses allows us to do so. It is basedon a single shared database with all the languages in it. We only include data thatis open: “anyone is free to use, reuse, and redistribute it — subject only, at most, tothe requirement to attribute and/or share-alike”.9
The accessibility of the data means that it is becoming widely used. Babelnet
2.0,10 a very large multilingual encyclopedic dictionary and semantic network, ismade by combining the OMW, PWN, Wikipedia and Omegawiki (a large collaborative multilingual dictionary). Google Translate11 also uses the OMW data.
The majority of freely available wordnets have been based on the expand
approach, basically adding lemmas in new languages to existing PWN synsets(Vossen, 1998, p11). These wordnets can easily be combined by using the PWN as
a pivot. We realize that this is an incomplete solution and a better one is discussed
in Section 5.2. Some wordnets are based on the merge approach, where independent language specific structures are built first and then some synsets linked to the
PWN. For those merged wordnets in the OMW (Danish and Polish), only a small
subset are actually linked, due more to lack of resources to link them than semantic
incompatibility.
Adding a new language to the OMW turned out to be difficult for two reasons.
The first problem was that the wordnets were linked to various versions of PWN.
7 http://globalwordnet.org/
8 http://compling.ntu.edu.sg/omw
9 Definition from the Open Knowledge Foundation: http://opendefinition.org/.
10 http://babelnet.org/about.jsp
11 http://translate.google.com/about/intl/en_ALL/

248

A Multilingual Lexico-Semantic Database and Ontology

In order to combine them into a single multilingual structure, we had to map to acommon version. The second problem was the incredible variety of formats thatthe wordnets are distributed in. Almost every project used a different format andthus required a new script to convert it. In fact, different releases from the same
project often had slightly different formats. These two problems mean that, even if awordnet is legally available, there is still a technical hurdle before it becomes easily
accessible.
The first problem can largely be overcome using the mappings from Daude et al.
(2003). Mapping introduces some distortions. In particular, when a synset is split,
we chose to only map the translations to the most probable mapping, so some new
synsets will have no translations. For example, the synset pwn16-legn:8 “a section
or portion of a journey or course” in PWN 1.6 maps to two senses in PWN 3.0:
pwn30-legn:9 “a section or portion of a journey or course” and pwn30-legn:8 “thedistance traveled by a sailing vessel on a single tack”. pwn16-legn:8 to pwn30-legn:9
is the most probable mapping, so any lemmas associated with pwn16-legn:8 will be
associated only with pwn30-legn:9.
The second problem we have currently solved through brute force, writing a
new script for every new wordnet we add. We discuss better possible solutions in
Section 5.2. In the future, we hope people will move to a common standard forexchange, with Wordnet-LMF being the strongest contender (Vossen et al., 2013).
The server currently includes English (Fellbaum, 1998); Albanian (Ruci, 2008);
Arabic (Black et al., 2006); Chinese (Huang et al., 2010; Wang & Bond, 2013);12
Danish (Pedersen et al., 2009); Finnish (Lindén & Carlson., 2010); French (Sagot &Fišer, 2008); Hebrew (Ordan & Wintner, 2007); Indonesian and Malaysian (Nurril
Hirfana et al., 2011); Italian (Pianta et al., 2002); Japanese (Isahara et al., 2008);
Norwegian (Bokmål and Nynorsk: Lars Nygaard 2012, p.c.); Persian (Montazery& Faili, 2010); Polish (Piasecki et al., 2009); Portuguese (de Paiva & Rademaker,2012); Thai (Thoongsup et al., 2009) and Basque, Catalan, Galician and Spanish
from the Multilingual Common Repository (Gonzalez-Agirre et al., 2012).
The wordnets are all in a shared sqlite database with either Python or PERL
cgi clients using the wordnet module produced by the Japanese Wordnet project(Isahara et al., 2008). The database is based on the logical structure of the PWN,
with an additional language attribute for lemmas, examples, definitions and senses.
It is thus effectively a single open multilingual resource. We summarize the size of
the wordnets and their coverage of core concepts in Table 2. Core concepts are the5,000 synsets proposed as a core lexicon based on the frequency of the word forms
in the British National Corpus (Burnard, 2000) and an intuitive sense of salience(Boyd-Graber et al., 2006). That is, the core concepts are frequently occurring concepts (at least in British English).
We make available the synset-lemma pairs as tab separated files, where they canbe used by the Natural Language Tool Kit13 (Bird et al., 2009) as well as WordNet12 Users see the union of the data from the two Chinese wordnets.
13 With the extensions that were added with the Japanese translation by Masato Hagiwara (Birdet al., 2010).

A Multilingual Lexico-Semantic Database and Ontology

249

WordnetLang Synsets Words Senses Core Licence
Albanetals
4,676 5,990 9,602 31% CC BY 3.0
Arabic WordNet (AWN) arb
10,165 14,595 21,751 48% CC BY SA 3.0
Chinese Wordnet (Taiwan) cmn
4,913 3,206 8,069 28% wordnet
Chinese Open Wordnet
cmn 42,316 61,536 79,812 99% wordnet
DanNetdan
4,476 4,468 5,859 81% wordnet
Princeton WordNet
eng 117,659 148,730 206,978 100% wordnet
Persian Wordnet
fas
17,759 17,560 30,461 41% Free to use
FinnWordNetfin
116,763 129,839 189,227 100% CC BY 3.0
WOLF
fra
59,091 55,373 102,671 92% CeCILL-C
Hebrew Wordnet
heb
5,448 5,325 6,872 27% wordnet
MultiWordNet
ita
34,728 40,343 61,558 83% CC BY 3.0
Japanese Wordnet
jpn
57,179 91,959 158,064 95% wordnet
Multilingual
cat45,826 46,531 70,622 81% CC BY 3.0
Centraleus
29,413 26,240 48,934 71% CC BY-NC-SA 3.0
Repository
glg
19,312 23,124 27,138 36% CC BY 3.0
(MCR)
spa
38,512 36,681 57,764 76% CC BY 3.0
Wordnet Bahasa
ind51,755 64,948 142,488 99% MIT
Wordnet Bahasa
zsm 42,615 51,339 119,152 99% MIT
Norwegian Wordnet
nno
3,671 3,387 4,762 66% wordnet
Norwegian Wordnet
nob
4,455 4,186 5,586 81% wordnet
plWordNet
pol
14,008 18,860 21,001 30% wordnet
OpenWN-PT
por
41,810 52,220 68,285 79% CC by SA 3.0
Thai Wordnet
tha
73,350 82,504 95,517 81% wordnet
Table 2 Available Wordnets

LMF (Lexical Markup Framework: Vossen et al., 2013) and lemon (McCrae et al.,2011).14
Finally, we also make the SQL database available (with all languages except
French and Basque, whose licenses are incompatible with the others). We use a
simple database schema extended from the schema for the Japanese wordnet (Bondet al., 2009). When we use the combined database in applications, we typically use
the database directly, or through the Perl interface. Licenses that allow redistribution of derivative works allow people to make the entire lexicons available in any
format, thus greatly improving their usefulness. There are also APIs for the databaseproduced by other researchers in Python, Java, Ruby, Objective-C, Gauche and analternative Perl module.15
There has been much research on making Wordnets available to the semantic
web, including formatting as RDF (van Assem et al., 2006; Koide et al., 2006),
serving LMF directly (Savas et al., 2010) or serving them through the lemon format (McCrae et al., 2011). Typically, these do not involve any changes in the actual content, the emphasis is instead on making it more easily accessible as Linked
Open Data (Berners-Lee, 2009). The proliferation of these approaches suggests that
there is still some way to go until we will have an agreed upon universal standard.
Therefore, our approach has been to make our data open, clearly documented, well14 Thanks to John P. McCrae for help in adding this.
15 http://nlpwww.nict.go.jp/wn-ja/index.en.html

250

A Multilingual Lexico-Semantic Database and Ontology

formatted and validated in a simple format we use ourselves (tab separated text) andsome standard formats for exchange (LMF and lemon). This can then be straightforwardly converted to whatever format is desired by those who want it in that format. Currently, in most of our use scenarios (principally word sense disambiguationand semantic processing) the latency of a web interface is problematic — we expectthat most of the users of our data will want to download the entire lexicon, and thisis what we offer.

Possible Wordnet Structural Enhancements
In this section we will discuss some extensions people have suggested to the structure of the original PWN: these are not currently part of the open wordnet. One
advantage of having many language specific projects loosely coordinated is that
there can be a wide variety of experimentation.
Our conversion scripts basically reduce each wordnet to a list of synset-lemma
pairs, plus frequency, definitions and examples if available. Everything is mapped
to PWN 3.0 synsets. Therefore, the current version loses any synsets not in the
English 3.0 wordnet. Many of the wordnets have such synsets, as well as meta-data,
definitions, examples and other useful information. One of the ongoing goals of theOMW project is to make this information more easily accessible between projects.
We do not consider wordnets with licenses that do not allow redistribution, as
we cannot legally include them. This includes some very well constructed wordnets
with excellent coverage, such as the Dutch,16 German and Korean wordnets (Vossen
et al., 2008; Kunze & Lemnitzer, 2002; Yoon et al., 2009). It is unfortunate that theycannot be integrated into the Open Wordnet. Some wordnets are built with their own
structure and do not link to the PWN. These also cannot be included. Finally, somewordnets were not included even though they were open as the quality was still too
poor due to the fact that they had been automatically made, with very little quality
control.
Many of the wordnet projects extend the PWN relations in some way. For example, EWN defined many cross-part-of-speech links: hammern:1 is an involvedrole of hammerv:1 (Vossen, 1998, pp97–110). Another instance of extensions is the
Chinese Wordnet (Taiwan) which takes a different approach in representing lexical
meanings. Unlike most models of lexical ambiguity resolution that assume only one
meaning is chosen in a given context, it allows more than one (related) meanings
to co-exist in the same context. A lexical item is actively complex if it allows simultaneous multiple readings.17 Meaning extensions thus are proposed to be distin16 We are delighted to see that an Open Dutch Wordnet will be released soon (Vossen & Postma,2014) and will integrate it as soon the data is available.
17 Note that according to psycholinguistic studies from Ahrens et al. (1998), there are two types ofactive complexity in natural language. The first is ’triggered complexity’ initiated by the speakerthat involve puns; the second is ’latent complexity’ in which no pun or vagueness is intended. The
Chinese Wordnet’s model focuses only on latent complexity.

A Multilingual Lexico-Semantic Database and Ontology

251

guished between two types: sense and meaning facet (Ahrens et al., 1998). Thesecan be distinguished as follows: given multiple possible meanings of a lemma, if
a sentence that allows co-existing multiple readings for that lemma can be found,the distinction of these meanings are recognized as meaning facet distinction, otherwise they are sense distinctions. The co-existence test for sense/meaning facet
distinction can be illustrated in (1–4). The lemma kànbı̀ng “seeing-sickness” in (1)allows two readings (“seeing the doctor” or “examining the patient”). The ambiguity
can be resolved given more contextual information and we can not find a sentencethat allows the co-existence of these two readings. Therefore, it is treated as twosenses of that lemma. However, for the lemma zázhı̀ “magazine”, it can refer to thephysical object in (2), or the information contained in (3), more
specifically, we can find a sentence like (4) in which the meaning of the lemma
can refer to both the physical object and the information contained inthat object. We therefore consider this meaning distinction of zazhi “magazine” is ameaning facet rather than a sense. Interestingly, among the 5,890 meaning facets
being identified in Chinese Wordnet, 9 regular systematic patterns are extracted,
which are similar to the regular polysemy (Apresjan, 1973) (of complex types) proposed by Pustejovsky (1995). This fine grained distinction is implemented by extending the types of semantic relations within the Chinese wordnet. Many (perhapsmost) of these relations are not specific to Chinese. One of the advantages of the
OMW is that we can look at research like this being done for one language, andeasily test its applicability to other languages.
(1) 他 正在
看病tā zhèngzài kànbı̀ng
he PROG seeing-sickness’He is seeing the doctor./He is examining the patient’(2) 他 手 上
拿 了 本 雜誌tā shǒu shàng ná le běn zázhı̀
he hand on
hold asp. CL magazine’He is holding one magazine in his hand.’(3) 他 在讀 那 一 本 雜誌
dú nà yī běn zázhı̀.
tā zàihe PROG read that one CL magazine.
’He is turning the pages of the magazine and reading it.’(4) 他 拿 一 本 雜誌
給 我 看tā ná yī běn zázhı̀
gěi wǒ kàn
he takes one CL magazine give me read’He passed me a magazine (to read).’252

A Multilingual Lexico-Semantic Database and Ontology

5 Extending the Multilingual Wordnet
In this section we discuss the immediate plans to extend the wordnets to deal withmultilingual issues. As was demonstrated in EWN, we can expect most languages
to have concepts that are not lexicalized in English. In addition, there are still many
concepts lexicalized in English, but not in PWN. Thus different wordnets will havesynsets that do not appear in most or even any other existing wordnet (this was thecase for seven of the wordnets in the OMW). Consider the example of the Tagalog
word hilamos – to wash one’s face (Borra et al., 2010).
Words such as this form part of the motivation for using a formal ontology. Whilesome wordnets have used English as an interlingua and created phrases to stand in
the place of otherwise unlexicalized concepts, another approach is to use SUMO as
an interlingua which can contain concepts which stand for the lexicalized conceptsof any particular language.
Exactly what counts as lexicalized can be hard to determine. Consider the following example: foal is lexicalized in English so must be in the English Wordnet.
In Malay, the closest equivalent is a phrase: anak kuda “horse child” which can beproduced compositionally by fully productive syntactic rules. In Japanese it is kouma “child+horse” a word produced by a semi-productive process. So it is not clear
whether the Malay wordnet should have an entry here. On the one hand, it is produced by a fully productive process. On the other, it is useful to have an entry, evenif fully compositional, for completeness. We suggest that it should be entered but
marked as syntagmatic using meta-data, following the example of Italian, Basqueand Hungarian wordnets (Pianta et al., 2002; Pociello et al., 2011). Vincze & Almázi(2014) show how it is possible to exploit this meta-data to automatically make two
versions of the monolingual wordnets — one showing translation equivalents andone only showing concepts lexicalized in particular language.
EWN distinguished a few types of non-universal lexicalisations and expressions,which call for different methods of handling:
cultural concepts concepts that exist in some cultures and not in others, e.g. Dutch
klunen=to walk on skates
pragmatic lexicalisations concepts that are known in all/most cultures but are notconsidered lexicalised in all of these, e.g. we all know the concept of a small fish
but Spanish happens to have a separate word for it alevinmorpho-syntactic mismatches concepts that are lexicalised through words withdifferent morpho-syntatic properties across languages, e.g. Dutch has no equivalence for like but uses the adjective aardig
differences in perspective some languages distinguish things depending on who
is doing what to whom in ways that other languages don’t, e.g. teach and learn
in English whereas French uses apprendre for both.
A pertinent question is what defines a word and what defines a concept. Commonly occurring collocations may have transparent, compositional semantics, yet
we may still consider these words. For example, noun compounds such as sailingA Multilingual Lexico-Semantic Database and Ontology

253boat are so common and ready made that we consider them to be one word. Another point is that the relation between the components cannot be predicted fromthe structure: who is doing the sailing, who has the sail and what is being sailed?
A classical Dutch example is kindermeel: meal for children and tarwemeel: flour
made of oats. From the structure, we cannot infer the relation. It needs to be learned
or inferred but Dutch speakers are probably not deriving them over and over again.
We are also extending the wordnets in terms of their size and coverage bothwithin individual projects and by exploiting the disambiguating power of multilingual data to link to other open resources such as Wiktionary (Bond & Foster,2013). The core idea is that by looking at multiple translations of a concept, we canpin-point the meaning exactly: bat in English is ambiguous between the sporting
equipment and the flying mammal, but adding, e.g. French removes the ambiguity(batte vs chauve-souris).
We are investigating two (compatible) methods of dealing with these new concepts. One is to create a concept in an external ontology and use this to link languages. In this approach, as hilamos is not lexicalized in English, it is not linked
directly to English wash in the English wordnet. The fundamental value of the ontology is to define meaning using axioms in an expressive logic so that the meanings
can then be manipulated without recourse to a human’s intuition about the meaningof a word.
The second approach is to have a shared group of synsets for all languages, but
not have them lexicalized in all languages. In this model English wash and Filipino
hugas are both lexicalizations of the same synset, and the synset for hilamos “washone’s face” inherits from this, but would be marked as unlexicalized in English.
Most expand style wordnets take this approach with non-lexicalized synsets being
either just left blank, or explicitly marked as non-lexicalized (as in, for example theMCR (Gonzalez-Agirre et al., 2012)).

5.1 Wordnets linked to external Ontologies
Using ontologies18 to link words (the first approach) is more labor intensive, butoffers other advantages.
Consider the notion of earlier. PWN has a synset for this word, but not a way touse it in temporal inference. SUMO however has a relation for earlier, and a formal
rule (among others) that allows an automated inference system such as those available with Sigma (Pease & Benzmüller, 2013; Pease et al., 2010) to conclude thatan interval that is earlier than another has an endpoint that precedes the start pointof the following interval. This is a necessary and sufficient definition for earlier
and uses the bi-implication or equivalence sign <=>.
18 It would be possible to link ontologies other than SUMO. There are other ontologies with atleast partial links to wordnet, including DOLCE (Gangemi et al., 2003) and the Kyoto Ontology(Laparra et al., 2012). We only discuss SUMO here, as it is both the largest ontology and the mostfully integrated with the OMW.

254

A Multilingual Lexico-Semantic Database and Ontology(<=>(earlier ?INTERVAL1 ?INTERVAL2)(before(EndFn ?INTERVAL1)(BeginFn ?INTERVAL2)))
Another example is the SUMO-based content developed to represent Muslim
cultural concepts in Arabic Wordnet (Black et al., 2006). The Udhiyah ritual is
performed during the period of Eid-Aladha and involves slaughtering a lamb by aMuslim. If a lamb has the attribute of being Udhiyah then there necessarily exists
an UdhiyahRitual in which it is the subject of the ritual.
(=>(instance ?UR UdhiyahRitual)(exists (?S ?EA ?P)(and(instance ?EA EidAladha)(during ?UR ?EA)
(attribute ?S Udhiyah)(agent ?UR ?P)(attribute ?P Muslim)(patient ?UR ?S))))(=>(attribute ?S Udhiyah)(exists (?UR)(and(instance ?S Lamb)(instance ?UR UdhiyahRitual)(patient ?UR ?S))))

Each of these symbols is further formalized, allowing them to be checked forlogical consistency by automated theorem provers. This is also a key advantage forformal logic representation. The more expressive the representation, and the more
extensive the set of formalizations for each concept, the more things that can bechecked automatically. A conventional dictionary must be checked by humans toensure correctness of definitions. This is true with a conventional data dictionary, inwhich concepts in a database are defined in natural language in hopes of ensuring
their correct usage. But when such a corpus of definitions grows large, into thethousands or more, it is not likely that a human or even many humans will be able tofind all inconsistencies. Automated means are needed. At that point, expressivenessalso matters. In a taxonomy, the only error that can be caught automatically is thepresence of a cycle in the graph. With a description logic, many more checks can be
performed. In a higher-order language such as that used by SUMO, theorem proving(Benzmüller & Pease, 2010) can find much more deep and subtle errors, leading todefinitions of considerable depth and consistency.
Because SUMO terms are mathematical symbols, with a semantics given solely
by their logical axioms, and unlike taxonomies or semantic networks, the symbol
names can be changed without altering their meaning. In fact, the current Sigmabrowser can display terms with their names in different languages, in order to emphasize this point, and make them more accessible to logicians who may not speak
English.

A Multilingual Lexico-Semantic Database and Ontology

255

5.2 InterLingual Index
The second approach is basically that of the Interlingual Index (ILI: Peters et al.,1998). The variety of approaches in the EWN initially resulted in wordnets thatwere mapped to very different sets of concepts in the ILI. Likewise, only a small
set of synsets could be traced to other languages through the ILI. To harmonize theoutput, EWN took two measures: (i) the definition of a shared set of (1,000 up to
5,000) Base Concepts that were manually aligned, and (ii) the classification of these
Base Concepts using a small top-ontology of 63 terms. Base Concepts (not to be
confused with the ”Basic Level Categories” of Rosch (1978)) represent synsets thathave the highest connectivity to the other synsets. The top-ontology classification ofthese synsets provided a shared semantic framework. Each wordnet made sure theBase Concepts were presented properly in their language and manually mapped to
the ILI. The minimal intersection across these wordnets through the ILI is thus theset of Base Concepts but in practice the intersection is much larger. During the EWN
project, it became clear that there are many problems with the ILI being based on
PWN and that there are many possibilities to improve the ILI for linking wordnets(Vossen et al., 1999).

6 Conclusion
Several goals are being pursued in parallel: (i) research on building wordnets forindividual languages; (ii) research on building a more formal upper ontology; (iii)
research on linking wordnets in many languages to make a multilingual resource.
The ontology as well as some of the lexicons have been expressed in OWL, as wellas their original formats, for use on the semantic web and in linked data. This effort
builds on WordNet, Global Wordnet, and SUMO to create a rich web of linguistic
data and mathematically specified world knowledge.

References
Ahrens, K., Chang, L. L., Chen, K. J., & Huang, C.-R. (1998). Meaning representation and meaning instantiation for Chinese nominals. In Int. J. Computational
Linguistics and Chinese Language Processing, vol. 3, (pp. 45–60).
Apresjan, J. (1973). Regular polysemy. Linguistics, 142(5), 5–32.
Benzmüller, C., & Pease, A. (2010). Progress in automating higher-order ontology
reasoning. In B. Konev, R. Schmidt, & S. Schulz (Eds.) Workshop on Practical
Aspects of Automated Reasoning (PAAR-2010). Edinburgh, UK: CEUR Workshop Proceedings.
Berners-Lee, T. (2009). Linked data-the story so far. International Journal on
Semantic Web and Information Systems, 5(3), 1–22.

256

A Multilingual Lexico-Semantic Database and Ontology

Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python.
O’Reilly. www.nltk.org/book.
Bird, S., Klein, E., & Loper, E. (2010). Nyumon Shizen Gengo Shori [Introduction
to Natural Language Processing]. O’Reilly. (translated by Hagiwara, Nakamura
and Mizuno).
Black, W., Elkateb, S., Rodriguez, H., Alkhalifa, M., Vossen, P., Pease, A., & Fellbaum, C. (2006). Introducing the Arabic WordNet Project. In Proceedings of theThird International WordNet Conference. Choi, Fellbaum and Vossen: Sojka.
Bond, F., & Foster, R. (2013). Linking and extending an open multilingual wordnet.
In 51st Annual Meeting of the Association for Computational Linguistics: ACL2013, (pp. 1352–1362). Sofia.
URL http://aclweb.org/anthology/P13-1133
Bond, F., Isahara, H., Fujita, S., Uchimoto, K., Kuribayashi, T., & Kanzaki, K.
(2009). Enhancing the Japanese WordNet. In The 7th Workshop on Asian Language Resources, (pp. 1–8). Singapore: ACL-IJCNLP 2009.
Bond, F., & Paik, K. (2012). A survey of wordnets and their licenses. In Proceedingsof the 6th Global WordNet Conference (GWC 2012). Matsue. 64–71.
Borra, A., Pease, A., Roxas, R., & Dita, S. (2010). Introducing Filipino WordNet. In
P. Bhattacharyya, C. Fellbaum, & P. Vossen (Eds.) Principles of Construction andApplication of Multilingual Wordnets: Proceedings of the 5th Global WordNet
Conference, (pp. 306–310). Mumbai, India: Narosa pub.
Boyd-Graber, J., Fellbaum, C., Osherson, D., & Schapire, R. (2006). Adding dense,
weighted connections to WordNet. In Proceedings of the Third Global WordNet
Meeting. Jeju.
Burnard, L. (2000). The British National Corpus Users Reference Guide. Oxford
University Computing Services.
Daude, J., Padro, L., & Rigau, G. (2003). Validation and tuning of Wordnet mapping
techniques. In Proceedings of the International Conference on Recent Advancesin Natural Language Processing (RANLP’03). Borovets, Bulgaria.
de Melo, G., Suchanek, F., & Pease, A. (2008). Integrating YAGO into the Suggested Upper Merged Ontology. Proceedings of the 20th IEEE International
Conference on Tools with Artificial Intelligence.
de Paiva, V., & Rademaker, A. (2012). Revisiting a Brazilian wordnet. In Proceedings of the 6th Global WordNet Conference (GWC 2012). Matsue.
Fellbaum, C. (Ed.) (1998). WordNet: An Electronic Lexical Database. MIT Press.
Fellbaum, C., & Vossen, P. (2012). Challenges for a multilingual wordnet. Language
Resources and Evaluation, 46(2), 313–326. Doi=10.1007/s10579-012-9186-z.
Gangemi, A., Guarino, N., Masolo, C., & Oltramari, A. (2003). Sweetening WordNet with DOLCE. AI Magazine, 24(3), 13–24.
Genesereth, M. (1991). Knowledge interchange format. In J. Allen, R. Fikes, &E. Sandewall (Eds.) Proceedings of the Second International Conference on the
Principles of Knowledge Representation and Reasoning, (pp. 238–249). Morgan
Kaufman.

A Multilingual Lexico-Semantic Database and Ontology

257

Gonzalez-Agirre, A., Laparra, E., & Rigau, G. (2012). Multilingual central repository version 3.0: upgrading a very large lexical knowledge base. In Proceedingsof the 6th Global WordNet Conference (GWC 2012). Matsue.
Huang, C.-R., Hsieh, S.-K., Hong, J.-F., Chen, Y.-Z., Su, I.-L., Chen, Y.-X., &Huang, S.-W. (2010). Chinese wordnet: Design and implementation of a crosslingual knowledge processing infrastructure. Journal of Chinese Information Processing, 24(2), 14–23. (in Chinese).
Isahara, H., Bond, F., Uchimoto, K., Utiyama, M., & Kanzaki, K. (2008). Development of the Japanese WordNet. In Sixth International conference on Language
Resources and Evaluation (LREC 2008). Marrakech.
Koide, S., Morita, T., Yamaguchi, T., Muljadi, H., & Takeda, H. (2006). OWL
expressions on WordNet and EDR. In AI society Semantic Web Ontology SIG 13,
SIG-SWO-A601-03. (in Japanese).
URL
http://www.jaist.ac.jp/ks/labs/kbs-lab/sig-swo/
fpapers.%htm
Kunze, C., & Lemnitzer, L. (2002). Germanet — representation, visualization, application. In LREC, (pp. 1485–1491).
Laparra, E., Rigau, G., & Vossen, P. (2012). Mapping wordnet to the kyoto ontology.
In N. Calzolari, K. Choukri, T. Declerck, M. U. Dogan, B. Maegaard, J. Mariani,
J. Odijk, & S. Piperidis (Eds.) Proceedings of the 8th international conference on
Language Resources and Evaluation (LREC2012), (pp. 2584–2589). Publ. European Language Resources Association (ELRA).
Lindén, K., & Carlson., L. (2010). Finnwordnet — wordnet påfinska viaöversättning. LexicoNordica — Nordic Journal of Lexicography, 17, 119–140.
In Swedish with an English abstract.
McCrae, J., Spohr, D., & Cimiano, P. (2011). Linking lexical resources and ontologies on the semantic web with lemon. In The Semantic Web: Research andApplications, (pp. 245–259).
Mohamed Noor, N., Sapuan, S., & Bond, F. (2011). Creating the open Wordnet
Bahasa. In Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation (PACLIC 25), (pp. 258–267). Singapore.
Montazery, M., & Faili, H. (2010). Automatic Persian wordnet construction. In
23rd International conference on computational linguistics, (pp. 846–850).
Niles, I., & Pease, A. (2001). Toward a Standard Upper Ontology. In C. Welty,& B. Smith (Eds.) Proceedings of the 2nd International Conference on Formal
Ontology in Information Systems (FOIS-2001), (pp. 2–9).
Niles, I., & Pease, A. (2003). Linking Lexicons and Ontologies: Mapping WordNet
to the Suggested Upper Merged Ontology. In Proceedings of the IEEE International Conference on Information and Knowledge Engineering, (pp. 412–416).
Ordan, N., & Wintner, S. (2007). Hebrew wordnet: a test case of aligning lexical
databases across languages. International Journal of Translation, 19(1), 39–58.
Pease, A. (2006). Formal representation of concepts: The Suggested Upper Merged
Ontology and its use in linguistics. In Ontolinguistics: How Ontological Status
Shapes the Linguistic Coding of Concepts. New York: Mouton de Gruyter.

258

A Multilingual Lexico-Semantic Database and Ontology

Pease, A. (2011). Ontology: A Practical Guide. Angwin, CA: Articulate Software
Press.
Pease, A., & Benzmüller, C. (2013). Sigma: An Integrated Development Environment for Logical Theories. AI Communications, 26, 9–97.
Pease, A., Fellbaum, C., & Vossen, P. (2008). Building the Global WordNet Grid.
In Proceedings of the CIL-18 Workshop on Linguistic Studies of Ontology. Seoul,
South Korea.
Pease, A., Sutcliffe, G., Siegel, N., & Trac, S. (2010). Large Theory Reasoning
with SUMO at CASC. AI Communications, Special issue on Practical Aspects ofAutomated Reasoning, 23(2-3), 137–144.
Pedersen, B., Nimb, S., Asmussen, J., Sørensen, N., Trap-Jensen, L., & Lorentzen,
H. (2009). DanNet — the challenge of compiling a wordnet for Danish by reusing
a monolingual dictionary. Language Resources and Evaluation.
Peters, W., Vossen, P., Dı́ez-Orzas, P., & Adriens, G. (1998). Cross-linguistic alignment of wordnets with an inter-lingual-index. In Vossen (1998), (pp. 149–251).
Pianta, E., Bentivogli, L., & Girardi, C. (2002). Multiwordnet: Developing analigned multilingual database. In In Proceedings of the First International Conference on Global WordNet, (pp. 293–302). Mysore, India.
Piasecki, M., Szpakowicz, S., & Broda, B. (2009). A Wordnet from the Ground Up.
Wroclaw University of Technology Press. (ISBN 978-83-7493-476-3).
URL
http://www.plwordnet.pwr.wroc.pl/main/content/
files/pub%lications/A_Wordnet_from_the_Ground_Up.pdf
Pociello, E., Agirre, E., & Aldezabal, I. (2011). Methodology and construction of
the Basque wordnet. Language Resources and Evaluation, 45(2), 121–142.
Pustejovsky, J. (1995). The Generative Lexicon. Cambridge, MA: MIT Press.
Rosch, E. (1978). Principles of categorization. In E. Rosch, & B. B. Lloyd (Eds.)
Cognition and Categorization, (pp. 27–48). Hillsdale (NJ), USA: Lawrence Erlbaum Associates. Reprinted in Readings in Cognitive Science. A Perspectivefrom Psychology and Artificial Intelligence, A. Collins and E.E. Smith, editors,
Morgan Kaufmann Publishers, Los Altos (CA), USA, 1991.
Ruci, E. (2008).
On the current state of Albanet and related applications.
Tech. rep., University of Vlora.
(http://fjalnet.com/
technicalreportalbanet.pdf).
Sagot, B., & Fišer, D. (2008). Building a free French wordnet from multilingual
resources. In E. L. R. A. (ELRA) (Ed.) Proceedings of the Sixth International
Language Resources and Evaluation (LREC’08). Marrakech, Morocco.
Savas, B., Hayashi, Y., Monachini, M., Soria, C., & Calzolari, N. (2010). An lmfbased web service for accessing wordnet-type semantic lexicons. In N. C. C.
Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.) Proceedings of the Seventh International Conference on
Language Resources and Evaluation (LREC’10). Valletta, Malta: European Language Resources Association (ELRA).
Thoongsup, S., Charoenporn, T., Robkop, K., Sinthurahat, T., Mokarat, C., Sornlertlamvanich, V., & Isahara, H. (2009). Thai wordnet construction. In Proceedings of The 7th Workshop on Asian Language Resources (ALR7), Joint confer-

A Multilingual Lexico-Semantic Database and Ontology

259

ence of the 47th Annual Meeting of the Association for Computational Linguistics(ACL) and the 4th International Joint Conference on Natural Language Processing (IJCNLP),. Suntec, Singapore.
van Assem, M., Gangemi, A., & Schreiber, G. (2006). Conversion of wordnet toa standard RDF/OWL representation. In Proceedings of The fifth international
conference on Language Resources and Evaluation (LREC 2006).
Vincze, V., & Almázi, A. (2014). Non-lexicalized concepts in wordnets: A casestudy of English and Hungarian. In Proceedings of the 7th Global WordNet Conference (GWC 2014), (pp. 118–126). Tartu.
Vossen, P. (Ed.) (1998). Euro WordNet. Kluwer.
Vossen, P., Maks, I., Segers, R., & Van der Vliet, H. (2008). Integrating lexicalunits, synsets and ontology in the Cornetto database. In LREC 2008. Marrakech,
Morocco: European Language Resources Association (ELRA).
Vossen, P., Peters, W., & Gonzalo, J. (1999). Towards a universal index of meaning.
In Proceedings of ACL-99 Workshop, Siglex-99, Standardizing Lexical Resources,(pp. 81–90). Maryland.
Vossen, P., & Postma, M. (2014). Open Dutch wordnet. In Proceedings of the 7th
Global WordNet Conference (GWC 2014). Tartu. (presentation only).
Vossen, P., & Rigau, G. (2010). Division of semantic labor in the global wordnet
grid. In P. Bhattacharyya, C. Fellbaum, & P. Vossen (Eds.) 5th Global Wordnet
Conference: GWC-2010. Mumbai: Narosa Pub.
Vossen, P., Soria, C., & Monachini, M. (2013). LMF - lexical markup framework.
In G. Francopoulo (Ed.) LMF - Lexical Markup Framework, chap. 4. ISTE Ltd +John Wiley & sons, Inc.
Wang, S., & Bond, F. (2013). Building a Chinese wordnet: Starting from core
synsets. In Proceedings of the 11th Workshop on Asian Language Resources.
Nagoya.
Yoon, A., Hwang, S., Lee, E., & Kwon, H.-C. (2009). Construction of Korean
wordnet KorLex 1.5. Journal of KIISE: Software and Applications, 36(1), 92–108.