Indowordnet and Its Linking with Ontology

IndoWordNet and its Linking with Ontology

Brijesh Bhatt Pushpak Bhattacharyya Center For Indian Language Technology, Center For Indian Language Technology, Department of Computer Science and Engineering, Department of Computer Science and Engineering, Indian Institute of Technology, Mumbai Indian Institute of Technology, Mumbai [email protected] [email protected]

Abstract hierarchical structure of concepts related by subsumption relation, which can be shared between Reasoning about natural language requires applications. Though WordNet is often considered combining semantically rich lexical re- as an ontology (the synset corresponds to concept sources with world knowledge, provided and ‘hypernymy-hyponymy’ relation is similar to by ontologies. In this paper, we describe subsumption), it is a language specific resource linking of WordNets of Indian languages which may vary from language to language. The with an upper ontology SUMO (Sug- ontological issues in WordNet discussed in (pease gested Upper Merged Ontology). This and Fellbaum, 2010; Gangemi et al., 2003; Niles creates multilingual resource for Indian and Pease, 2003) are as follows, languages which can be used in various natural language processing applications. This paper presents the architecture of 1. Confusion between concept and individual: IndoWordNet- Linking of WordNets of WordNet synsets do not distinguish between seventeen different Indian languages and concept and individual. For example, both provides a method to link it with upper on- ‘Martial Art’ and ‘Karate’ are considered as tology SUMO. Two different systems: In- concept. doWordNet navigator and SIGMAKEE interface for Indian languages are developed 2. Confusion between object level and meta to access this resource. level concept: WordNet covers both object level and meta level concepts as hyponymy 1 Introduction of same concept. For example, concept ‘Ab- WordNet (Fellbaum, 1998) has emerged as a great straction’ includes both object level concept resource for the Natural Language Processing ap- ‘Time’ and meta level concept ‘Attribute’. plications. Following English WordNet, Word- Nets are built for many languages of the world. 3. Heterogeneous level of generality: Two hy- Hindi WordNet (D. Narayan and Bhattacharyya, ponyms of a concept may represent different 2002) is the first WordNet built for an Indian lan- level of generality. For example, as a hy- guage. WordNets for other 16 Indian languages ponymy of concept ‘Animal’, there is a gen- are being built from Hindi WordNet using expan- eral concept ‘chordate’ and a more specific sion approach (Vossen, 1998). Linking of all these concept ‘Work Animal’. WordNets provides rich knowledge base for the Indian languages, which can be useful for infor- 4. Lexical gap: A language may not have an in- mation extraction, retrieval and many other natural digenous lexeme to describe a concept. For language processing applications. example, vehicles can be divided into two classes, 1) Vehicles that run on the road and 1.1 WordNet and ontology 2) Vehicles that run on the rail, but English Ontology is defined as “Explicit specification of language does not have specific words to de- conceptualization”(Gruber, 1993). Ontology is a scribe these classes.

“Proceedings of ICON-2011: 9th International Conference on Natural Language Processing, Macmillan Publishers, India. Also accessible from http://ltrc.iiit.ac.in/proceedings/ICON-2011” 1.2 Benefits of linking WordNet with and (17) Urdu. Together these languages repre- ontology sents three language families: Indo-Aryan, Dra- By keeping ontological relations in the formal on- vidian and Tibeto-Barman. The comparative study tology and linguistic relations in the lexicon, one of IndoWordNet with EuroWorNet is presented in can avoid merging two different levels of analy- (Bhattacharyya, 2010) sis and can capture the information needed about 2.1 Synset Categorization formal concepts and linguistic tokens (pease and Fellbaum, 2010). Linking of WordNet with on- Synsets of the Hindi WordNet are used as basis tology allows the language independent semantic to create synsets of WordNets of other languages. relations of ontology to be used for inferencing on As a synset is represented by a gloss and a set language specific words. The benefits of linking of words of a particular language, in many cases, ontology and WordNet are as follows (Niles and the synset representation of a concept may vary Pease, 2003): in sense across the languages. Also there exist some concepts for which there may not be words 1. The formal specifications of the ontology can in all the languages. For example, Kashmiri lan- be used with the WordNet in the sense that guage does not have words for the concepts like the axioms corresponding to the words can g}h (‘graaha’, Planet), som (‘som’), m\gl (‘man- be retrieved from the ontology. gal’). Kinship terms also vary across Indian languages. To handle this concept divergence across 2. The formal axioms of ontology can be used languages, synsets are divided into different cate- with natural language text. gories. Table 1 describes these categories. Such classification of synsets helps in linking 3. Linking ontology concepts with WordNet can concepts of different languages. For example, if be used to check completeness of ontology. a synset belongs to the universal synset then it is 4. Concepts of different languages can be com- present in both Hindi and English. And if a synset pared and linked using ontology. belongs to the Pan-Indian category then it belongs to both Hindi and Gujarati. Thus, WordNet devel- 5. WordNet concepts can be refined and restruc- opment using expansion approach will be faster by tured using ontology. this method. This classification also helps in cross lingual information access. By identifying the cat- 6. Different domain ontologies can be linked us- egory of a synset, its presence in another language ing WordNet concepts. can be easily predicted. Till date, 7163 universal synsets and 1356 Pan- 1.3 Organization of the paper Indian synsets have been identified and are now The remaining of the paper is organized as fol- linked across all languages. Language specific lows: Section 2 describes the IndoWordNet. The synsets are being developed and later they will be selection criteria for upper ontology and a compar- linked by translating them into Hindi and English. ative study of upper ontology is given in Section 3 and 4. Section 5 describes the method to link In- 3 Survey of upper ontology doWordNet with ontology and interfaces designed Ontologies are categorized into three different to access the system. Observations and conclu- types according to their level of generality (Guar- sions are discussed in section 6 and 7 respectively. ino, 1998). Top level/Upper ontology, Domain 2 IndoWordNet specific ontology and application specific ontology. Upper ontology defines very general con- Seeing the enormous potential of WordNet, 17 cepts independent of application or domain. Up- out of 22 official languages of India have started per ontologies are useful in linking and devel- developing WordNets. These languages are: (1) opment of more specific domain/application on- Hindi (2) Marathi (3) Konkani (4) Sanskrit (5) tology. There are various upper ontologies like Nepali (6) Kashimiri (7) Assamese (8) Tamil (9) SUMO, DOLCE, CYC etc. The ontological Malyalam (10) Telugu (11) Kannad (12) Manipuri choices for designing upper ontologies, discussed (13) Bodo (14) Bangla (15) Punjabi (16) Gujarati in (Oberle et al., 2007), are as follows, Table 2: Comparison of Upper ontologies Case SUMO DOLCE Open CYC License Open Open Free Table 1: Synset categories in IndoWordNet subset Category Description of Cyc Universal These concepts appear in all the Modularity Yes No Yes Language KIF, OWL Cyc languages. These concepts are es- OWL sential and most frequently used. Multiplicative(M)- -MM For example, s y (‘soorya’, sun) Reductionist(R) Descriptive(D)- DDD Pan-Indian Concepts common to Indian lan- prescriptive(Pr) guages and linkable across all In- Endurant(E)- No Yes Yes dian languages but does not have Perdurant(Pd) parallel concepts in English. For Universal(U)- Both Pt Both Particular(Pt) example, tºlA (‘tabalaa’, An In- Linking to Word- Full Partial Partial dian rhythm instrument) Net

In-Family These concepts are common in specific subset of Indian lan- 1. Endurant-Perdurant : An endurant is an en- guages and linkable across all lan- tity which exists in full in every instant at guages of the family. For example, which it exists at all. A perdurant “unfolds ÊAÊA (‘chacha’ ,paternal uncle), itself over time in successive temporal parts BEtjA (‘bhatija’, brother’s son) or phases.” Both endurants and perdurants are taken to be concrete particulars, i.e., in- Language These are the concepts specific to stances. Specific a culture or a language. These may include local food, festivals, 2. Descriptive-prescriptive :A descriptive on- etc. For example, Eºh (‘bihu’ , tology tries to capture more commonsense Name of a festival celebrated in and social notions based on natural language Assam state of India) word is very usage and human cognition. Concepts are specific to the state and culture and divided into things and events. A prescrip- does not appear in any other lan- tive ontology emphasizes upon the scientific guage. and philosophical perspectives. All the con- structs in revisionary ontology are space-time Rare This includes very specific con- objects. cepts adopted in most of the lan- multiplicative-reductionist multiplicative guages. Specific scientific terms 3. : In like ‘ngram’ belongs to this cate- upper ontology concepts can include any- gory thing that reality seems to require. Contrar- ily, reductionist ontology reduces the number Synthesized Synset created in a language due of concepts to the fewest primitives sufficient to influence of another language. to derive the rest of the complex reality. These synsets are not natural to 4. Universal-particular : Universals are the en- the language but required to link tities that have instances, while particulars WordNets of two different lan- are entities that do not have instances. guages. A brief comparison of ontologies based on these choices and other parameters such as availability of system, support for domain specific ontology and linking with WordNet etc.is shown in table 2 Table 3: IndoWordNet Index iwnindex(IWNID, POS, WNLINKTYPE, EN- GID,SUMOTERM, SUMOLINKTYPE)

IWNID : IndoWordNet synset ID POS : Part of speech of the synset WNLINKTYPE : type of link through which Hindi and English synsets are connected. It can be either direct or Hyponymy Figure 1: IndoWordNet architecture ENGID : English sense Id of the corresponding synset SUMOTERM : SUMO term associated to concept SUMOLINKTYPE : Type of link through which synset is the Fig 1. connected to SUMO term. It can be any of the direct, As discussed in section 2, synsets are divided subsumption or instance. into six categories: Universal, Pan-Indian, In- Family, Language specific, Rare and Synthetic. 4 SUMO-Suggested Upper Merged Synsets of these categories are linked through IWNID. SynsetId of the synset represents this Ontology IWNID and gives unique identification to the SUMO (Niles and Pease, 2001) is created at synset. Universal, Pan-Indian, Rare and subset Teknowledge Corporation, by merging publicly of In-family synsets for each language are devel- available ontological content into a single, com- oped from corresponding Hindi synsets, as these prehensive, and cohesive structure. The knowl- synsets are also present in Hindi WordNet. There- edge representation language for the SUMO is fore, IDs assigned to these synsets are synsetIDs SUO-KIF. SUMO is the largest freely available of Hindi. For some of the in-family synsets and ontology which is linked to entire English Word- language specific synsets which are not present in Net. A mid-level ontology MILO (Niles and Terry, Hindi, separate ID ranges are decided for each lan- 2004) and many domain ontologies for the variety guage group and these synsets are translated into of domains like Communication, Countries and re- Hindi and English language (Though there may gion, Distributed Computing, Finance, Military, not be a word specific to the synset). Geography, Government, etc. are constructed using SUMO. Including all these SUMO provides 5.1 Linking IndoWordNet and SUMO around 23000 terms and 123000 axioms. SUMO The approach to link synsets and the method of is supported by resolution theorem prover: Vam- using relations of Hindi WordNet to other Indian pire (Riazanov and Voronkov, 2002). SUMO is language WordNet are discussed in (Ramanand et also one of the backbone ontologies for the Global al., 2007), (Sinha et al., 2006). The semantic re- WordNet Grid (Pease et al., 2008). Open source lations defined in Hindi WordNet are inherited in project SigmaKEE(Sigma Knowledge Engineer- all other languages. Lexical relations remain with ing Environment) (Pease and Benzmuller,¨ 2010) the language specific WordNet, as they vary across which provides environment for first order logic languages. theory development is optimized for SUMO. All Once synsets of all the Indian languages are these factors make SUMO an obvious choice as linked through IWNID, linking of English Word- backbone ontology for the IndoWordNet. Net and SUMO with the IndoWordNet is done by the process shown in table 4. This process takes a 5 Implementation synset as an input and creates iwnindex by finding WordNets of Indian languages are linked to corresponding English synset ID and SUMO term. SUMO by using English WordNet. A common in- readIwnIndex : This process returns the IWN dex is designed to link all WordNets with SUMO. ID for the given input word. The common index- iwnIndex (shown in table 3) FindEnglishSynset : A semi automated ap- is defined as five tuple, which unambiguously clas- proach (Saraswati et al., 2010) is used to link sifies the concept. IWNID to EnglishID. It searches most appropriate The overall system architecture is as shown in English synset for the input Hindi Synset. If there For example, Hindi concept Table 4: SUMO linking process readIwnIndex; ‘f B’, ‘vh jo aQCA ho’ findEnglishSynset ; (‘shubha’, vaha jo achchhaa ho’) findSumoConcept; (auspicious, something that good is) findSumoMapType; linkSumo; (auspicious, something that is good) has direct equivalent relation with English concept ‘Auspicious’, ’auguring favorable are multiple English synsets found for the given circumstances and good luck’ input Hindi Synset, the best synset is selected by manual verification. As this is a subjective term, it does not Two types of semantic links are used to link have any direct concept mapped to SUMO, Hindi synset with an English synset. 1. Direct but SUMO defines more general concept linkage: There exists an identical concept in En- ‘SubjectiveAssessmentAttribute’ to link such glish WordNet for the given Hindi concept. 2. Hy- terms with ontology. pernymy linkage: If there is no identical concept 3. Instance mapping: The Hindi synset is linked for the given Hindi concept, then Hindi concept to the English synset either through direct is linked to the English concept which is in turn or hypernymy link and the English synset is linked with nearest hypernymy of the Hindi con- linked to the SUMO term through instance cept. relation. FindSUMOConcept: The complete English For example, Hindi concept WordNet is linked with SUMO. (Niles and Pease, 2003). Once the IWNID is mapped to English ID, ‘EmT n rAEf’, ‘ºArAh rAEfyo m tFsrF the SUMO term is linked to form index. rAEf’ linkSUMO: This process identifies the semantic (‘mithuna raashi’ , ‘baaraaha raashiyon type to link Hindi synset with SUMO term. There mein tIsarI raashi’) are three types of links defined, (Niles and Pease, (gemini , twelve zodiacs in third zodiac) 2003), to link a synset with the SUMO term. (gemini , third of the twelve zodiacs) 1. Direct mapping: The Hindi synset is linked to has direct equivalent relation with English the English synset through direct link and the concept ‘Gemini’,’The third sign of the zo- English synset is linked to the SUMO term diac’ through equivalent link. It is mapped with the SUMO term ‘Astronom- For example, Hindi concept icalBody’ using instance relation. ‘aAfA’,‘mn kA yh BAv Ek am k kAy ho jAegA’ 4. Complement mapping: WordNet defines concepts which are opposite to each other. Many (’aasha’, mana ka vaha bhava ki aamuka times SUMO does not have concept for karya ho jaayenga) both the senses. In that case the synset is (hope, mind’s that feeling some work fulfills) linked with SUMO by complement relation. (hope, feeling of mind that some work fulfills) For example, synset corresponding to the Hindi word ‘GoEqt’ (‘ghoshit’) is mapped has equivalent synset in English ‘Hope’, to SUMO term ‘stating’ using subsump- ‘General feeling that some desire will be ful- tion mapping and its opposite ‘aGoEqt’ filled’ (‘aghoshit’) is linked with same SUMO term SUMO also has an equivalent concept ‘stating’ using complemented subsumption ‘Hope’. mapping.

2. Subsumption mapping: The Hindi synset is The following symbols are used to identify linked through hypernymy to the English link type: Direct(‘=’), Subsumption(‘+’), In- synset or English synset is linked to the stance(‘@’). Complements of these relations are SUMO term through subsumption link. identiﬁed by symbols ‘[’, ‘]’, ‘:’, respectively. Figure 2: IndoWordNet browser

2. The interface can be browsed in 17 different Table 5: iwnindex file: output of linking process Indian languages 100, adj, Direct, 00935500, hasSkill, + 10980, adv, Direct, 00051848, ShapeAt- 3. It provides a comparative view of the synsets tribute, + of different languages 10035, noun, Direct, 06123363, FieldOfS- tudy, @ 4. Relations between synsets can be explored in 2534, verb, Direct, 02706046, Selecting, [ all the languages SIGMA knowledge Engineering Environment Some entries of the iwnindex file are as shown is available as an open source project. It is used in table 5. Each row uniquely defines concept to access SUMO ontology and its mapping to En- using- Indowordnet ID, Part-of-speech, Hindi- glish WordNet. English link type, English sense Id, SUMO term An interface is created in SIGMA Knowledge and type of SUMO link. Engineering Environment to access SUMO terms corresponding to the Hindi synset. Fig 3 shows 5.2 Interface development snapshot of the system. The interface takes Hindi word as input and returns all the synsets for that Two interfaces are developed to access the In- word and its linking with SUMO. Fig 3 shows doWordNet system, IndoWordNet browser and Hindi synsets and SUMO linking for the word SUMO browser. IndoWordNet browser is a web jFvn (‘Jeevana’, life). based interface to navigate WordNets of Indian languages. It generates xml file output which can 6 Observation be useful for different natural language processing applications. All the IndoWordNet synsets and Currently, 22148 Hindi synsets are linked with the relations are stored in MySQL database and the English WordNet and SUMO. Table 6 shows the browser is designed using HTML, JSP, AJAX and status of linking. Each row refers to synsets of MySQL technology. Fig. 2 shows snapshot of the specific part of speech and column refers to type interface. of SUMO link. The basic features of the system are as follows: Using the Hindi WordNet-SUMO link the formal semantic structure and axioms of SUMO can 1. It provides easy access to all the WordNets of be used to process texts of different Indian lan- Indian languages guages. Figure 3: SUMO linking for the Hindi synsets

6.2 Restructuring WordNet concepts Table 6: Hindi synsets linked with SUMO POS Direct Subsumption Instance Compl Total Linking of IndoWordNet with SUMO can be used adjective 194 2487 310 102 3093 to restructure WordNet concept and extract taxon- adverb 14 210 5 4 233 omy from the Indian language documents. Figure noun 2336 13690 1071 19 17116 4 shows the hyponymy of the Hindi synset con- verb 231 1473 2 0 1706 taining words jFv (‘Jeev’), þAZF (‘PraNi’) linked with SUMO concept ‘organism’. SUMO classifies the concepts ‘organism’ into three different 6.1 Inferencing using SUMO categories ‘animal’, ‘plant’ and ‘micro organism’. Formal semantics of SUMO can be used for in- As shown in fig. 4, the WordNet synsets can be ferencing on natural language text using WordNet. grouped into these three categories by SUMO link. For example, if there are two statements, The SUMO concept hierarchy can be extended by referring to WordNet synsets in all of these cate- X: usgAnA ps\Ú h{ gories. This can lead to a large taxonomy of the (‘use gaanaa pasanda hai’) ‘organism’. (she singing likes) As shown in figure 4, hyponymy synsets (she likes singing) represent the heterogeneous level of generality. Y: uss\gFt ps\Ú h{ It contains synsets corresponding to terms þAZF (‘praaNI’, Animal), Em/ (‘mitra’, Friend) , p{Ú (‘use sangeet pasanda hai’) (‘peda’ , Tree), pOrAZFk p zq (‘pauraNIka pu- (she music likes) Rusha’, Mythological being), etc. SUMO link can (she likes music) be used to separate out concepts like Em/ (‘mi- then using WordNet there is no easy way to in- tra’, Friend) from the structure of organism. Hindi fer Y from X, as gAnA (‘gaanaa’, singing) and WordNet classifies jlÊr (‘jalachar’) as a sepa- s\gFt (‘sangeet’, music) are not related through rate concept. As per this classification the concept hypernymy-hyponymy relation. It requires pro- related to kml (‘kamala’ , Lotus) is under the hy- cessing gloss or to measure semantic distance to ponymy of jlÊr (‘jalachar’). So it is not related relate these two concepts. However in SUMO, to ‘plant’ or ‘flower’. With SUMO linking it can gAnA (‘gaanaa’, singing) is related to music by di- be correctly categorized under the concept ‘flow- rect subsumption relation. So inferring Y from X ering plant’. This way, IndoWordNet SUMO link- becomes easier. ing helps in restructuring WordNet and domain

SYNSET: जीव, ाणी, जीवधार, जीवामा, अनीश, सजीव, ाणधार, तनुधार, (LREC’10). European Language Resources Associ- Gloss : सजीव ाणी या वह िजसम ाण हो Example statement : "पृवी पर वभन कार के जीव पाये जाते ह" ation (ELRA). Gloss in English : a living thing that has (or can develop) the ability to act or function independently

Direct Hyponymy in Hindi Wordnet SUMO term D. Chakrabarty P. Pande D. Narayan and P. Bhat- organism 748 ाणी , जीव , जीवधार, वह जीवधार िजसम Animal tacharyya. 2002. An experience in building the indo वैिछक गत होती है "पृवी पर अनेक कार के जतु पाये invertebrate arthropod जाते ह" wordnet - a wordnet for hindi. In International Con- mollusk worm vertebrate ference on Global WordNet (GWC02), Mysore, In- cold blooded vertebrate dia. fish reptile warm blooded vertebrate Christiane Fellbaum. 1998. WordNet: An Electronic bird mammal Lexical Database. MIT Press.

715 , , , normative attribute म दोत साथी वह जो सब बात म subjective assessment सहायक और शुभचतक हो "सचे म क परा आपित-काल attribute Aldo Gangemi, Nicola Guarino, Claudio Masolo, and

म होती है" 1249 , - , organism Alessandro Oltramari. 2003. Sweetening WordNet वनपत पेड़ पौधा वह सजीव िजसम गत नहं plant होती है और अधकांशतः वह अपना भोजन वयं बनाता है flowering plant with DOLCE. AI Magazine, 24(3):13–24, sep. non flowering plant "जंगल म तरह-तरह क वनपतयाँ पायी जाती ह"

agent 1712पौराणक जीव , वह जीव िजसका वणन पुराण या धामक sentient agent T. R. Gruber. 1993. Towards principles for the design ंथ म मलता है "हेमत पौराणक जीव संबंधी कथाएँ सुनने म cognitive agent

च लेता है" of ontologies used for knowledge sharing. In Formal Plant 1554जलय जीव , जलचर जल म पाया जानेवाला या flowering plant Ontology in Conceptual Analysis and Knowledge रहनेवाला जीव (जंतु, वनपत आद) "शैवाल, कमल, मोलक आद जलय जीव ह" Representation, Deventer, The Netherlands. Kluwer organism 4490रोगाणु , वे सूम जीव जो हवा या खाने-पीने क चीज़ म microorganism Academic Publishers. मले रहते ह और अनेक कार के रोग के मूल कारण माने जाते bacterium virus ह"रोगाणु मानव के लए बहु त ह घातक होते ह"

Nicola Guarino. 1998. Formal ontology in informa- Figure 4: relation of synsets in SUMO tion systems : Proceedings of the 1st international conference june 6-8, 1998. pages 3–15. IOS Press. specific taxonomies can be extracted by using this Ian Niles and Adam Pease. 2001. Towards a stan- linking. dard upper ontology. In FOIS ’01 Proceedings of the international conference on Formal Ontology in In- formation Systems - Volume 2001, pages 2–9. ACM 7 Conclusion Press. Linking WordNets of the Indian languages and Ian Niles and Adam Pease. 2003. Linking lexicons and SUMO creates a useful resource for natural lan- ontologies: Mapping wordnet to the suggested upper guage processing applications targeted at Indian merged ontology. In Proceedings of the 2003 Inter- languages. The language independent semantics national Conference on Information and Knowledge Engineering (IKE 03), Las Vegas, pages 23–26. and formalism of SUMO ontology can be used with WordNet for various text processing applica- Ian Niles and A Terry. 2004. The milo: A general- tions. The system is made available through two purpose, mid-level ontology. In Proceedings of the International Conference on Information and different interfaces: IndoWordNet browser that Knowledge Engineering, Las Vegas, Nevada, USA, provides WordNet browsing in different Indian pages 5–19. CSREA Press. languages and SIGMAKEE open source package Daniel Oberle, Anupriya Ankolekar, Pascal Hitzler, for first order theory development. This system Philipp Cimiano, Michael Sintek, Malte Kiesel, can be useful for concept and relation extraction B. Mougouie, S. Vembu, S. Baumann, Massimo from Indian language documents. Future aim is Romanelli, Paul Buitelaar, R. Engel, D. Sonntag, to develop methods for automatic domain specific N. Reithinger, Berenike Loos, R. Porzel, H.-P. Zorn, V. Micelli, C Schmidt, Moritz Weiten, F. Burkhardt, ontology extraction using this system. and J. Zhou. 2007. Dolce ergo sumo: On founda- tional and domain models in swinto (smartweb inte- Acknowledgements grated ontology)). Journal of Web Semantics: Sci- ence, Services and Agents on the World Wide Web, The support of the Department of Information 5(3):156–174. Technology (DIT), Ministry of Communication and Information Technology, Government of India Adam Pease and Christoph Benzmuller.¨ 2010. Sigma: An integrated development environment for logical and also of Ministry of Human Resource Develop- theory development. In The ECAI 2010 Workshop ment is gratefully acknowledged. on Intelligent Engineering Techniques for Knowl- edge Bases (IKBET’2010), Lisbon, Portugal. References Adam pease and Christiane Fellbaum. 2010. Formal ontology as interlingua: The sumo and wordnet link- Pushpak Bhattacharyya. 2010. Indowordnet. In ing project and global wordnet. In Ontology and Proceedings of the Seventh conference on In- Lexicon, A Natural Language Processing perspec- ternational Language Resources and Evaluation tive, pages 25–35. Cambridge University Press. Adam Pease, Christiane Fellbaum, and Piek Vossen. 2008. Building the global wordnet grid. In Proceed- ings of the 18th International Congress of Linguists (CIL18), Seoul, Republic of Korea. J. Ramanand, Akshay Ukey, Brahm Kiran Singh, and Pushpak Bhattacharyya. 2007. Mapping and struc- tural analysis of multi-lingual wordnets. IEEE Data Eng. Bull., 30(1):30–43. Alexandre Riazanov and Andrei Voronkov. 2002. The design and implementation of vampire. AI Com- mun., 15:91–110, August. Jaya Saraswati, Rajita Shukla, Ripple Goyal, and Push- pak Bhattacharyya. 2010. Hindi to english wordnet linkage: Challenges and solutions. In IndoWord- Net Workshop 2010, colocated with ICON 2010, Kharagpur, India. Manish Sinha, Mahesh Reddy, and Pushpak Bhat- tacharyya. 2006. An approach towards construction and application of multilingual indo-wordnet. In 3rd Global Wordnet Conference ( GWC 06), Jeju Island, Korea. Piek Vossen, editor. 1998. EuroWordNet: a multilingual database with lexical semantic networks. Kluwer Academic Publishers, Norwell, MA, USA.