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Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher

Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher. 24 - 26 March 2010 Hue City Vietnam . Laura Po and Sonia Bergamaschi DII, University of Modena and Reggio Emilia, Italy laura.po@unimore.it sonia.bergamaschi@unimore.it. The idea.

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Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher

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  1. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher 24 - 26 March 2010 Hue City Vietnam Laura Po and Sonia Bergamaschi DII, Universityof Modena and Reggio Emilia, Italy laura.po@unimore.it sonia.bergamaschi@unimore.it

  2. The idea • When we are dealing with data sources, we are dealing with structure information that are labeled by humans. • Humans use lexical expressions to assign names. • Natural language labels provide a rich connection between formal objects (e.g. classes and properties) and their intended meanings. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  3. Lexical knowledge inside sources • It is necessary to address the problem of how the concepts are "labelled", i.e. understanding the meaning behind the names denoting ontology elements. • In NLP (Natural Language Processing), Word Wense Disambiguation (WSD) is the process of identifying which sense of a word (i.e. meaning) is used in any given sentence, when the word has a number of distinct senses (polysemy). Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  4. Lexical knowledge inside sources • It is necessary to address the problem of how the concepts are "labelled", i.e. understanding the meaning behind the names denoting ontology elements. • In NLP (Natural Language Processing), Word Wense Disambiguation (WSD) is the process of identifying which sense of a word (i.e. meaning) is used in any given sentence, when the word has a number of distinct senses (polysemy). Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  5. Applications of WSD Roberto Navigli. Word Sense Disambiguation: A Survey, ACM Computing Surveys, 41(2), 2009 • Information Retrieval • Information Extraction • Machine Translation • Content Analysis • Word Processing • Lexicography • The Semantic Web • ontology learning: to build domain taxonomies and enrich large-scale semantic networks Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  6. Lexical Annotation Lexical Annotation is a particular metadata annotation that refers to a semantic resource. • Each lexical annotation has the property to own one or more lexical descriptions. • Lexical Annotation • assigns meanings to class and property names w.r.t. a semantic resource (WordNet) • derives relationships among source elements • Lexical Annotation can be an effective method to solve ambiguity problems! Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  7. Lexical Annotation – an example BT Hypernym of √ √ √ √ lexicalrelationshipsextracted Book SYN Volume Book BT Catalog (Catalog Í Book) Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  8. The ontology matching problem • An ontology is an explicit specification of a conceptualization (Gruber, 1993). • The ontology matching process, for two separate and autonomous ontologies, O1 and O2, consists of finding corresponding entities in ontologies O1 and O2 Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  9. Ontology matchers • Severalontologymatchershavebeenproposed in litterature, altought the mostdo not take advantage of the linguisticaspectof the involvedontologies. • In particular, ontologymatchers do notdiscern elementswithdifferentmeanings. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  10. The SCARLET matcher • Scarlet* is a technique for discovering relationships between two concepts by making use of online available ontologies. • Scarlet discovers semantic relationships between concepts by exploiting the entire Semantic Web as a source of background knowledge. * SCARLET has been developed by the Knowledge and Media Institute at Milton Keynes, UK. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  11. The SCARLET matcher Online Ontology • By using semantic search engines (Swoogle and WATSON), it finds online ontologies containing concepts with the same names as the candidate concepts and then it derives mappings from the relationships in the online ontologies. Í B0 A0 Í A B Ontology 1 Ontology 2 Legenda anchoring relationship Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  12. The SCARLET matcher Online Ontologies • Scarlet is able to identify disjoint relations, subsumption relations, and correspondences. All relations are obtained by using derivation rules which explore direct relations and also relations deduced by applying subsumption reasoning. Í B0 C0 C Í A0 Í A B Ontology 1 Ontology 2 Legenda anchoring relationship Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  13. The evaluation of SCARLET • On a large-scale, real life data sets SCARLET retrived a precision value of 70% • More than half of incorrect anchonring were due to ambiguities . • SCARLET is not able to take advantage of the ontological context in which a concept appears. Lexical Annotation can used to solve the ambiguity problems! Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  14. SCARLET + lexical annotation • By identifying a meaning (or a set of meanings) for each concept it is possible to, more accurately, compare the concept with the concepts that appear in online ontologies. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  15. How Lexical Annotation enhances the ontology matching performance • Performing lexical annotation on the ontologies involved in the matching process allows: • to detect false positive mappings • to discover new mappings • to identify synonyms and more general classes for a given concept Improvingprecision Improvingrecall Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  16. SYNSET1 SYNSET2 SYNSET3 SYNSET4 Lexical annotation improvements 1 - detection of false positive mappings If a concept and its anchoring concept have disregarding meanings (i.e. if they do not have the same list of meanings), the anchoring is detect as a false positive. Í B0 A0 X Í X A B Ontology 1 Ontology 2 Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  17. SYNSET1 SYNSET2 SYNSET3 SYNSET4 Lexical annotation improvements 1- detection of false positive mappings Í B0 C0 X C Í A0 X Í A B Ontology 1 Ontology 2 Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  18. SYNSET4 SYNSET3 SYNSET2 SYNSET1 Lexical annotation improvements 2 - new mapping discovery hyponym Í A B Ontology 1 Ontology 2 Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  19. SYNSET4 SYNSET3 SYNSET2 SYNSET1 Lexical annotation improvements 3 - identification of synonyms and more general concepts Í B0 house New anchoring Í home B Ontology 1 Ontology 2 Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  20. ALA tool • We employ the Automatic Lexical Annotation tool to perform lexical annotation of the ontologies involved in the matching process (source ontologies, online ontologies). • ALA combines the output of 4 WSD algorithms and 2 heuristic rules. • The combination is a sequential composition: • only the first algorithm is executed on the entire data source, the following algorithms are executed only on the set of concepts that were not disambiguated by the previous ones. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  21. Lexical Annotation Evaluation • The application of lexical annotation techniques on the SCARLET results has been tested on two test cases: • NALT+AGROVOC two real life thesauri: the United Nations Food and Agriculture Organization (FAO)’s AGROVOC thesaurus, the United States National Agricultural Library (NAL) Agricultural thesaurus NALT • OAEI 2006 benchmark The benchmark is bibliographic domain, the bibliographic ontologies we took into account are the reference ontology and the Karlsruhe ontology. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  22. Lexical Annotation Evaluation:detection of incorrect anchoring • The results of the automatic lexical annotation have been compared with the manual evaluation done by an expert on the entire set of matching. the most are due to the presence of compound nouns Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  23. Lexical Annotation Evaluation:new mapping discovery • After the execution of lexical annotation, we computed a mapping between two concepts, if we find a relationship between their corresponding meanings in WordNet. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  24. Lexical Annotation Evaluation:comparison • We compared our results with a multiontology disambiguation method that has been previously applied on SCARLET • Unlikemultiontology disambiguation method that retrieves similarity measures, ourmethod offers a definite answer regarding the detection ofsynonymrelationships. • Comparing the results, we evaluated some possible thresholds on the similarity measures retrieved by the multiontolgy disambiguation method (0.19 – 0.22). Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  25. Conclusion • We proposed and experimentally investigated a method to solve ambiguity problems in the context of ontology matching by using automatic lexical annotation techniques (ALA tool). The method has been applied on SCARLET, a semantic web based matcher. • Experimental results have proved that by performing lexical annotation of ontologies we are able to: • to detect false positive mappings • to discover new mappings Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  26. Future work on lexann + matchers • Lexical Annotation is able to identify synonymous and generalization of concepts. Implementing this will give the matcher the possibility to widen the search among online ontologies, thus, improving matching results. • In order to cope with more complex ontologies, our method needs to be extended by including the treatment of compound terms and abbreviations (published at ER2009). • The method could be coped with any matcher. Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  27. Future perspectives • There are several scenarios where we applied lexical annotation • Data integration • Ontology matching • Disambiguation/classification of Google hits • New scenarios • blogs • social networks • Mash up ?! Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

  28. Thanksforyourattention! www.dbgroup.unimo.it Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher - Laura Po

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