Design and Evaluation of Semantic Similarity Measures for Concepts Stemming from the Same or Different Ontologies

1. Design and Evaluation of Semantic Similarity Measures for Concepts Stemming from the Same or Different Ontologies Euripides G.M. Petrakis Giannis Varelas Angelos Hliaoutakis Paraskevi Raftopoulou WMS'06, Chania, Crete

2. Semantic Similarity • Relates to computing the conceptual similarity between terms which are not necessarily lexicacally similar • “car”-“automobile”-“vehicle”, • “drug”- “medicine” • Tool for making knowledge commonly understandable in applications such as IR, information communication in general WMS'06, Chania, Crete

3. Methodology • Terms from different communicating sources are represented by ontologies • Map two terms to an ontology and compute their relationship in that ontology • Terms from different ontologies: Discover linguistic relationships or affinities between terms in different ontologies WMS'06, Chania, Crete

4. Contributions • We investigate several Semantic Similarity Methods and we evaluate their performance • http://www.intelligence.tuc.gr/similarity • We propose a novel semantic similarity measure for comparing concepts from different ontologies WMS'06, Chania, Crete

5. Ontologies • Tools of information representation on a subject • Hierarchical categorization of terms from general to most specific terms • object  artifact  construction  stadium • Domain Ontologies representing knowledge of a domain • e.g., MeSH medical ontology • General Ontologies representing common sense knowledge about the world • e.g., WordNet WMS'06, Chania, Crete

6. WordNet • A vocabulary and a thesaurus offering a hierarchical categorization of natural language terms • More than 100,000 terms • Nouns, verbs, adjectives and adverbs are grouped into synonym sets (synsets) • Synsets represent terms or concepts with similar meaning • stadium, bowl, arena, sports stadium – (a large structure for open-air sports or entertainments) WMS'06, Chania, Crete

7. WordNet Hierarchies • The synsets are also organized into senses • Senses: Different meanings of the same term • The synsets are related to other synsets higher or lower in the hierarchy by different types of relationships e.g. • Hyponym/Hypernym (Is-A relationships) • Meronym/Holonym (Part-Of relationships) • Nine noun and several verb Is-A hierarchies WMS'06, Chania, Crete

8. A Fragment of the WordNet Is-A Hierarchy WMS'06, Chania, Crete

9. MeSH • MeSH: ontology for medical and biological terms by the N.L.M. • Organized in IS-A hierarchies • More than 15 taxonomies, more than 22,000 terms • No part-of relationships • The terms are organized into synsets called “entry terms’’ WMS'06, Chania, Crete

10. A Fragment of the MeSH Is-A Hierarchy WMS'06, Chania, Crete

11. Semantic Similarity Methods • Map terms to an ontology and compute their relationship in that ontology • Four main categories of methods: • Edge counting: path length between terms • Information content: as a function of their probability of occurrence in a corpus • Feature based: similarity between their properties (e.g., definitions) or based on their relationships to other similar terms • Hybrid: combine the above ideas WMS'06, Chania, Crete

12. Example • Edge counting distance between “conveyance” and “ceramic” is 2 • An information content method, would associate the two terms with their common subsumer and with their probabilities of occurrence in a corpus WMS'06, Chania, Crete

13. X-Similarity • Relies on matching between synsets and set description sets • A,B: synsets or term description sets • Do the same with all IS-A, Part-Of relationships and take their maximum WMS'06, Chania, Crete

14. Example • S(Hypothyroidism, Hyperthyroidism) = 0.387 WMS'06, Chania, Crete

15. Evaluation • The most popular methods are evaluated • All methods applied on a set of 38 term pairs • Their similarity values are correlated with scores obtained by humans • The higher the correlation of a method the better the method is WMS'06, Chania, Crete

16. Evaluation on WordNet WMS'06, Chania, Crete

17. Evaluation on MeSH WMS'06, Chania, Crete

18. Cross Ontology Measures • We used 40 MeSH terms pairs • One of the terms is a also a WordNet term • We measured correlation with scores obtained by experts WMS'06, Chania, Crete

19. Comments • Edge counting/Info. Content methods work by exploiting structure information • Good methods take the position of the terms into account • Higher similarity for terms which are close together but lower in the hierarchy e.g., [Li et.al. 2003] • X – Similarity performs at least as good as other Feature-Based methods • Outperforms other Cross-Ontology methods WMS'06, Chania, Crete

20. Conclusions • Semantic similarity methods approximated the human notion of similarity reaching correlation up to 83% • Cross ontology similarity is a difficult problem that required further investigation • Work towards integrating Sem. Sim within IntelliSearch information Retrieval System for Web documents • http://www.intelligence.tuc.gr/intellisearch WMS'06, Chania, Crete

21. Try our system on the Web http://www.intelligence.tuc.gr/similarity Implementation: Giannis Varelas Spyros Argyropoulos WMS'06, Chania, Crete

22. www.intelligence.tuc.gr/similarity WMS'06, Chania, Crete