Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web

1. Semantic Similarity Methods in WordNet andTheir Application to Information Retrieval onthe Web Giannis Varelas Epimenidis Voutsakis Paraskevi Raftopoulou Euripides G.M. Petrakis Evangelos Milios Semantic Similarity

2. Semantic Similarity • Semantic Similarity relates to computing the conceptual similarity between terms which are not lexicographically similar • “car” “automobile” • Map two terms to an ontology and compute their relationship in that ontology Semantic Similarity

3. Objectives • We investigate several Semantic Similarity Methods and we evaluate their performance • http://www.ece.tuc.gr/similarity • We propose the Semantic Similarity Retrieval Model (SSRM) for computing similarity between documents containing semantically similar but not necessarily lexicographically similar terms • http://www.ece.tuc.gr/intellisearch Semantic Similarity

4. 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 Semantic Similarity

5. WordNet • A vocabulary and a thesaurus offering a hierarchical categorization of natural language terms • More than 100,000 terms • An ontology of natural language terms • Nouns, verbs, adjectives and adverbs are grouped into synonym sets (synsets) • Synsets represent terms or concepts • stadium, bowl, arena, sports stadium – (a large structure for open-air sports or entertainments) Semantic Similarity

6. 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 Semantic Similarity

7. A Fragment of the WordNet Is-A Hierarchy Semantic Similarity

8. Semantic Similarity

9. 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 corpus • Feature based: similarity between their properties (e.g., definitions) or based on their relationships to other similar terms • Hybrid: combine the above ideas Semantic Similarity

10. 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 Semantic Similarity

11. Semantic Similarity on WordNet • 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 Semantic Similarity

12. Evaluation Semantic Similarity

13. Observations • 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] • Information Content is measured on WordNet rather than on corpus [Seco2002] • Similarity only for nouns and verbs • No taxonomic structure for other p.o.s Semantic Similarity

14. http://www.ece.tuc.gr/similarity Semantic Similarity

15. Semantic Similarity Retrieval Model (SSRM) • Classic retrieval models retrieve documents with the same query terms • SSRM will retrieve documents which also contain semantically similar terms • Queries and documents are initially assigned tfxidf weights • q=(q1,q2,…qN) , d=(d1,d2,…dN) Semantic Similarity

16. SSRM • Query term re-weighting similar terms reinforce each other • Query term expansion with synonyms and similar terms • Document similarity Semantic Similarity

17. Query Term Expansion Semantic Similarity

18. Observations • Specification of T ? • Large T may lead to topic drift • Word sense disambiguation for expanding with the correct sense • Expansion with co-concurring terms? • SVD, local/global analysis • Semantic similarity between terms of different parts of speech? • Work with compound terms (phrases) Semantic Similarity

19. Evaluation of SSRM • SSRM is evaluated through intellisearcha system for information retrieval on the WWW • 1,5 Million Web pages with images • Images are described by surrounding text • The problem of image retrieval is transformed into a problem of text retrieval Semantic Similarity

20. http://www.ece.tuc.gr/intellisearch Semantic Similarity

21. Methods • Vector Space Model (VSM) • SSRM • Each method is represented by a precision/recall plot • Each point is the average precision/recall over 20 queries • 20 queries from the list of the most frequent Google image queries Semantic Similarity

22. Experimental Results Semantic Similarity

23. MeSH and MedLine • MeSH: ontology for medical and biological terms by the N.L.M. • 22,000 terms • MedLine: the premier bibliographic medical database of N.L.M. • 13 Million references Semantic Similarity

24. Evaluation on MedLine Semantic Similarity

25. Conclusions • Semantic similarity methods approximated the human notion of similarity reaching correlation up to 83% • SSRM exploits this information for improving the performance of retrieval • SSRM can work with any semantic similarity method and any ontology Semantic Similarity

26. Future Work • Experimentation with more data sets (TREC) and ontologies • Extend SSRM to work with • Compound terms • More parts of speech (e.g., adverbs) • Co-occurring terms • More terms relationships in WordNet • More elaborate methods for specification of thresholds Semantic Similarity

27. Try our system on the Web • Semantic Similarity System: http://www.ece.tuc.gr/similarity • SRRM: http://www.ece.tuc.gr/intellisearch Semantic Similarity