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 ACM WIDM'2005

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 ACM WIDM'2005

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 ACM WIDM'2005

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 ACM WIDM'2005

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) ACM WIDM'2005

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 ACM WIDM'2005

7. A Fragment of the WordNet Is-A Hierarchy ACM WIDM'2005

8. 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 ACM WIDM'2005

9. 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 ACM WIDM'2005

10. 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 ACM WIDM'2005

11. Evaluation ACM WIDM'2005

12. 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 ACM WIDM'2005

13. http://www.ece.tuc.gr/similarity ACM WIDM'2005

14. 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) ACM WIDM'2005

15. SSRM • Query term re-weighting similar terms reinforce each other • Query term expansion with synonyms and similar terms • Document similarity ACM WIDM'2005

16. Query Term Expansion ACM WIDM'2005

17. 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) ACM WIDM'2005

18. 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 ACM WIDM'2005

19. http://www.ece.tuc.gr/intellisearch ACM WIDM'2005

20. 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 ACM WIDM'2005

21. Experimental Results ACM WIDM'2005

22. 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 ACM WIDM'2005

23. Evaluation on MedLine ACM WIDM'2005

24. 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 ACM WIDM'2005

25. 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 ACM WIDM'2005

26. Try our system on the Web • Semantic Similarity System: http://www.ece.tuc.gr/similarity • SRRM: http://www.ece.tuc.gr/intellisearch ACM WIDM'2005