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Slide 1: SQE – Semantic Query Expansion Digital Enterprise Research Institute National University of Ireland, Galway sebastian.kruk@deri.org jakub.demczuk@deri.org mateusz.kaczmarek@deri.org lukasz.porwol@deri.org  Copyright 2006 Digital Enterprise Research www.deri.ie Institute. All rights reserved.

Slide 2: Outline • Motivations • Algorithm overview • Ideas 2

Slide 3: SQE motivations: • Why? – Simple search returns too many results – Keyword search must be precise – Ambiguity of words 3

Slide 4: SQE motivations: • How – Improve search results by applying semantic based reasoning • Expand queries that have few results, or • Narrow number of results when there are to many of them – reflect user’s interests in results 4

Slide 5: Algorithm overview: 1. User writes a query 3. Query is processed by the fulltext search engine – Lucene • Results are sorted using two metrics: TF(Term Frequency) and IDF(Inverse Document Frequency) 5. Semantic Query Expansion is called 7. Search history is saved for future use 5

Slide 6: SQE picture: Lucene Search: Java web Results (TF/IDF) Query Expansion (WordNet,Taxonomies,FOAF) History 60 Bookmarks 10 20 Java(PL) Java (Island) 50 SSCF Friends Web(www) Spider’s web FOAFRealm Result s 6

Slide 7: Semantic Query Expansion in details: 1. Giving words meanings 2. Associating weights to results: • Long-term context – user’s interests • Mid-term context – recent searches, recently browsed resources etc. • Short-term context – last queries and query itself 3. Final weight is calculated for each result 4. If results are not sufficient or are to broad expansion is being made, else results are returned 7

Slide 8: Ideas : • Word similarity • Word meaning in text • Allow users to view algorithm steps, and if neccessary, modify them by hand – The „Tell me why” button 8