Rise 2014 st requier
- 1. Contextual Web Service Suggest a query and a search engine Aurélien Saint Requier, LITIS Rouen
- 2. Table of contents What is search on the Internet?What is search on the Internet?What is search on the Internet?What is search on the Internet? Our proposal ● Modelize user interests ● Suggest pairs of conceptual query and search engine Evaluation Conclusion
- 4. Search the web 1.Select a search engine 2.Formulate your need 3.Hope to find a relevant result in the result list
- 5. Select a search engine
- 6. Formulate a need ➔Users express query in few words (2-3) ➔Between 20% and 30% of queries contain a single word ➔Users often reformulate their queries ➔For novice users, the formulation of queries is a difficult task ➔For a complex information task, users formulate more and longer queries in a same session
- 7. Problems
- 9. Analyze results ➔Users show interest on the first and second results ➔Users do not go beyond the first result page ➔For a complex information task, users spend more time on the result page
- 10. Proposal Goal: ➔Help the user to formulate his need and suggest a search engine according to his need How: ➔Get interests of users ➔Suggest a pair composed of a conceptual query and a search engine
- 11. Get interests of a user ➔Use a weighted conceptual user profile: ● a long term profile = knowledge of the user ● a short term profile = context of the search ➔Corpus: ● LP : web pages mark as favorite, saved web pages and documents provide by users to avoid cold start problem. ● SP : all visited web pages
- 12. Get interests of a user ➔Represent an interest by a DBPedia category ➔Weight is equal to the probability of occurrence of the concept in the corpus
- 13. Technical issues to profile construction ●Use Zemanta to extract DBPedia concepts from text ●Encode profile in Attention Profiling Markup Language (APML) ●Develop a Firefox extension to track user web activities
- 14. From concepts to thematic profile
- 17. Suggest pairs of conceptual query and search engine Process : 1.Get keyword user query 2.Translate keyword query in conceptual queries 3.Match conceptual queries with search engines 4.Suggest pairs of conceptual query and search engines
- 18. Translate keyword query to conceptual query
- 19. Determine relevant search engine to the conceptual query (1) ●Define a semantic description of a Search Engine : <SearchEngine> <Id>e018</Id> <Name>LastFM</Name> <Url>http://www.lastfm.fr/music/</Url> <Description>Last.fm is a music recommendation service. </Description> <Specialized>true</Specialized> <Thematic> <Subject> … </Subject> </Thematic> <ContentType> <Type>http://dbpedia.org/ontology/Band</Type> <Type>http://dbpedia.org/ontology/Single</Type> <Type>http://dbpedia.org/ontology/MusicalWork</Type> <Type>http://dbpedia.org/ontology/Album</Type> </ContentType> <Popularity>5</Popularity> <Searchable>true</Searchable> </SearchEngine>
- 20. Determine relevant search engine to the conceptual query (2) ●Use a similarity measure based on the types and the categories of conceptual queries
- 21. Finaly
- 22. Finaly
- 23. Experimental system ●Based on WebLab and Liferay ● Use Web services and portlet
- 24. Experimental system ●Based on WebLab and Liferay ● Use Web services and portlet
- 25. Conclusion Modelize user interests by a thematic profile Use this thematic profile to translate keyword queries into conceptual queries Suggest pairs of conceptual query and search engine Upcoming evaluation ● Compare our approach of (conceptual query / search engine) pair suggestion to (google suggestion / google search engine) pair suggestion