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

15. Profile fusion
●Function

16. Profile fusion
●Result

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