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DBLP SSE: A DBLP Search Support Engine ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Evolution Towards the Intelligent Web ,[object Object],[object Object],[object Object],Nova Spivack, CEO & Founder, Radar Networks.  Making Sense of the Semantic Web, 2002
Back to the Origin of Web Intelligence How? [Zhong, Liu, Yao 2002] Ning Zhong, Jiming Liu, Yiyu Yao: In Search of the Wisdom Web. IEEE Computer 35(11): 27-31 (2002) Questions from a more practical perspective:  How to serve users  more wisely  from a  personal perspective  ? Can  user personalization  be realized in  different perspectives ? Web-empowered  systems should provide  various supporting functionalities  to users to meet their  diverse needs . User Interests
Search Support Engine ,[object Object],[object Object],[object Object],[object Object]
Creating a Context for User Interest : from Various Perspectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interest Retention and Interest Prediction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],A comparative study of total research interests through the years 1990-2008 and current research interests in 2009 (based on both the power law and exponential law models) Difference on the contribution values from papers published in different years
DBLP-SSE : DBLP Search Support Engine Let’s use our  WI 2009 Program Chair ,  Ricardo Baeza-Yates  to test our system! ,[object Object],[object Object],1.05 1 Usage 1.05 2 Performance 1.0500001 4 Quality 1.0501344 15 Model 1.0515785 2 Impact 1.0516398 5 System 1.0531572 5 Process 1.0907129 2 Trade-off 1.0907176 6 User 1.0922915 8 Index 1.1330068 8 Data 1.2631637 26 Query 2.1001248 10 Content 2.144009 15 Mining 2.269411 19 Engine 3.1938698 7 Distributed 5.587062 64 Search 7.8095837 65 Web Interest Retention Frequency Interest
Representing User Interests ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],<foaf:mbox rdf:resource=&quot; [email_address] &quot; /> <rdfs:seeAlso rdf:resource= &quot; http://dblp.uni-trier.de/db/indices/a-tree/b/Baeza=Yates:Ricardo_A=.html &quot;/> <rdf:Seq> <foaf:topic_interest>Web</foaf:topic_interest> <foaf:topic_interest>Search</foaf:topic_interest> <foaf:topic_interest>Distributed</foaf:topic_interest> <foaf:topic_interest>Engine</foaf:topic_interest> </rdf:Seq> </foaf:Person> </rdf:RDF>
DBLP-SSE : DBLP Search Support Engine ,[object Object],Vague or incomplete  query can be  refined  by the  starting point  (containing  recent interests extracted through interest retention models ). *  Searching  in a Maze, in  Search  of Knowledge: Issues in Early  Artificial Intelligence . *  Web   Intelligence  and  Artificial Intelligence  in Education. * Using  Distributed Data Mining  and  Distributed   Artificial Intelligence  for Knowledge Integration. * Parallel,  Distributed  and Multi-Agent Production Systems – A Research Foundation for  Distributed   Artificial Intelligence . ......  with a starting point (recent interests constraints) List 2 :  * PROLOG Programming for  Artificial Intelligence , Second Edition.  * Artificial Intelligence Architectures for Composition and Performance Environment.  *  Artificial Intelligence  in Music Education: A Critical Review.  * Music,  Intelligence  and Artificiality.  Artificial Intelligence  and Music Education.  * ......  without a starting point (recent interests constraints) List 1 :  Artificial Intelligence   Query :  Web, search, distributed, engine, mining, content, query, data, index  Top 9 Recent interests  Ricardo Baeza-Yates (WI 2009 Program Chair) Log in
Domain Analysis Support Learning hierarchical knowledge structures from conference proceeding indexes. An illustrative example: Domain structure for Artificial Intelligence from conference indexes in the DBLP dataset. A partial multi-level knowledge structure of Artificial Intelligence according to analysis on proceedings indexes of IJCAI 1969-2007. Finer grained sub knowledge structure on robotics in the structure of Artificial Intelligence.
Domain Tracking Support ,[object Object],[object Object],Domain Tracking of “learning” based on Proceedings of the IJCAI 1981-2007. Author Distribution in some fields of Artificial Intelligence.
Why It is a Short Paper : Future Work ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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DBLP-SSE: A DBLP Search Support Engine

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  • 3. Back to the Origin of Web Intelligence How? [Zhong, Liu, Yao 2002] Ning Zhong, Jiming Liu, Yiyu Yao: In Search of the Wisdom Web. IEEE Computer 35(11): 27-31 (2002) Questions from a more practical perspective: How to serve users more wisely from a personal perspective ? Can user personalization be realized in different perspectives ? Web-empowered systems should provide various supporting functionalities to users to meet their diverse needs . User Interests
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  • 10. Domain Analysis Support Learning hierarchical knowledge structures from conference proceeding indexes. An illustrative example: Domain structure for Artificial Intelligence from conference indexes in the DBLP dataset. A partial multi-level knowledge structure of Artificial Intelligence according to analysis on proceedings indexes of IJCAI 1969-2007. Finer grained sub knowledge structure on robotics in the structure of Artificial Intelligence.
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