Evolving Web, Evolving Search
This event took place on Tuesday 04 March 2008 at 11:30
Prof. Yong Yu Shanghai Jiao Tong University
Web is evolving from time to time, more and more intelligent search engines are also emerging over time. the Traditional Web is composed of many unstructured Web pages. These pages are linked together and mainly for human reading. We focus on how to crawl more pages, improve search relevance or make search interactions simpler. Accordingly, we build the general search engines, the vertical search engines and the meta search engines. The emergence of Web 2.0 lowers the barrier for contribution. More people are involved and make the Web social. We focus on how to elaborate user involved data. Accordingly, we develop blog search, wiki search and tag enhanced search. The Semantic Web is composed of structured interlinked data. These data includes schema, axiom definitions and related assertions. It is mainly for machine understanding. We focus on how to learn or populate ontologies from the traditional Web, do search on the combination of Web ontology and Web pages, integrate reasoning with search to the Web scale or do semantic search using keyword queries.
This event took place on Tuesday 04 March 2008 at 11:30
Web is evolving from time to time, more and more intelligent search engines are also emerging over time. the Traditional Web is composed of many unstructured Web pages. These pages are linked together and mainly for human reading. We focus on how to crawl more pages, improve search relevance or make search interactions simpler. Accordingly, we build the general search engines, the vertical search engines and the meta search engines. The emergence of Web 2.0 lowers the barrier for contribution. More people are involved and make the Web social. We focus on how to elaborate user involved data. Accordingly, we develop blog search, wiki search and tag enhanced search. The Semantic Web is composed of structured interlinked data. These data includes schema, axiom definitions and related assertions. It is mainly for machine understanding. We focus on how to learn or populate ontologies from the traditional Web, do search on the combination of Web ontology and Web pages, integrate reasoning with search to the Web scale or do semantic search using keyword queries.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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