Next Generation Search Engines: Advanced Models for Information Retrieval: Advanced Models for Information Retrieval

Jouis, Christophe
IGI Global, 31 mrt. 2012 - 560 pagina's

Recent technological progress in computer science, Web technologies, and the constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Current search engines employ advanced techniques involving machine learning, social networks, and semantic analysis.

Next Generation Search Engines: Advanced Models for Information Retrieval is intended for scientists and decision-makers who wish to gain working knowledge about search in order to evaluate available solutions and to dialogue with software and data providers. The book aims to provide readers with a better idea of the new trends in applied research.


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Indexing the World Wide Web
Decentralized Search and the Clustering Paradox in Large Scale Information Networks
Metadata for Search Engines
Crosslingual Access to Photo Databases
Fuzzy Ontologies Building Platform for Semantic Web
Section 2
Searching and Mining with Semantic Categories
Semantic Models in Information Retrieval
Using Association Rules for Query Reformulation
Question Answering
Finding Answers to Questions in Text Collections or Web in Open Domain or Specialty Domains
ContextAware Mobile Search Engine
SpatioTemporal Based Personalization for Mobile Search
Section 4
Studying Web Search Engines from a User Perspective
Artificial Intelligence Enabled Search Engines AIESE and the Implications

The Use of Text Mining Techniques in Electronic Discovery for Legal Matters
Intelligent Semantic Search Engines for Opinion and Sentiment Mining
Section 3
HumanCentred Web Search
Extensions of Web Browsers useful to Knowledge Workers
Next Generation Search Engine for the Result Clustering Technology
A Framework for Evaluating the Retrieval Effectiveness of Search Engines
Compilation of References
About the Contributors

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Over de auteur (2012)

Christophe Jouis is assistant professor at the University Paris Sorbonne Nouvelle, France. He received a Ph.D. in Applied Mathematics at the Ecole des Hautes Etudes en Sciences Sociales (EHESS); and CAMS ( Centre d Analyse et de Mathématiques Sociales ), OPTION: Science, Logic, Linguistics. From 2000 to 2004 he was associate professor in the Department of Computer Science at the University of Quebec at Trois-Rivieres (Canada), under the direction of Professor Ismail Biskri. In 2005, he joined the LIP6 ("Laboratoire d'Informatique de Paris 6), affiliated with the University Pierre et Marie Curie (UMPC) and the CNRS (France). Within the LIP6, he is currently a member of the research team ACASA ( Cognitive Agents and Automated Symbolic Learning ), led by Professor Jean-Gabriel Ganascia. His research interests are in natural language processing (NLP), cognitive sciences, ontology, typicality, data mining and information retrieval.

Ismaïl Biskri is full professor in computational linguistics and artificial intelligence at the computer science department of the University of Quebec at Trois-Rivières. He is also associate professor at the Computer Science Department of the University of Quebec at Montreal. He is a researcher at the LAMIA Laboratory. His research interests concern aspects of fundamental research on the syntactic and functional semantic analysis of natural languages with using models of Categorial Grammars and combinatory logic. He also works on specific issues in text-mining, information retrieval, and terminology. His research is funded by the Canadian granting agencies FQRSC, SSHRC, and NSERC.

Jean-Gabriel Ganascia is presently Professor of computer science at Paris University Pierre et Marie Curie (Paris VI) and researcher at the computer science laboratory of Paris VI University (LIP6) where he leads the ACASA ( Cognitive Agents and Automated Symbolic Learning ) team. He originally worked on symbolic machine learning and knowledge engineering. His thèse d'état , defended in 1987, was a pioneering work on the algebraic framework on which the association rule extraction techniques are based. Today, his main scientific interests cover different areas of artificial intelligence: scientific discovery, cognitive modeling, data-mining, and digital humanities. He has published more than 350 scientific papers in conference proceedings, journals, and books. In the past, Jean-Gabriel Ganascia was also program leader in the CNRS executive from 1988 to 1992 before moving to direct the Cognitive Science Coordinated Research Program and head the Cognition Sciences Scientific Interest Group from 1993 until 2000.

Magali Roux is a CNRS Research Director involved in the development and administration of programs and courses in e-Biology. Her research interests span a wide range with domains centered on knowledge organization and data management in Medical Biology, Molecular Biology and, recently, in Systems Biology in the context of e-Sciences. After obtaining her Ph.D. in Biochemistry from the University of the Mediterranean in 1979, she started as assistant-professor at the Marseille University Hospital before being offered a post-doctoral position at Harvard University in the Pr. J. Strominger laboratory, where she provided one of the first bioinformatics analyses performed on DNA data. Since that, she has produced leading contributions in the fields of Immunology and Cancer. In the early 2000s, she moved from Experimental to Digital Biology to promote interoperability, data sharing and re-use. Dr. Roux serves on numerous study panels and is currently active in a number of scientific societies. [Editor]

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