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Titlebook: Artificial Intelligence: Methodology, Systems, and Applications; 14th International C Darina Dicheva,Danail Dochev Conference proceedings 2

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期刊全稱Artificial Intelligence: Methodology, Systems, and Applications
期刊簡稱14th International C
影響因子2023Darina Dicheva,Danail Dochev
視頻videohttp://file.papertrans.cn/163/162575/162575.mp4
發(fā)行地址Unique visibility.State-of-the-art survey.Fast-track conference proceedings
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Intelligence: Methodology, Systems, and Applications; 14th International C Darina Dicheva,Danail Dochev Conference proceedings 2
影響因子The 14th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2010) was held in Varna, Bulgaria, during September 8–10, 2010. The AIMSA conference series has provided a biennial forum for the presen- tion of artificial intelligence research and development since 1984. The conference covers the full range of topics in artificial intelligence (AI) and related disciplines and provides an ideal forum for international scientific exchange between Central/Eastern Europe and the rest of the world. The 2010 AIMSA edition continued this tradition. For AIMSA 2010, we decided to place special emphasis on the application and leverage of AI technologies in the context of knowledge societies where knowledge creation, accessing, acquiring, and sharing empower individuals and communities. A number of AI techniques play a key role in responding to these challenges. AI is - tensively used in the development of systems for effective management and flexible and personalized access to large knowledge bases, in the Semantic Web technologies that enable sharing and reuse of and reasoning over semantically annotated resources, in the emerging social Semantic Web a
Pindex Conference proceedings 2010
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A Relative Word-Frequency Based Method for Relevance Feedbackeveral TREC collections demonstrate that retrieval effectiveness can be much improved when the proposed approach is used. Experimental results show that the improvement of our proposed approach is more than 30% over traditional relevance feedback techniques.
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0302-9743 tive management and flexible and personalized access to large knowledge bases, in the Semantic Web technologies that enable sharing and reuse of and reasoning over semantically annotated resources, in the emerging social Semantic Web a978-3-642-15430-0978-3-642-15431-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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