Chapter 3: Search for Knowledge
Identifieur interne : 000452 ( Main/Exploration ); précédent : 000451; suivant : 000453Chapter 3: Search for Knowledge
Auteurs : Gerhard Weikum [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
English descriptors
- Teeft :
- Active knowledge, Bayern munich, Binary relations, Category system, Comprehensive knowledge base, Comprehensive knowledge bases, Data model, Database, Different languages, Different meanings, Ennio morricone, Entity, European leagues, Explicit knowledge base, Extensional knowledge, Extraction, Fact hypotheses, Famous people, French politicians, French president, German soccer clubs, German team, Harvesting, Hasplayedagainst relationship, Haswonaward academyaward, High precision, Individual entities, Information extraction, Internet search engines, Keyword, Keyword queries, Keyword query, Keyword search, Keywords, Knowledge base, Knowledge bases, Knowledge harvesting, Knowledge search, Knowledge services, Knowledge workers, Large knowledge bases, Large ontology, Locatedin europe, Long tail, Longest river, Madrid, Nicolas sarkozy, Ontology, Open data, Oscar winners, Other projects, Prominent entities, Property names, Query, Query language, Query processing, Ranking, Ranking model, Ranking models, Real madrid, Regular expressions, Relational facts, Sarkozy, Search conditions, Search engine, Search engines, Search result, Seed facts, Semantic classes, Semantic search, Semantic search engines, Sigmod record, Small extent, Soccer, Sparql, Such queries, Suchanek, Technical challenges, Temporal knowledge, Triple, Triple pattern, Triple patterns, User, Weikum, Western movies, Wikipedia, Wikipedia articles, Yago.
Abstract
Abstract: There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. In addition, Semantic-Web-style ontologies, structured Deep-Web sources, and Social-Web networks and tagging communities can contribute towards a grand vision of turning the Web into a comprehensive knowledge base that can be efficiently searched with high precision. This vision and position paper discusses opportunities and challenges along this research avenue. The technical issues to be looked into include knowledge harvesting to construct large knowledge bases, searching for knowledge in terms of entities and relationships, and ranking the results of such queries.
Url:
DOI: 10.1007/978-3-642-12310-8_3
Affiliations:
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This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. In addition, Semantic-Web-style ontologies, structured Deep-Web sources, and Social-Web networks and tagging communities can contribute towards a grand vision of turning the Web into a comprehensive knowledge base that can be efficiently searched with high precision. This vision and position paper discusses opportunities and challenges along this research avenue. 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