Search (4 results, page 1 of 1)

  • × theme_ss:"Computerlinguistik"
  • × theme_ss:"Wissensrepräsentation"
  • × type_ss:"el"
  1. Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012) 0.00 
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    Abstract
    Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
  2. Wong, W.; Liu, W.; Bennamoun, M.: Ontology learning from text : a look back and into the future (2010) 0.00 
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    Abstract
    Ontologies are often viewed as the answer to the need for inter-operable semantics in modern information systems. The explosion of textual information on the "Read/Write" Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas such as natural language processing have fuelled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium, and discusses the remaining challenges that will define the research directions in this area in the near future.
  3. Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002) 0.00 
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    Date
    29. 6.2015 14:55:01
    29. 6.2015 16:09:05
  4. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.00 
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    Date
    29. 6.2015 14:51:28

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