Automatic Ontology Generation for Semantic Search System Using Data Mining Techniques
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Date
2005-02-02
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INFLIBNET Centre
Abstract
Here we present about automatically generated ontologies for a semantic web search
system using data mining techniques. This will improve the query process and will get
better semantic results. Ranking algorithm[1] is used to search and analyze web documents
in a more flexible and effective way. Hyperlink structure of web document is utilized to rank
the results. We use association rule mining to find the maximal keyword patterns. Clustering
is used to group retrieved documents into distinct sets. This will extract knowledge about
query from the web, populate a knowledge base. The search engine that searches the web
documents so far are syntactic oriented. Here we develop a searching system that
semantically searches the documents. The semantics of the terms is achieved using the
ontologies. Ontology serves as Meta data schemas, providing a controlled vocabulary of
concepts, each with explicitly defined meaning. Ranking algorithm used here is the hyper
textual ranking algorithm that scans both the contents of the documents and also the
reciprocally linked documents. This technique has several advantages that include providing
better semantic notion during the search. It also serves for multiple frame documents.
There is a need for automatic generation of ontologies when using the semantic searching
system. The paper here focuses on how the automatic generation of ontologies could be
done for a semantic search system using data mining techniques.
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Keywords
Ontology, Data mining, Semantic web, Information retrieval