Automatic Ontology Generation for Semantic Search System Using Data Mining Techniques

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.