SemCrawl: Framework for Crawling Ontology Annotated Web Documents for Intelligent Information Retrieval

摘要

Web is considered as the largest information pool and search engine, a tool for extracting information from web, but due to unorganized structure of the web it is getting difficult to use search engine tool for finding relevant information from the web. Future search engine tools will not be based merely on keyword search, whereas they will be able to interpret the meaning of the web contents to produce relevant results. Design of such tools requires extracting information from the contents which supports logic and inferential capability. This paper discusses the conceptual differences between the traditional web and semantic web, specifying the need for crawling semantic web documents. In this paper a framework is proposed for crawling the ontologies/semantic web documents. The proposed framework is implemented and validated on different collection of web pages. This system has features of extracting heterogeneous documents from the web, filtering the ontology annotated web pages and extracting triples from them which supports better inferential capability.