Enormous news contents are getting generated today which keeps on growing to a great extent. The Archiving of news items has become a tedious and challenging task because of its rich quantity. It also creates problems for journalists to find proper content using current search tools. Personalized news extraction helps the journalist to find the right news content without browsing through irrelevant news items. Semantic Web techniques improve personalizing the news content for information extraction. The proposed Ontology-based news extraction framework provides a high degree of semantically similar news contents for a search query. That helps the journalist to develop a news story within a short span of time. The Proposed framework is evaluated using YouTube – 8M dataset and results are positive. The lack of local knowledge concepts incorporated with the underlying ontology used in this framework can be addressed in the future enhancement. Further researches are needed to include the priority listing of news contents extracted for the same search query on geographical and news value specific.

Information Extraction, Knowledge Management, Ontology, Personalization, Semantic web techniques

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