A Semantic Content Based Recommendation System for Cross-Lingual News
News articles in web narrate important events happening worldwide. These articles are not only written in English, but also in different languages for different native people. In this paper, we propose an approach for an automated Bengali-English semantic recommender system based on ontology by analyzing news domain. News ontology is designed automatically by using information extraction techniques. Both the news title and news body are considered separately in the ontology creation process. First, important information from news is extracted and ontology is created from the source language document. Then, ontology is created from target language document following similar technique. Next, ontology matching is performed between the translated source ontology and target English Ontology. Matching can also be done with synonymous documents. A matching factor is calculated which can be taken as the semantic similarity measure between the cross-lingual documents. Recommendation of news items is done based on this matching factor. The experiment study verifies the proposed method adopted by us.
Semantic Recommendation, Bilingual News, Semantic Web, News Ontology, Recommendation System