Semantic annotation of Bangla news stream to record history

Every day thousands of news articles are published in Bangla from several different sources on the web and this number is even increasing rapidly. On the contrary, the readers are often selective to read their desired news only. In this connection, classical Information Extraction (IE) techniques are used to query with keywords from unstructured or semi-structured news contents to fulfill partial requirements. However, they cannot interpret sequences of events, relation among entities, inference some unveiled facts to facilitate further human analysis. To achieve this goal, semantic technology adds formal structure and semantics to the news stream. In this paper, we propose a system to analyze Bangla news content to annotate especially things, people and places with semantic technology automatically by extracting what happened, when, where and who being involved in the news with the help of classical Natural Language Processing (NLP) techniques. Furthermore, we relate news of today with the previous news to accumulate information over time. We present our proposed system of annotating Bangla news semantically and experiment with SPARQL to inference integrated news from different sources over time and shows its effectiveness in querying specific information.