An Ontology Based Anatomy Approach To Text Mining Summarization

Nowadays many events posted on the internet. It provides us abundant resources. The user may find some difficulties in extracting the most informative summary and the associative core parts of the topic which is defined temporary. Thus, as a result, summarization is used as a technique for improving querying. To ensure this technique an anatomy based summarization method called Topic Summarization and Content Anatomy (TSCAN) was proposed to summarize the content of a temporal topic in existing work. A temporal similarity (TS) function is applied to generate the event dependencies and context similarity to form an evolution graph of the topic. In this paper, we compare two methods for article summarization. The first method is mainly based on term-frequency, while the second method is based on ontology. We build an ontology database for analyzing the main topics of the article using NPL tool and protégé tool. Protégé can be customized to provide domain-friendly support for creating knowledge models and entering data.