Towards a better contextualization of web contents via entity-level analytics

Abstract

With the abundance of data and wide access to the internet, a user can be overwhelmed with information. For an average Web user, it is very difficult to identify which information is relevant or irrelevant. Hence, in the era of continuously enhancing Web, organization and interpretation of Web contents are very important in order to easily access the relevant information. Many recent advancements in the area of Web content management such as classification of Web contents, information diffusion, credibility of information, etc. have been explored based on text and semantic of the document. In this paper, we propose a purely semantic contextualization of Web contents. We hypothesize that named entities and their types present in a Web document convey substantial semantic information. By extraction of this information, we aim to study the reasoning and explanation behind the Web contents or patterns. Furthermore, we also plan to exploit LOD (Linked Open Data) to get a deeper insight of Web contents.