Using Text Comprehension Model for Learning Concepts, Context, and Topic of Web Content

Concepts in web ontologies help machines to understand data through the meanings they hold. Furthermore, learning contexts and topics of web documents also have helped in better semantic-oriented structuring and retrieval of data on the web. In this short paper we present a novel approach for domain-independent open learning of domain concepts, context and topic of any given Web document. Our approach is based on a computational version of the Construction-Integration (CI) model of text comprehension. Our proposed system mimics the way humans learn the meanings of textual units and identify domain concepts, contexts and topics in the form of semantic networks. We apply our system on a number of web documents with a range of topics and domains. The resulting semantic networks provide a quantitative and qualitative insights into the nature of the given web documents.