Constructing Knowledge Graph by Extracting Correlations from Wikipedia Corpus for Optimizing Web Information Retrieval

The conversion of unstructured big data into knowledgeable information has been the hotspot of search applications today. Nearly 75% of queries issued to Web search engines aim at finding information about entities. In an ideal case, the user wants to know the relations existing between the data objects. Conceptual knowledge graph provides an efficient way for exploring such relations. Past researches relied on knowledge bases like DBpedia to build such graphs. In this paper, we introduce a method that automatically extracts the key aspects of search query from the Wikipedia corpus. The extracted relations are dynamically expressed as a knowledge graph. Additionally, the system returns the list of results i.e., Wikipedia documents ranked in the order of their relevance in response to the search query. Thus, the proposed system can be viewed as an information retrieval system that leverages knowledge graph to provide more promising information to the user.