A novel semantic knowledge engine using automated knowledge extraction from World Wide Web

Abstract

It is extremely difficult for the existing search engines (such as Google and Bing) to crawl, index, rank, and manage huge amount of data and locate information. Semantic web technology (such as Google Knowledge Graph and Wolfram Alpha) is emerging into the answer engine market in order to transform the unstructured data into more structured useful information. However, the existing engines suffer due to the fact that curators and volunteers feed these systems manually. In this paper, we introduce a semantic knowledge engine, where we transform the unstructured data into more useful data using an automation technique. We implement the proposed system in 20 different categories including university, car, and movie. According to the experimental results, the proposed knowledge engine is about 450 times faster and requires about 660 times less storage than the existing search engines. The proposed knowledge engine is very user-friendly and produces right-to-the-point answers very fast.