A semantic knowledge engine using automated knowledge extraction from world wide web
Mabbu, Venkatesk. 2016. A semantic knowledge engine using automated knowledge extraction from world wide web. --In Proceedings: 12th Annual Symposium on Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 72
It becomes extremely difficult for the existing search engines (such as Google) to crawl, index, rank, and manage huge amount of data and locate information while answering questions. Semantic web technology (such as Google Knowledge Graph) 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 project, we aim to transform the unstructured data into more useful data using an automation technique. We implement the proposed system in 11 different categories including universities. Based on a survey among 50 university students, we receive excellent satisfactory results as the proposed engine answers more effectively. In an average, the proposed engine helps save 25% search time and 25% energy consumption for each 100 searches when compared with the existing search engines.
Presented to the 12th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Heskett Center, Wichita State University, April 29, 2016.
Research completed at Department of Electrical Engineering and Computer Science, College of Engineering