Word Embedding Based Knowledge Representation with Extracting Relationship Between Scientific Terminologies

Abstract

With the trends of big data era, many people want to acquire the reliable and refined information from web environments. However, it is difficult to find appropriate information because the volume and complexity of web information is increasing rapidly. So many researchers are focused on text mining and personalized recommendation for extracting users' interests. The proposed approach extracted semantic relationship between scientific terminologies with word embedding approach. We aggregated science data in BT for supporting users' wellness. In our experiments, query expansion is performed with relationship between scientific terminologies with user's intention.