Exploratory Search Using Vector Model and Linked Data

Daeun Yim, Purdue University

Abstract

The way people acquire knowledge has largely shifted from print to web resources. Meanwhile, search has become the main medium to access information. Amongst various search behaviors, exploratory search represents a learning process that involves complex cognitive activities and knowledge acquisition. Research on exploratory search studies on how to make search systems help people seek information and develop intellectual skills. This research focuses on information retrieval and aims to build an exploratory search system that shows higher clustering performance and diversified search results. In this study, a new language model that integrates the state-of-the-art vector language model (i.e., BERT) with human knowledge is built to better understand and organize search results. The clustering performance of the new model (i.e., RDF+BERT) was similar to the original model but slight improvement was observed with conversational texts compared to the pre-trained language model and an exploratory search baseline. With the addition of the enrichment phase of expanding search results to related documents, the novel system also can display more diverse search results.

Degree

M.Sc.

Advisors

Yang, Purdue University.

Subject Area

Language|Artificial intelligence|Cognitive psychology|Design|Logic|Psychology|Web Studies

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