Demonstration of Faceted Search on Scholarly Knowledge Graphs

TL;DR

This paper demonstrates a faceted search system that retrieves and filters scholarly knowledge graph data using dynamic facets, enhancing the relevance and specificity of search results for scientific queries.

Contribution

It introduces a novel faceted search approach with dynamic facets that adapt based on content, improving scholarly literature retrieval from knowledge graphs.

Findings

Effective retrieval of scholarly data using dynamic facets

Facilitates comparison and filtering of scholarly knowledge graphs

Enhances relevance of search results in scholarly research

Abstract

Scientists always look for the most accurate and relevant answer to their queries on the scholarly literature. Traditional scholarly search systems list documents instead of providing direct answers to the search queries. As data in knowledge graphs are not acquainted semantically, they are not machine-readable. Therefore, a search on scholarly knowledge graphs ends up in a full-text search, not a search in the content of scholarly literature. In this demo, we present a faceted search system that retrieves data from a scholarly knowledge graph, which can be compared and filtered to better satisfy user information needs. Our practice's novelty is that we use dynamic facets, which means facets are not fixed and will change according to the content of a comparison.