Creating a Scholarly Knowledge Graph from Survey Article Tables

TL;DR

This paper introduces a human-in-the-loop methodology for constructing scholarly knowledge graphs from survey article tables, leveraging high-quality tabular data to automate and streamline knowledge extraction from scientific literature.

Contribution

The paper presents a novel five-step process utilizing survey tables and references to efficiently build scholarly knowledge graphs with human oversight.

Findings

Successfully imported 92 survey articles and 160 tables into the knowledge graph.

Added 2,626 papers to the knowledge graph using the proposed methodology.

Demonstrated the approach's feasibility while highlighting the need for manual effort.

Abstract

Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2,626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.