Creating a Fine-grained Scholarly Knowledge Graph from Abstracts
Date:
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ICT.OPEN website
Abstract: Scholarly knowledge graphs (SKGs) have been proposed as a promising solution to describe research papers in a structural way to improve scientific discovery. However, most current SKGs focus on describing metadata-level information such as title, abstract, keywords, author, venue, affiliation, references and citation, while less attention are paid to structuring the content of the papers. To bridge this gap, we design an ontology to describe the content of abstracts and then use this ontology to construct a fine-grained SKG from vast of abstracts. To construct this SKG, we first extract different types of sentence from abstracts, including research question, research method and research result. We compare two different approaches to achieve sentence type extraction: traditional-ML/DL based and LLMs-based approaches. We finally evaluate the fine-grained SKGs with several down stream tasks.