SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs

TL;DR

This paper introduces SKG, a semantic knowledge graph framework that enhances academic paper analysis by enabling versatile, semantic-based information retrieval and discovery through a semi-supervised knowledge extraction pipeline and interactive query system.

Contribution

The paper presents a novel semantic knowledge graph framework for academic literature, integrating entity extraction, normalization, and an ontology to support flexible semantic queries.

Findings

Effective knowledge extraction from research abstracts

Supports diverse semantic queries over academic corpora

Demonstrated usefulness with real-world visualization cases

Abstract

The number of published research papers has experienced exponential growth in recent years, which makes it crucial to develop new methods for efficient and versatile information extraction and knowledge discovery. To address this need, we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts from abstracts and other meta-information to represent the corpus. The SKG can support various semantic queries in academic literature thanks to the high diversity and rich information content stored within. To extract knowledge from unstructured text, we develop a Knowledge Extraction Module that includes a semi-supervised pipeline for entity extraction and entity normalization. We also create an ontology to integrate the concepts with other meta information, enabling us to build the SKG. Furthermore, we design and develop a dataflow system that demonstrates how to conduct various semantic queries flexibly and interactively over the SKG. To demonstrate the effectiveness of our approach, we conduct the research based on the visualization literature and provide real-world use cases to show the usefulness of the SKG. The dataset and codes for this work are available at https://osf.io/aqv8p/?view_only=2c26b36e3e3941ce999df47e4616207f.