Citation Recommendation for Research Papers via Knowledge Graphs

TL;DR

This paper introduces a novel citation recommendation method that leverages research knowledge graphs to improve the relevance of suggested citations, outperforming existing approaches in accuracy.

Contribution

It proposes integrating research knowledge graphs with traditional methods for citation recommendation, demonstrating improved performance across multiple scientific domains.

Findings

Outperforms state-of-the-art with 20.6% MAP for top-50 results

Combining knowledge graphs with text-based methods enhances recommendation quality

Effective across ten scientific domains

Abstract

Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KG) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results.