The Solution for The PST-KDD-2024 OAG-Challenge

TL;DR

This paper presents a second-place solution for the KDD-2024 OAG-Challenge, combining BERT and GCN models to improve paper source tracing accuracy through refined text processing and semantic graph construction.

Contribution

The paper introduces a novel hybrid approach using BERT and GCN to enhance source tracing in academic papers, with specific techniques for fragment refinement and semantic graph building.

Findings

Achieved a score of 0.47691 in the competition

Effectively combined BERT and GCN for improved performance

Refined fragment processing enhances BERT's accuracy

Abstract

In this paper, we introduce the second-place solution in the KDD-2024 OAG-Challenge paper source tracing track. Our solution is mainly based on two methods, BERT and GCN, and combines the reasoning results of BERT and GCN in the final submission to achieve complementary performance. In the BERT solution, we focus on processing the fragments that appear in the references of the paper, and use a variety of operations to reduce the redundant interference in the fragments, so that the information received by BERT is more refined. In the GCN solution, we map information such as paper fragments, abstracts, and titles to a high-dimensional semantic space through an embedding model, and try to build edges between titles, abstracts, and fragments to integrate contextual relationships for judgment. In the end, our solution achieved a remarkable score of 0.47691 in the competition.