Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task

TL;DR

This paper introduces KG-HAIT, a human-AI collaborative system that enhances knowledge graph embedding for link prediction by incorporating human-designed features, leading to improved accuracy and faster convergence.

Contribution

It presents a novel human-AI team framework that integrates human insights via dynamic programming into KGE models, significantly boosting performance.

Findings

Improved link prediction accuracy across benchmarks.

Faster convergence of KGE models with human-AI collaboration.

Effective use of human-designed features in knowledge graph embedding.

Abstract

Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the rapidly growing literature, insufficient attention has been paid to the cooperation between humans and AI on KG. However, humans' capability to analyze graphs conceptually may further improve the efficacy of KGE models with semantic information. To this effect, we carefully designed a human-AI team (HAIT) system dubbed KG-HAIT, which harnesses the human insights on KG by leveraging fully human-designed ad-hoc dynamic programming (DP) on KG to produce human insightful feature (HIF) vectors that capture the subgraph structural feature and semantic similarities. By integrating HIF vectors into the training of KGE models, notable improvements are observed across various benchmarks and metrics, accompanied by accelerated model convergence. Our results underscore the effectiveness of human-designed DP in the task of LP, emphasizing the pivotal role of collaboration between humans and AI on KG. We open avenues for further exploration and innovation through KG-HAIT, paving the way towards more effective and insightful KG analysis techniques.