MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion

TL;DR

A knowledge-aware reasoning model (MUSE), which designs a novel multi-knowledge representation learning mechanism for missing relation prediction and significantly outperforms other baselines on four public datasets.

Abstract

Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph aggregation. However, they do not fully explore the Knowledge Graph (KG) features and neglect the guidance of external semantic knowledge. To address these shortcomings, we propose a knowledge-aware reasoning model (MUSE), which designs a novel multi-knowledge representation learning mechanism for missing relation prediction. Our model develops a tailored embedding space through three parallel components: 1) Prior Knowledge Learning for enhancing the triplets' semantic representation by fine-tuning BERT; 2) Context Message Passing for enhancing the context messages of KG; 3) Relational Path Aggregation for enhancing the path representation from the head entity to the tail entity. The experimental results show that MUSE significantly outperforms other baselines on four public datasets, achieving over 5.50% H@1 improvement and 4.20% MRR improvement on the NELL995 dataset. The code and datasets will be released via https://github.com/SUSTech-TP/ADMA2024-MUSE.git.

Figures (6)

• Figure 1: Two Example Cases of Relation Prediction in the LIS and RIS Scenarios.
• Figure 2: The Architecture of MUSE Framework in Knowledge Graph Completion.
• Figure 3: Illustration of the Prior Knowledge Learning. We fine-tune BERT on the datasets: FB15k-237, WN18, WN18RR, and NELL995, respectively.
• Figure 4: Analysis of the Entity Description in Relation Prediction Task. We define the $\text{MUSE~w/o~Fine-Tuning}$ model as applying the BERT model directly in Figure \ref{['fig:pkla']}. As shown in Figure \ref{['fig:pklb']}, the $\text{MUSE w/o Attention}$ model aggregates entities without using attention mechanism in Equation \ref{['eq:attention']}.
• Figure 5: Comparison of MUSE and PathCon Performance on the NELL995.