SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction

TL;DR

This paper proposes a novel Schema-augmented Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction, which first exploit network schema as the prior constraint to sample negatives and pre-train the model by employing a multi- level contrastive learning method to yield both prior schema and contextual information.

Abstract

Link prediction is the task of inferring missing links between entities in knowledge graphs. Embedding-based methods have shown effectiveness in addressing this problem by modeling relational patterns in triples. However, the link prediction task often requires contextual information in entity neighborhoods, while most existing embedding-based methods fail to capture it. Additionally, little attention is paid to the diversity of entity representations in different contexts, which often leads to false prediction results. In this situation, we consider that the schema of knowledge graph contains the specific contextual information, and it is beneficial for preserving the consistency of entities across contexts. In this paper, we propose a novel Schema-augmented Multi-level contrastive LEarning framework (SMiLE) to conduct knowledge graph link prediction. Specifically, we first exploit network schema as the prior constraint to sample negatives and pre-train our model by employing a multi-level contrastive learning method to yield both prior schema and contextual information. Then we fine-tune our model under the supervision of individual triples to learn subtler representations for link prediction. Extensive experimental results on four knowledge graph datasets with thorough analysis of each component demonstrate the effectiveness of our proposed framework against state-of-the-art baselines. The implementation of SMiLE is available at https://github.com/GKNL/SMiLE.

Figures (6)

• Figure 1: An example of KG fragment. Nicole Kidman has two types Actress and Citizen, and each of them preserves different information in different contexts.
• Figure 2: Overall illustration of the proposed SMiLE model: detailed framework of SMiLE model(left) and a sketch map of multi-level contrastive learning mechanism(right).
• Figure 3: Histogram distribution of triple scores on FB15k and HumanWiki datasets.
• Figure 4: The visualization of entity embeddings on FB15k dataset using t-SNEt-SNE. Points in same color indicate that they are head entities connected to the same tail entity via a relation.
• Figure 5: Effects of balancing coefficient $\lambda$ on datasets FB15k and JF17k with SMiLE.