Explainable Representations for Relation Prediction in Knowledge Graphs

• URL: http://arxiv.org/abs/2306.12687v1
• Date: Thu, 22 Jun 2023 06:18:40 GMT
• Title: Explainable Representations for Relation Prediction in Knowledge Graphs
• Authors: Rita T. Sousa, Sara Silva, Catia Pesquita
• Abstract summary: We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects between entities and learning representations for each subgraph. We evaluate SEEK on two real-world relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourhood properties, but sacrifice explainability. However, in tasks such as link or relation prediction, understanding which specific features better explain a relation is crucial to support complex or critical applications. We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects (i.e., subgraphs) between entities and learning representations for each subgraph, producing a multi-faceted and explainable representation. We evaluate SEEK on two real-world highly complex relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction. Our extensive analysis using established benchmarks demonstrates that SEEK achieves significantly better performance than standard learning representation methods while identifying both sufficient and necessary explanations based on shared semantic aspects.

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