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Embedding Entities and Relations for Learning and Inference in Knowledge Bases

A survey on approaches to learning first-order logic rules over knowledge graphs.

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Embedding Entities and Relations for Learning and Inference in Knowledge Bases

By Wu, Hong, Wen-tau Yih, Wang, Kewen, Omran, Pouya Ghiasnezhad, Li, JiangmengarXiv (Cornell University)
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This paper reviews state-of-the-art systems for learning first-order logic rules over knowledge graphs. It conducts a comparative analysis of various approaches, including ILP-based, statistical path generalisation, and neuro-symbolic methods.

The authors highlight important application scenarios of rule learning in knowledge graph completion, fact checking, and other research areas.

Abstract

This paper reviews state-of-the-art systems for learning first-order logic rules over knowledge graphs. It conducts a comparative analysis of various approaches, including ILP-based, statistical path generalisation, and neuro-symbolic methods. The authors highlight important application scenarios of rule learning in knowledge graph completion, fact checking, and other research areas.

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rule learningknowledge graphsfirst-order logicinductive logic programmingneuro-symbolic methodsKnowledge GraphsOntology & TaxonomyStructured ContentAI Agents
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Embedding Entities and Relations for Learning and Inference in Knowledge Bases | Aramai