Theoretical framework demonstrates neuro-symbolic integration for knowledge graph completion, highlighting enhanced interpretability, robustness, and data efficiency.
The goal of this thesis is to address knowledge graph completion tasks using neuro-symbolic methods. Neuro-symbolic methods allow the joint utilization of symbolic information defined as meta-rules in ontologies and knowledge graph embedding methods that represent entities and relations of the graph in a low-dimensional vector space. This approach has the potential to improve the resolution of knowledge graph completion tasks in terms of reliability, interpretability, data-efficiency and robustness.
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Luisa Werner (2024) studied this question.
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