ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals

• URL: http://arxiv.org/abs/2504.00852v1
• Date: Tue, 01 Apr 2025 14:38:22 GMT
• Title: ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals
• Authors: Antonis Klironomos, Baifan Zhou, Zhuoxun Zheng, Gad-Elrab Mohamed, Heiko Paulheim, Evgeny Kharlamov,
• Abstract summary: Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph.<n>We propose ReaLitE, a novel relation-centric KGE model that dynamically aggregates and merges entities' numerical attributes with the embeddings of the connecting relations.
• Score: 6.014443576489523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph, giving little attention to other literal values, which might encode important information. Therefore, some literal-aware KGE models attempt to either integrate numerical values into the embeddings of the entities or convert these numerics into entities during preprocessing, leading to information loss. Other methods concerned with creating relation-specific numerical features assume completeness of numerical data, which does not apply to real-world graphs. In this work, we propose ReaLitE, a novel relation-centric KGE model that dynamically aggregates and merges entities' numerical attributes with the embeddings of the connecting relations. ReaLitE is designed to complement existing conventional KGE methods while supporting multiple variations for numerical aggregations, including a learnable method. We comprehensively evaluated the proposed relation-centric embedding using several benchmarks for link prediction and node classification tasks. The results showed the superiority of ReaLitE over the state of the art in both tasks.

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