Geometric Relational Embeddings

• URL: http://arxiv.org/abs/2409.15369v1
• Date: Wed, 18 Sep 2024 22:02:24 GMT
• Title: Geometric Relational Embeddings
• Authors: Bo Xiong,
• Abstract summary: We propose relational embeddings, a paradigm of embeddings that respect the underlying symbolic structures. Results obtained from benchmark real-world datasets demonstrate the efficacy of geometric relational embeddings.
• Score: 19.383110247906256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational representation learning transforms relational data into continuous and low-dimensional vector representations. However, vector-based representations fall short in capturing crucial properties of relational data that are complex and symbolic. We propose geometric relational embeddings, a paradigm of relational embeddings that respect the underlying symbolic structures. Specifically, this dissertation introduces various geometric relational embedding models capable of capturing: 1) complex structured patterns like hierarchies and cycles in networks and knowledge graphs; 2) logical structures in ontologies and logical constraints applicable for constraining machine learning model outputs; and 3) high-order structures between entities and relations. Our results obtained from benchmark and real-world datasets demonstrate the efficacy of geometric relational embeddings in adeptly capturing these discrete, symbolic, and structured properties inherent in relational data.

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