Interpreting Knowledge Graph Relation Representation from Word EmbeddingsDownload PDF

Published: 12 Jan 2021, Last Modified: 05 May 2023ICLR 2021 PosterReaders: Everyone
Keywords: knowledge graphs, word embedding, representation learning
Abstract: Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-specific mapping. Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained. Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations. We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justified by our analysis.
One-sentence Summary: Interpreting the structure of knowledge graph relation representation using insight from word embeddings.
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Data: [NELL](, [NELL-995](, [WN18](, [WN18RR](
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