Universal Knowledge Graph Embeddings

TL;DR

This paper introduces universal knowledge graph embeddings that integrate multiple large-scale knowledge sources, enabling cross-graph entity comparison and improving semantic encoding for applications like entity disambiguation.

Contribution

We propose a method to fuse large knowledge graphs into universal embeddings, supporting cross-graph tasks and providing a scalable API for practical use.

Findings

Universal embeddings encode better semantics than single-graph embeddings.

Our embeddings cover 180 million entities and 1.2 billion triples.

Experiments show improved link prediction performance.

Abstract

A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect only the structure of a single knowledge graph, and embeddings for different knowledge graphs are not aligned, e.g., they cannot be used to find similar entities across knowledge graphs via nearest neighbor search. However, knowledge graph embedding applications such as entity disambiguation require a more global representation, i.e., a representation that is valid across multiple sources. We propose to learn universal knowledge graph embeddings from large-scale interlinked knowledge sources. To this end, we fuse large knowledge graphs based on the owl:sameAs relation such that every entity is represented by a unique identity. We instantiate our idea by computing universal embeddings based on DBpedia and Wikidata yielding embeddings for about 180 million entities, 15 thousand relations, and 1.2 billion triples. We believe our computed embeddings will support the emerging field of graph foundation models. Moreover, we develop a convenient API to provide embeddings as a service. Experiments on link prediction suggest that universal knowledge graph embeddings encode better semantics compared to embeddings computed on a single knowledge graph. For reproducibility purposes, we provide our source code and datasets open access.