Learning Triple Embeddings from Knowledge Graphs

Authors

  • Valeria Fionda University of Calabria
  • Giuseppe Pirrò Sapienza University of Rome

DOI:

https://doi.org/10.1609/aaai.v34i04.5800

Abstract

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.

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Published

2020-04-03

How to Cite

Fionda, V., & Pirrò, G. (2020). Learning Triple Embeddings from Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3874-3881. https://doi.org/10.1609/aaai.v34i04.5800

Issue

Section

AAAI Technical Track: Machine Learning