Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

Authors

  • Bhushan Kotnis NEC Laboratories Europe
  • Carolin Lawrence NEC Laboratories Europe
  • Mathias Niepert NEC Laboratories Europe

DOI:

https://doi.org/10.1609/aaai.v35i6.16630

Keywords:

Neuro-Symbolic AI (NSAI), Linked Open Data, Knowledge Graphs & KB Completio, Relational Learning

Abstract

Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of predicting the answers of conjunctive queries with multiple missing entities. We propose Bidirectional Query Embedding (BiQE), a method that embeds conjunctive queries with models based on bi-directional attention mechanisms. Contrary to prior work, bidirectional self-attention can capture interactions among all the elements of a query graph. We introduce two new challenging datasets for studying conjunctive query inference and conduct experiments on several benchmark datasets that demonstrate BiQE significantly outperforms state of the art baselines.

Downloads

Published

2021-05-18

How to Cite

Kotnis, B., Lawrence, C., & Niepert, M. (2021). Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 4968-4977. https://doi.org/10.1609/aaai.v35i6.16630

Issue

Section

AAAI Technical Track Focus Area on Neuro-Symbolic AI