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Poster
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Zhaocheng Zhu · Mikhail Galkin · Zuobai Zhang · Jian Tang

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #432
in Poster Session 1 »

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides good interpretation for each step. Recent neural methods learn geometric embeddings for complex queries. These methods can generalize to incomplete knowledge graphs, but their reasoning process is hard to interpret. In this paper, we propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets, which provides interpretability for intermediate variables. To reason about the missing links, GNN-QE adapts a graph neural network from knowledge graph completion to execute the relation projections, and models the logical operations with product fuzzy logic. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries. Meanwhile, GNN-QE can predict the number of answers without explicit supervision, and provide visualizations for intermediate variables.

Keywords: APP: Everything Else · DL: Graph Neural Networks
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Author Information

Zhaocheng Zhu (Mila - Quebec AI Institute)
Mikhail Galkin (Mila, McGill University)
Zuobai Zhang (Mila)
Jian Tang (HEC Montreal & MILA)

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