Neural-Symbolic Models for Logical Queries on Knowledge Graphs. (arXiv:2205.10128v1 [cs.AI])

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 …

ai arxiv graphs knowledge knowledge graphs