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Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involvesretrieving nodes from a knowledge graph (KG) to answer natural languagequestions. Recent GNN-based approaches formulate this task as a KG pathsearching problem, where messages are sequentially propagated from the seednode towards the answer nodes. However, these messages are past-oriented, andthey do not consider the full KG context. To make matters worse, KG nodes oftenrepresent proper noun entities and are sometimes encrypted, being uninformativein selecting between paths. To address these problems, we propose Neural TreeSearch (NuTrea), a tree search-based GNN model that incorporates the broader KGcontext. Our model adopts a message-passing scheme that probes the unreachedsubtree regions to boost the past-oriented embeddings. In addition, weintroduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) nodeembedding that considers the global KG context to better characterize ambiguousKG nodes. The general effectiveness of our approach is demonstrated throughexperiments on three major multi-hop KGQA benchmark datasets, and our extensiveanalyses further validate its expressiveness and robustness. Overall, NuTreaprovides a powerful means to query the KG with complex natural languagequestions. Code is available at https://github.com/mlvlab/NuTrea.

NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA