NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Hyeong Kyu Choi Seunghun Lee Jaewon Chu Hyunwoo J. Kim
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves
retrieving nodes from a knowledge graph (KG) to answer natural language
questions. Recent GNN-based approaches formulate this task as a KG path
searching problem, where messages are sequentially propagated from the seed
node towards the answer nodes. However, these messages are past-oriented, and
they do not consider the full KG context. To make matters worse, KG nodes often
represent proper noun entities and are sometimes encrypted, being uninformative
in selecting between paths. To address these problems, we propose Neural Tree
Search (NuTrea), a tree search-based GNN model that incorporates the broader KG
context. Our model adopts a message-passing scheme that probes the unreached
subtree regions to boost the past-oriented embeddings. In addition, we
introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node
embedding that considers the global KG context to better characterize ambiguous
KG nodes. The general effectiveness of our approach is demonstrated through
experiments on three major multi-hop KGQA benchmark datasets, and our extensive
analyses further validate its expressiveness and robustness. Overall, NuTrea
provides a powerful means to query the KG with complex natural language
questions. Code is available at https://github.com/mlvlab/NuTrea.