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Abstract

Question answering over knowledge graphs (KG-QA) is a vital topic in IR.
Questions with temporal intent are a special class of practical importance, but
have not received much attention in research. This work presents EXAQT, the
first end-to-end system for answering complex temporal questions that have
multiple entities and predicates, and associated temporal conditions. EXAQT
answers natural language questions over KGs in two stages, one geared towards
high recall, the other towards precision at top ranks. The first step computes
question-relevant compact subgraphs within the KG, and judiciously enhances
them with pertinent temporal facts, using Group Steiner Trees and fine-tuned
BERT models. The second step constructs relational graph convolutional networks
(R-GCNs) from the first step's output, and enhances the R-GCNs with time-aware
entity embeddings and attention over temporal relations. We evaluate EXAQT on
TimeQuestions, a large dataset of 16k temporal questions we compiled from a
variety of general purpose KG-QA benchmarks. Results show that EXAQT
outperforms three state-of-the-art systems for answering complex questions over
KGs, thereby justifying specialized treatment of temporal QA.