Knowledge Graph Question Answering (KGQA) involves retrieving facts from a
Knowledge Graph (KG) using natural language queries. A KG is a curated set of
facts consisting of entities linked by relations. Certain facts include also
temporal information forming a Temporal KG (TKG). Although many natural
questions involve explicit or implicit time constraints, question answering
(QA) over TKGs has been a relatively unexplored area. Existing solutions are
mainly designed for simple temporal questions that can be answered directly by
a single TKG fact. This paper puts forth a comprehensive embedding-based
framework for answering complex questions over TKGs. Our method termed temporal
question reasoning (TempoQR) exploits TKG embeddings to ground the question to
the specific entities and time scope it refers to. It does so by augmenting the
question embeddings with context, entity and time-aware information by
employing three specialized modules. The first computes a textual
representation of a given question, the second combines it with the entity
embeddings for entities involved in the question, and the third generates
question-specific time embeddings. Finally, a transformer-based encoder learns
to fuse the generated temporal information with the question representation,
which is used for answer predictions. Extensive experiments show that TempoQR
improves accuracy by 25--45 percentage points on complex temporal questions
over state-of-the-art approaches and it generalizes better to unseen question
types.

本文提出了一种基于嵌入感知的方法，名为 'Temporal Question Reasoning' (TempoQR)，用于回答复杂的基于时间的知识图谱问答。实验表明，TempoQR 在复杂的时间问题上比现有的先进方法提高了 25-45 个百分点，并且更好地泛化到看不见的问题类型。