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.

EXAQT 是第一个用于回答时间问题的端到端系统，可以为包含多个实体和谓词以及相关时间条件的复杂时间问题提供自然语言答案，并通过 Group Steiner Trees 和 fine-tuned BERT 模型以及时间感知的实体嵌入和关注时间关系的关系图卷积网络，提高 KG-QA 的效率和准确性。

知识图谱上的复杂时间问答

Complex Temporal Question Answering on Knowledge Graphs

Search systems are often focused on providing relevant results for the "now",
assuming both corpora and user needs that focus on the present. However, many
corpora today reflect significant longitudinal collections ranging from 20
years of the Web to hundreds of years of digitized newspapers and books.
Understanding the temporal intent of the user and retrieving the most relevant
historical content has become a significant challenge. Common search features,
such as query expansion, leverage the relationship between terms but cannot
function well across all times when relationships vary temporally. In this
work, we introduce a temporal relationship model that is extracted from
longitudinal data collections. The model supports the task of identifying,
given two words, when they relate to each other. We present an algorithmic
framework for this task and show its application for the task of query
expansion, achieving high gain.

通过抽取一种从纵向数据集中提取出的时间关系模型，该篇论文为查询扩展任务提供了一种算法框架，以实现对搜索系统中的历史内容的更精确的识别和检索。