Multi-granularity Temporal Question Answering over Knowledge Graphs (2023)

Abstract: Recently, question answering over temporal knowledge graphs (i.e., TKGQA) has been introduced and investigated, in quest of reasoning about dynamic factual knowledge. To foster research on TKGQA, a few datasets have been curated (e.g., CronQuestions and Complex-CronQuestions), and various models have been proposed based on these datasets. Nevertheless, existing efforts overlook the fact that real-life applications of TKGQA also tend to be complex in temporal granularity, i.e., the questions may concern mixed temporal granularities (e.g., both day and month). To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answering over knowledge graphs and present a large scale dataset for multi-granularity TKGQA, namely MultiTQ. To the best of our knowledge, MultiTQis among the first of its kind, and compared with existing datasets on TKGQA, MultiTQfeatures at least two desirable aspects—ample relevant facts and multiple temporal granularities. It is expected to better reflect real-world challenges, and serve as a test bed for TKGQA models. In addition, we propose a competing baseline MultiQA over MultiTQ, which is experimentally demonstrated to be effective in dealing with TKGQA. The data and code are released at https://github.com/czy1999/MultiTQ .

## introduction in real-life applications factual knowledge is apt to evolve over time @xcite @xcite ; for instance, the host city of the winter olympic games in 2018 was south korea, while in 2022 it was beijing.in this connection, there is a current trend to investigate knowledge graphs (kgs) involving time, and these kgs are coined as temporal knowledge graphs (tkgs). in a tkg, fact triplets are equipped with temporal information (e.g., timestamps), and a temporal fact can be stated in the form like "(beijing, held, winter olympic games, 2022)". to exploit the value of tkgs, recent research effort has been devoted to process natural language questions over tkg, i.e., question answering over tkg (tkgqa in short) @xcite . given a question and a background tkg, it retrieves from the tkg an answer to the question. to foster research on tkgqa, several datasets have been introduced, among which cronques-tions @xcite is by far the largest. we explain the task with a sample question in cron-questions. example 1 in the upper part of figure 1 , the agent is supplied with the question "what award did carlo taverna receive in 1863?" as well as a tkg. by considering the semantic relevance of the facts, the agent locates the candidate fact "(carlo taverna, receive, order of saints maurice and lazarus, 1863)", and hence, order of saints maurice and lazarus is returned as the answer. specifically, cronquestions comprises 410k temporal questions, each of them has a temporal constraint, e.g., "in 1863" in the example above. albeit large scale, the questions in cronques-tions tend to be "pseudo-temporal" @xcite . by looking into the construction of cron-questions, we find that most of the questions are related to, respectively, only one fact, which can be well located without enforcing the temporal constraint in the question; for example, carlo taverna only received one award, which was order of saints maurice and lazarus. in this case, the temporal constraint does not further restrict the candidate facts to answering the question, and the question is essentially atemporal in the context of the given kg. moreover, in cronques-tions, questions and the tkg are designed to be both described in the temporal granularity of year. this simplification, however, is less practical, since questions and knowledge in the real world are not limited to the time frame of years. for instance, as shown in the lower part of figure 1 , the agent is likely to be given a question in the granularity of month, which is common in the real world. in short, these two important aspects are not well attended by existing tkgqa datasets, which thus may be insufficient in evaluating tkgqa models. in this research, we are motivated to address the shortcomings by presenting a new dataset for tkgqa, namely multitq. multitq is a largescale dataset featuring ample relevant facts and multiple temporal granularities (comparison of statistics in in summary, our contribution is three-fold: ## experiments we experimentally evaluates multiqa against five baselines. in the interest of space, experiment settings are in appendix a.6. ## conclusion and limitation in this paper, we introduce the concept of multigranularity temporal question answering and construct a benchmark dataset multitq, which features ample relevant facts and multiple temporal granularities. we also propose a multi-granularity temporal question answering model multiqa, serving as a strong baseline for follow-up research. limitation. the main drawback of our data creation protocol is that the question/answer pairs were generated automatically, leading the question distribution to be artificial from a semantic perspective. in addition, the kg adopted in the research focuses on a single event domain, and extending the dataset to multiple domains is planned as future work. as the expression of time is more fixed, we adopt a rule-based method to extract time information from the question text for subsequent reasoning. specifically, a series of regular expressions based on common time formats have written to extract the corresponding time information in the question.