Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1867 - 1877 December 7-11, 2022 ©2022 Association for Computational Linguistics Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs Wentao Ding, Hao Chen, Huayu Li, Yuzhong Qu State Key Laboratory for Novel Software Technology, Nanjing University, China {wtding, haochen97}@smail.nju.edu.cn, lihuayuu@gmail.com, yzqu@nju.edu.cn Abstract Answering factual questions with temporal in- tent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic con- nections between events can make them tem- porally related, which may limit their capabil- ity. We systematically analyze the possible interpretation of temporal constraints and con- clude the interpretation structures as the Seman- tic Framework of Temporal Constraints, SF- TCons. Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by ex- ploring the relevant facts of mentioned entities, where the exploring process is restricted by SF- TCons. Our evaluations show that SF-TQA sig- nificantly outperforms existing methods on two benchmarks over different knowledge graphs. 1 Introduction With the rapid growth of knowledge graphs, tem- poral question answering over knowledge graphs (temporal KGQA) is attracting rising attention in recent years (Jia et al., 2018b, 2021). In tempo- ral KGQA, a common phenomenon is that ques- tions express temporal relations between events or time expressions, while knowledge graphs de- scribe the facts resulting from each event. Existing methods handle the heterogeneity between natural language and knowledge graph representation in two ways. Some systems express temporal intents by constructing executable queries, some apply time-sensitive neural models to rank candidate an- swers. Considering that neural models are difficult to characterize the clear boundaries of concepts (e.g., exactly filter all events that occur “before 2022”), this paper focuses on generating queries that correspond to the meaning of questions. From the logic perspective, formulated queries are actually logical restrictions about KG facts. The answers to a question is a set of KG objects, each of which satisfies the corresponding logical restric- tions. In previous studies (e.g., Jia et al., 2018b), temporal intents are converted into restrictions over KG facts with quantitative time values. Example 1 illustrates a typical conversion from a temporal question to such restriction. Example 1. “Who was the president of the U.S. when John Lennon was shot?” The corresponding query on Wikidata can be formulated as the following logical restriction: T1 = time(position_held(ANS, U.S._president)) ∧ T2 = time(Murder_of_John_Lennon) ∧ OVERLAPS(T1, T2). However, the idea of constructing queries with quantitative restrictions can not exhaust all possible scenarios. As illustrated in Example 2, facts with time values are not a necessary premise to introduce a temporal relation. Example 2. “Where was John Lennon standing when he was shot?” To construct a comparison restriction, we need to enumerate the “standing” of J.L. (i.e. all the experiences of his life). The enumeration is hard to implement and might introduce errors.1 In fact, the temporal intent does …

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