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A Dataset for Event-Centric Question Answering Over Knowledge Graphs

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A Dataset for Event-Centric Question Answering Over Knowledge Graphs Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs Tarcísio Souza Costa, Simon Gottschalk, Elena Demidova L3S Research Center, Leibniz Universität Hannover, Hannover, Germany {souza,gottschalk,demidova}@L3S.de ABSTRACT expressions. This way, development and evaluation of QA systems Semantic Question Answering (QA) is a crucial technology to faci- concerning the event-centric questions are currently not adequately litate intuitive user access to semantic information stored in know- supported. ledge graphs. Whereas most of the existing QA systems and datasets In this paper, we introduce novel Event-QA (Event-Centric Ques- focus on entity-centric questions, very little is known about these tion Answering Dataset) dataset for training and evaluating QA systems’ performance in the context of events. As new event-centric systems on complex event-centric questions over knowledge graphs. knowledge graphs emerge, datasets for such questions gain impor- With complex, we mean that the intended SPARQL queries con- tance. In this paper, we present the Event-QA dataset for answering sist of more than one triple pattern, similar to [18]. The aims of event-centric questions over knowledge graphs. Event-QA contains Event-QA are to: 1) Provide a representative selection of relations 1000 semantic queries and the corresponding English, German and involving events in the knowledge graph, to ensure the diversity of Portuguese verbalizations for EventKG - an event-centric know- the resulting event-centric semantic queries. To achieve this goal, ledge graph with more than 970 thousand events. we approach the query generation via a random walk through the knowledge graph, starting from randomly selected relations. 2) En- KEYWORDS sure the quality of the natural language questions. To this extent, the resulting queries are manually translated into natural language Question Answering, Events, Knowledge Graphs, Dataset expressions in English, Portuguese and German. To the best of our knowledge, this is the first QA dataset focused on event-centric Resource type: Dataset questions so far, and the only QA dataset that targets EventKG. Resource DOI: 10.5281/zenodo.3568387 The contributions of this paper are as follows: Permanent URL: http://eventcqa.l3s.uni-hannover.de • We propose an approach for an automatic generation of semantic queries for an event-centric QA dataset from a 1 INTRODUCTION knowledge graph. • We present the Event-QA dataset containing 3000 event- Knowledge graphs (KGs) with popular examples including DBpedia centric natural language questions (i.e. 1000 in each lan- [10], Wikidata [21], YAGO [12] and EventKG [5, 6] have recently guage) with the corresponding SPARQL interpretations for evolved as an important source of semantic information on the Web. the EventKG knowledge graph. These questions exhibit a Semantic Question Answering (QA) is a key technology to facilitate variety of SPARQL queries and their verbalizations. To facili- intuitive access to knowledge graphs for end-users via natural tate an easier integration with the existing QA pipelines, we language (NL) interfaces. QA systems automatically translate a also provide a translation of the SPARQL queries in Event-QA user question expressed in a natural language into the SPARQL to DBpedia, where possible. query language to query knowledge graphs [3, 18]. In recent years, • We provide an open source, extensible framework for auto- researchers have developed a large variety of QA approaches [7]. matic dataset generation to facilitate dataset maintenance QA datasets play an essential role in the development and evalua- and updates. tion of QA systems [3, 19]. The active development of QA datasets The remainder of the paper is organised as follows: In Section 2, arXiv:2004.11861v2 [cs.CL] 5 Aug 2020 supports the advancement of QA technology, for example, through the QALD (Question Answering over Linked Data) initiative1. As we discuss the motivation for an event-centric QA dataset. Then, in popular knowledge graphs are mostly entity-centric, they do not Section 3, we present the problem of the event-centric QA dataset sufficiently cover events and temporal relations5 [ ]. As a conse- generation. In Section 4, we describe the corresponding approach quence, existing QA datasets, with recent examples including LC- to generate an event-centric QA dataset from a knowledge graph. QuAD [18], LC-QuAD 2.0 [3] and the QALD challenges, mainly Following that, in Section 5, we provide a brief overview of EventKG focus on entity-centric queries. More recently, event-centric know- and the application of the proposed approach to this knowledge ledge graphs such as EventKG [5] that includes historical and im- graph. Section 6 discusses the characteristics of the resulting Event- portant contemporary events as well as knowledge graphs that QA dataset, and Section 7 summarises the availability aspects. Then, contain daily happenings extracted from the news (e.g., [9, 14]) Section 8 gives an overview of the related work. Finally, Section 9 evolved. However, there is a lack of QA datasets dedicated to event- provides a conclusion. centric questions and only a few datasets (e.g., TempQuestions [8] and the dataset proposed by Saquete et al. [15]) focus on temporal 2 RELEVANCE Relevance to the research community and society: Question Answer- 1Question Answering over Linked Data: http://qald.aksw.org ing (QA) [7] is a crucial technology to provide intuitive end-users ©2020 Copyright held by the authors. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in the Proceedings of the 29th ACM International Conference on Information and Knowledge Management, https://doi.org/10.1145/3340531.3412760. Tarcísio Souza Costa, Simon Gottschalk, Elena Demidova access to semantic data. Research on the development of QA appli- Definition 3.1. Knowledge Graph. A knowledge graph KG is cations is of interest to several scientific communities, including a labelled multi-graph KG = ¹V ; Rv ; Rl ; Lº. V = Ev [ En is a set of Semantic Web, Natural Language Processing, and Human Computer nodes in KG. The set Ev represents real-world events. The set En Interaction [16]. Semantic QA approaches automatically translate represents real-world entities. L is a set of literals. Rv and Rl are user queries posed in a natural language into structured queries, sets of edges. An edge in Rv connects two nodes in V and an edge e.g., in the SPARQL query language. Whereas current research in Rl connects a node in V with a literal in L. mostly focuses on entity-centric questions, events are still under- Following this definition, an event is represented as a node in represented in the semantic sources and the corresponding QA E in a knowledge graph. Literals represent specific properties of datasets. v events and entities, e.g., the start time of the name. Relevance for Question Answering applications: Event-centric se- mantic reference sources are still rare, with the first event-centric Definition 3.2. Relation. Rel ⊆ V × Rv × V denotes the set of knowledge graphs such as EventKG evolving only recently. Often, real-world relations between events and entities in a knowledge event-centric information spread across entity-centric knowledge graph KG. graphs is less annotated and more complex than the entity-centric information and is more difficult to retrieve. As a consequence, exist- Relation examples include sub-event relations (e.g., the "2018 ing QA datasets such as LC-QuAD [3, 18] and QALD are mainly FIFA World Cup Final" and the "2018 FIFA World Cup"), as well as entity-centric. Specialized event-centric QA datasets are currently relations connecting an event to its participants (e.g., the "French non-existing. The provision of such resources can bring a novel per- National football team" and the "2018 FIFA World Cup Final"). spective in the Question Answering research and facilitate further A query graph is a subgraph of the knowledge graph. A query development of QA approaches in the context of events. graph includes a subset of nodes and edges of the knowledge graph, Impact on the adoption of semantic technologies: Event-centric as well as a set of variables representing such nodes and edges. As information is of crucial importance for researchers and practi- the focus of this work is on event-centric questions, at least one tioners in various domains, including journalists, media analysts, node in the query graph represents an event. and Digital Humanities researchers. Current and historical events ¹ 0; 0 ; 0; 0; º Definition 3.3. Query graph. A query graphq = V Rv Rl L U of global importance such as the COVID-19 outbreak, the Brexit, is a sub-graph of the knowledge graph KG = ¹V ; Rv ; Rl ; Lº, V = the Olympic Games and the US withdrawal from the nuclear arms [ 0 ⊂ 0 ⊂ 0 ⊂ 0 ⊂ Ev En, where V V , Rv Rv , Rl Rl , L L, and U is a set treaty with Russia are the subject of current research in several fields of variables. Each variable u 2 U maps to a node of the knowledge including Digital Humanities and Web Science (see, e.g., [4, 13]). graph KG. At least one node in the query graph represents an event: 0 0 0 0 0 0 Event-centric repositories and the corresponding QA systems can v 2 Ev : v 2 V _ u 2 U : u 7! v . help to answer relevant questions and support these studies. We be- 9 9 lieve that the provision of intuitive access methods to the semantic A semantic query q includes a query graph, a query type, and reference sources can facilitate the broader adoption of semantic optionally a set of constraints. The query type represents a projec- technologies by researchers and practitioners in these fields. tion operator such as SELECT and ASK or an aggregation operator such as COUNT.
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