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CASQAD - A New Dataset for Context-Aware Spatial Question Answering

: Rose, J.; Lehmann, J.


Pan, J.Z.:
The Semantic Web - ISWC 2020. 19th International Semantic Web Conference. Proceedings. Pt.II : Athens, Greece, November 2-6, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12507)
ISBN: 978-3-030-62465-1 (Print)
ISBN: 978-3-030-62466-8 (Online)
ISBN: 978-3-030-62467-5
International Semantic Web Conference (ISWC) <19, 2020, Online>
Fraunhofer IAIS ()

The task of factoid question answering (QA) faces new challenges when applied in scenarios with rapidly changing context information, for example on smartphones. Instead of asking who the architect of the “Holocaust Memorial” in Berlin was, the same question could be phrased as “Who was the architect of the many stelae in front of me?” presuming the user is standing in front of it. While traditional QA systems rely on static information from knowledge bases and the analysis of named entities and predicates in the input, question answering for temporal and spatial questions imposes new challenges to the underlying methods. To tackle these challenges, we present the Context-aware Spatial QA Dataset (CASQAD) with over 5,000 annotated questions containing visual and spatial references that require information about the user’s location and moving direction to compose a suitable query. These questions were collected in a large scale user study and annotated semi-automatically, with appropriate measures to ensure the quality.