However, it moves the burden of dealing with huge amounts of data from the consumer to the publisher, who needs to serialize the text data into HDT.
This allows to reduce both the volume needed to store it and the speed at which it can be transferred or queried. HDT is a binary serialization of RDF that makes use of compact data structures, making it possible to publish and query highly compressed RDF data. RDF is usually serialized in verbose text formats, which is problematic for publishing and managing huge datasets. These questions were collected in a large scale user study and annotated semi-automatically, with appropriate measures to ensure the quality.ĭata generation in RDF has been increasing over the last years as a means to publish heterogeneous and interconnected data. 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.
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. 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. The task of factoid question answering (QA) faces new challenges when applied in scenarios with rapidly changing context information, for example on smartphones.
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