On the SPOT: Question Answering over Temporally Enhanced Structured Data
Mohamed Yahya, Klaus Berberich, Maya Ramanath, Gerhard Weikum · Max Planck Digital Library · 2013
Natural-language question answering is a convenient way for humans to discover relevant information in structured Web data such as knowledge bases or Linked Open Data sources. This paper focuses on data with a temporal dimension, and discusses the problem of mapping natural-language questions into extended SPARQL queries over RDF-structured data. We specifically address the issue of disambiguating temporal phrases in the question into temporal entities like dates and named events and temporal predicates. For the situation where the data has only partial coverage of the time dimension but is augmented with textual descriptions of entities and facts, we also discuss how to generate queries that combine structured search with keyword conditions.