Dhruv Gupta
Klaus Berberich
ABSTRACT
Knowledge graphs capture very little temporal information associated with facts. In this work, we address the problem of identifying time intervals of knowledge graph facts from large document collections annotated with temporal expressions. Prior approaches in this direction have leveraged limited metadata associated with documents in large collections (e.g., publication dates) or have limited techniques to model the uncertainty and dynamics of temporal expressions. Our approach to identify time intervals for time-sensitive facts in knowledge graphs leverages a time model that incorporates uncertainty and models them at different levels of granularity (i.e., day, month, and year). Evaluation on a temporal fact benchmark using two large news archives amounting to more than eleven million documents show the quality of our results.
- K. Berberich et al. A Language Modeling Approach for Temporal Information Needs ECIR 2010. Google Scholar
Digital Library
- D. Gerber et al. DeFacto-Temporal and Multilingual Deep Fact Validation. Web Semantics, Vol. 35, P2 (Dec. 2015). Google ScholarDigital Library
- D. Gupta and K. Berberich. Identifying Time Intervals of Interest to Queries. CIKM 2014. Google ScholarDigital Library
- E. Kuzey et al. As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes WWW 2016. Google ScholarDigital Library
- C. D. Manning et al. The Stanford CoreNLP Natural Language Processing Toolkit ACL 2014.Google Scholar
- P. P. Talukdar et al. Coupled temporal scoping of relational facts. In WSDM 2012. Google ScholarDigital Library
Index Terms
- Identifying Time Intervals for Knowledge Graph Facts
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