License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICLP.2017.4
URN: urn:nbn:de:0030-drops-84612
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Chekol, Melisachew Wudage ; Stuckenschmidt, Heiner

Rule Based Temporal Inference

OASIcs-ICLP-2017-4.pdf (1 MB)


Time-wise knowledge is relevant in knowledge graphs as the majority facts are true in some time period, for instance, (Barack Obama, president of, USA, 2009, 2017). Consequently, temporal information extraction and
temporal scoping of facts in knowledge graphs have been a focus of recent research. Due to this, a number of temporal knowledge graphs have become available such as YAGO and Wikidata. In addition, since the temporal facts are obtained from open text, they can be weighted, i.e., the extraction tools assign each fact with a confidence score indicating how likely that fact is to be true. Temporal facts coupled with confidence scores result in a probabilistic temporal knowledge graph. In such a graph, probabilistic query evaluation (marginal inference) and computing most probable explanations (MPE inference) are fundamental problems. In addition, in these problems temporal coalescing, an important research in temporal databases, is very challenging. In this work, we study these problems by using probabilistic programming. We report experimental results comparing the efficiency of several state of the art systems.

BibTeX - Entry

  author =	{Melisachew Wudage Chekol and Heiner Stuckenschmidt},
  title =	{{Rule Based Temporal Inference}},
  booktitle =	{Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017)},
  pages =	{4:1--4:14},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-058-3},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{58},
  editor =	{Ricardo Rocha and Tran Cao Son and Christopher Mears and Neda Saeedloei},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-84612},
  doi =		{10.4230/OASIcs.ICLP.2017.4},
  annote =	{Keywords: temporal inference, temporal knowledge graphs, probabilistic temporal reasoning}

Keywords: temporal inference, temporal knowledge graphs, probabilistic temporal reasoning
Collection: Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017)
Issue Date: 2018
Date of publication: 14.02.2018

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