Rule Based Temporal Inference

Authors Melisachew Wudage Chekol, Heiner Stuckenschmidt

Thumbnail PDF


  • Filesize: 1.27 MB
  • 14 pages

Document Identifiers

Author Details

Melisachew Wudage Chekol
Heiner Stuckenschmidt

Cite AsGet BibTex

Melisachew Wudage Chekol and Heiner Stuckenschmidt. Rule Based Temporal Inference. In Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017). Open Access Series in Informatics (OASIcs), Volume 58, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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.
  • temporal inference
  • temporal knowledge graphs
  • probabilistic temporal reasoning


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. Dbpedia: A nucleus for a web of open data. The semantic web, pages 722-735, 2007. Google Scholar
  2. Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. Hinge-loss markov random fields and probabilistic soft logic. arXiv:1505.04406 [cs.LG], 2015. Google Scholar
  3. Michael H. Böhlen, Richard T. Snodgrass, and Michael D. Soo. Coalescing in temporal databases. In VLDB'96, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India, pages 180-191, 1996. Google Scholar
  4. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka Jr, and Tom M Mitchell. Toward an architecture for never-ending language learning. In AAAI, volume 5, page 3, 2010. Google Scholar
  5. Melisachew Wudage Chekol, Giuseppe Pirrò, Joerg Schoenfisch, and Heiner Stuckenschmidt. Marrying uncertainty and time in knowledge graphs. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., pages 88-94, 2017. Google Scholar
  6. Yang Chen and Daisy Zhe Wang. Knowledge expansion over probabilistic knowledge bases. In SIGMOD, pages 649-660. ACM, 2014. Google Scholar
  7. Yang Chen, Daisy Zhe Wang, and Sean Goldberg. Scalekb: scalable learning and inference over large knowledge bases. The VLDB Journal, 25(6):893-918, 2016. Google Scholar
  8. Alex Dekhtyar, Robert Ross, and VS Subrahmanian. Probabilistic temporal databases, i: algebra. ACM Transactions on Database Systems (TODS), 26(1):41-95, 2001. Google Scholar
  9. Anton Dignös, Michael H Böhlen, and Johann Gamper. Temporal alignment. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pages 433-444. ACM, 2012. Google Scholar
  10. Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion. In SIGKDD, pages 601-610, 2014. Google Scholar
  11. Maximilian Dylla, Iris Miliaraki, and Martin Theobald. A temporal-probabilistic database model for information extraction. Proc. of the VLDB Endowment, 6(14):1810-1821, 2013. Google Scholar
  12. Maximilian Dylla, Iris Miliaraki, and Michael Theobald. Top-k query processing in probabilistic databases with non-materialized views. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 122-133. IEEE, 2013. Google Scholar
  13. Maximilian Dylla, Mauro Sozio, and Martin Theobald. Resolving Temporal Conflicts in Inconsistent RDF Knowledge Bases. In BTW, pages 474-493, 2011. Google Scholar
  14. Tobias Emrich, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, and Andreas Zufle. Querying uncertain spatio-temporal data. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pages 354-365. IEEE, 2012. Google Scholar
  15. Anthony Fader, Stephen Soderland, and Oren Etzioni. Identifying relations for open information extraction. In Proceedings of the Conference of Empirical Methods in Natural Language Processing (EMNLP '11), Edinburgh, Scotland, UK, July 27-31 2011. Google Scholar
  16. Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M Suchanek. Fast Rule Mining in Ontological Knowledge Bases with AMIE+. The VLDB Journal, 24(6):707-730, 2015. Google Scholar
  17. Eric Gribkoff and Dan Suciu. Slimshot: In-database probabilistic inference for knowledge bases. PVLDB, 9(7):552-563, 2016. Google Scholar
  18. Claudio Gutierrez, Carlos Hurtado, and Alejandro Vaisman. Temporal RDF. In Proc. of European Semantic Web Conference, pages 93-107, 2005. Google Scholar
  19. Patrick Hayes. RDF Semantics. W3C Recommendation, 2004. Google Scholar
  20. Johannes Hoffart, Fabian M Suchanek, Klaus Berberich, Edwin Lewis-Kelham, Gerard De Melo, and Gerhard Weikum. Yago2: exploring and querying world knowledge in time, space, context, and many languages. In Proceedings of the 20th international conference companion on World wide web, pages 229-232. ACM, 2011. Google Scholar
  21. Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren Etzioni. Markov logic networks for natural language question answering. arXiv preprint arXiv:1507.03045, 2015. Google Scholar
  22. Angelika Kimmig, Bart Demoen, Luc De Raedt, Vitor Santos Costa, and Ricardo Rocha. On the implementation of the probabilistic logic programming language problog. Theory and Practice of Logic Programming, 11(2-3):235-262, 2011. Google Scholar
  23. Feng Niu, Christopher Ré, AnHai Doan, and Jude Shavlik. Tuffy: Scaling up statistical inference in markov logic networks using an rdbms. Proc. of the VLDB Endowment, 4(6):373-384, 2011. Google Scholar
  24. Gultekin Ozsoyoglu and Richard T Snodgrass. Temporal and real-time databases: A survey. IEEE Transactions on Knowledge and Data Engineering, 7(4):513-532, 1995. Google Scholar
  25. Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, and Mathias Verbeke. Inducing probabilistic relational rules from probabilistic examples. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 1835-1843, 2015. Google Scholar
  26. Stefan Schoenmackers, Oren Etzioni, Daniel S Weld, and Jesse Davis. Learning first-order horn clauses from web text. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1088-1098. Association for Computational Linguistics, 2010. Google Scholar
  27. Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, and Christopher Ré. Incremental knowledge base construction using deepdive. Proceedings of the VLDB Endowment, 8(11):1310-1321, 2015. Google Scholar
  28. Richard T Snodgrass. Temporal databases. In Theories and methods of spatio-temporal reasoning in geographic space, pages 22-64. Springer, 1992. Google Scholar
  29. Dan Suciu, Dan Olteanu, Christopher Ré, and Christoph Koch. Probabilistic databases. Synthesis Lectures on Data Management, 3(2):1-180, 2011. Google Scholar
  30. Denny Vrandečić and Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78-85, 2014. Google Scholar
  31. Hong Zhu, Caicai Zhang, Zhongsheng Cao, and Ruiming Tang. On efficient conditioning of probabilistic relational databases. Knowl.-Based Syst., 92:112-126, 2016. Google Scholar
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail