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HandWiki. Probabilistic Soft Logic. Encyclopedia. Available online: https://encyclopedia.pub/entry/35422 (accessed on 06 October 2026).
HandWiki. Probabilistic Soft Logic. Encyclopedia. Available at: https://encyclopedia.pub/entry/35422. Accessed October 06, 2026.
HandWiki. "Probabilistic Soft Logic" Encyclopedia, https://encyclopedia.pub/entry/35422 (accessed October 06, 2026).
HandWiki. (2022, November 21). Probabilistic Soft Logic. In Encyclopedia. https://encyclopedia.pub/entry/35422
HandWiki. "Probabilistic Soft Logic." Encyclopedia. Web. 21 November, 2022.
Probabilistic Soft Logic
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The content is sourced from: https://handwiki.org/wiki/Software:Probabilistic_soft_logic

Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. It is applicable to a variety of machine learning problems, such as collective classification, entity resolution, link prediction, and ontology alignment. PSL combines two tools: first-order logic, with its ability to succinctly represent complex phenomena, and probabilistic graphical models, which capture the uncertainty and incompleteness inherent in real-world knowledge. More specifically, PSL uses "soft" logic as its logical component and Markov random fields as its statistical model. PSL provides sophisticated inference techniques for finding the most likely answer (i.e. the maximum a posteriori (MAP) state). The "softening" of the logical formulas makes inference a polynomial time operation rather than an NP-hard operation.

machine learning ontology modeling

References

  1. Getoor, Lise; Taskar, Ben (2007). Introduction to Statistical Relational Learning. MIT Press. ISBN 978-0262072885. https://linqs.github.io/linqs-website/publications/#id:getoor-book07. 
  2. "GitHub repository". https://github.com/linqs/psl. Retrieved 26 March 2018. 
  3. Broecheler, Matthias; Getoor, Lise (2009). "Probabilistic Similarity Logic". International Workshop on Statistical Relational Learning (SRL). https://linqs.github.io/linqs-website/publications/#id:broecheler-srl09. 
  4. Bach, Stephen; Broecheler, Matthias; Huang, Bert; Getoor, Lise (2017). "Hinge-Loss Markov Random Fields and Probabilistic Soft Logic". Journal of Machine Learning Research 18: 1–67. 
  5. "Rule Specification". LINQS Lab. December 6, 2019. https://psl.linqs.org/wiki/master/Rule-Specification.html. 
  6. Augustine, Eriq (15 July 2018). "Getting Started with PSL" (in en). https://psl.linqs.org/blog/2018/07/15/getting-started-with-psl.html. Retrieved 15 July 2020. 
  7. "PSL API Reference" (in en). https://psl.linqs.org/api/. Retrieved 15 July 2020. 
  8. "Maven Repository: org.linqs » psl-java". https://mvnrepository.com/artifact/org.linqs/psl-java. Retrieved 15 July 2020. 
  9. "pslpython: A python inferface to the PSL SRL/ML software.". https://pypi.org/project/pslpython/. Retrieved 15 July 2020. 
  10. Augustine, Eriq (6 December 2019). "PSL 2.2.1 Release" (in en). https://psl.linqs.org/blog/2019/12/06/psl-2.2.1-release.html#new-python-interface. Retrieved 15 July 2020. 
  11. "Maven Repository: org.linqs » psl-groovy". https://mvnrepository.com/artifact/org.linqs/psl-groovy. 
  12. Augustine, Eriq (6 December 2019). "PSL 2.2.1 Release" (in en). https://psl.linqs.org/blog/2019/12/06/psl-2.2.1-release.html#groovy-interface-deprecated. Retrieved 15 July 2020. 
  13. "linqs/psl-examples". linqs. 19 June 2020. https://github.com/linqs/psl-examples. 
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