The goal of the Rule Markup Initiative is to develop RuleML as the canonical Web language for rules using XML markup, formal semantics, and efficient implementations.RuleML covers the entire rule spectrum, from derivation rules to transformation rules to reaction rules. RuleML can thus specify queries and inferences in Web ontologies, mappings between Web ontologies, and dynamic Web behaviors of workflows, services, and agents.

References in zbMATH (referenced in 27 articles , 1 standard article )

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  1. Vojíř, Stanislav; Kliegr, Tomáš: Editable machine learning models? A rule-based framework for user studies of explainability (2020)
  2. Gleißner, Tobias; Steen, Alexander; Benzmüller, Christoph: Theorem provers for every normal modal logic (2017)
  3. Mitsikas, Theodoros; Stefaneas, Petros; Ouranos, Iakovos: A rule-based approach for air traffic control in the vicinity of the airport (2017)
  4. Kowalski, Robert; Sadri, Fariba: Programming in logic without logic programming (2016)
  5. Fisch, Dominik; Jänicke, Martin; Kalkowski, Edgar; Sick, Bernhard: Learning from others: exchange of classification rules in intelligent distributed systems (2012) ioport
  6. Paschke, Adrian; Boley, Harold; Zhao, Zhili; Teymourian, Kia; Athan, Tara: Reaction RuleML 1.0: Standardized semantic reaction rules (2012) ioport
  7. Alechina, Natasha; Logan, Brian; Nguyen, Hoang Nga; Rakib, Abdur: Automated verification of resource requirements in multi-agent systems using abstraction (2011) ioport
  8. Bocchi, Laura; Gorton, Stephen; Reiff-Marganiec, Stephan: From StPowla processes to SRML models (2010)
  9. Boley, Harold; Paschke, Adrian; Shafiq, Omair: RuleML 1.0: the overarching specification of Web rules (2010) ioport
  10. Linse, Benedikt: Data integration on the (semantic) web with rules and rich unification. (2010)
  11. Eiter, Thomas; Ianni, Giovambattista; Lukasiewicz, Thomas; Schindlauer, Roman; Tompits, Hans: Combining answer set programming with description logics for the semantic web (2008)
  12. Lukasiewicz, Thomas: Fuzzy description logic programs under the answer set semantics for the semantic web (2008)
  13. Samuel, Ken; Obrst, Leo; Stoutenberg, Suzette; Fox, Karen; Franklin, Paul; Johnson, Adrian; Laskey, Ken; Nichols, Deborah; Lopez, Steve; Peterson, Jason: Translating OWL and semantic web rules into Prolog: moving toward description logic programs (2008)
  14. Viegas Damásio, Carlos; Pan, Jeff Z.; Stoilos, Giorgos; Straccia, Umberto: Representing uncertainty in RuleML (2008)
  15. Kim, Henry M.; Sengupta, Arijit: Extracting knowledge from XML document repository: A semantic Web-based approach (2007) ioport
  16. Lukasiewicz, Thomas: Probabilistic description logic programs (2007)
  17. Zarri, Gian Piero: Ontologies and reasoning techniques for (legal) intelligent information retrieval systems. (2007) ioport
  18. Ma, Hui; Schewe, Klaus-Dieter; Thalheim, Bernhard; Zhao, Jane: View integration and cooperation in databases, data warehouses and web information systems (2005)
  19. Zarri, Gian Piero: Integrating the two main inference modes of NKRL, transformations and hypotheses (2005)
  20. Alferes, J. J.; Brogi, A.; Leite, J. A.; Pereira, Luís Moniz: An evolvable rule-based e-mail agent. (2003)

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