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Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

Published online by Cambridge University Press:  18 January 2019

DIEGO CALVANESE Open the ORCID record for DIEGO CALVANESE [Opens in a new window] ,
MARCO MONTALI ,
MARLON DUMAS  and
FABRIZIO M. MAGGI
DIEGO CALVANESE*
Affiliation:
Free University of Bozen-Bolzano, Bolzano, Italy (e-mails: calvanese@inf.unibz.it, montali@inf.unibz.it)
MARCO MONTALI
Affiliation:
Free University of Bozen-Bolzano, Bolzano, Italy (e-mails: calvanese@inf.unibz.it, montali@inf.unibz.it)
MARLON DUMAS
Affiliation:
University of Tartu, Tartu, Estonia (e-mails: m.dumas@ut.ee, f.m.maggi@ut.ee)
FABRIZIO M. MAGGI
Affiliation:
University of Tartu, Tartu, Estonia (e-mails: m.dumas@ut.ee, f.m.maggi@ut.ee)
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Abstract

The Decision Model and Notation (DMN) is a recent Object Management Group standard for the elicitation and representation of decision models and for managing their interconnection with business processes. DMN builds on the notion of decision tables and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely, background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic (DL) ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.

Keywords

decision model and notationdecision tablesdescription logicsdatatypes
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Issue 4 , July 2019 , pp. 536 - 573
Copyright
© Cambridge University Press 2019 

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Footnotes

This research is partly supported by the Estonian Research Council Grant IUT20-55, by the project “Reasoning and Enactment for Knowledge-Aware Processes” (REKAP), which is funded through the 2017 call issued by the Research Committee of the Free University of Bozen-Bolzano, and by the Euregio Interregional Project Network IPN12 “Knowledge-Aware Operational Support” (KAOS), which is funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under the first call for basic research projects and by the Free University of Bozen-Bolzano. This is an extended version of a paper presented at the RuleML+RR 2017 conference, which has been invited for submission to TPLP. The authors acknowledge the assistance of the RuleML+RR 2017 Program Chairs Stefania Costantini, Enrico Franconi, Fariba Sadri, and William van Woensel.

References

Artale, A., Kontchakov, R. and Ryzhikov, V. 2012. DL-Lite with attributes and datatypes. In Proc. of the 20th European Conference on Artificial Intelligence (ECAI). Frontiers in Artificial Intelligence and Applications, vol. 242. IOS Press, Amsterdam, 61–66.Google Scholar
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider, P. F., Eds. 2007. The Description Logic Handbook: Theory, Implementation and Applications, 2nd ed. Cambridge University Press, Cambridge.10.1017/CBO9780511711787CrossRefGoogle Scholar
Baader, F. and Sattler, U. 2000. Tableau algorithms for description logics. In Proc. of the 9th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX). Lecture Notes in Artificial Intelligence, vol. 1847. Springer, Berlin, 1–18.10.1007/10722086_1CrossRefGoogle Scholar
Batoulis, K., Meyer, A., Bazhenova, E., Decker, G. and Weske, M. 2015. Extracting decision logic from process models. In Proc. of the 27th Int.ernational Conference on Advanced Information Systems Engineering (CAiSE). Springer, Berlin.Google Scholar
Calvanese, D., Dumas, M., Laurson, Ü.,Maggi, F. M., Montali, M. and Teinemaa, I. 2016. Semantics and analysis of DMN decision tables. In Proc. of the 14th International Conference on Business Process Management (BPM). Lecture Notes in Computer Science, vol. 9850. Springer, Berlin, 217–233.Google Scholar
Calvanese, D., Dumas, M., Maggi, F. M. and Montali, M. 2017. Semantic DMN: Formalizing decision models with domain knowledge. In Proc. of the 1st International Joint Conference on Rules and Reasoning (RuleML+RR). Lecture Notes in Computer Science, vol. 10364. Springer, Berlin, 70–86.Google Scholar
CODASYL Decision Table Task Group. 1982. A Modern Appraisal of Decision Tables: A CODASYL Report. ACM.Google Scholar
Drabent, W., Eiter, T., Ianni, G., Krennwallner, T., Lukasiewicz, T. and Maluszynski, J. 2009. Hybrid reasoning with rules and ontologies. In Semantic Techniques for the Web, The REWERSE Perspective, Bry, F., and Maluszynski, J., Eds. Lecture Notes in Computer Science, vol. 5500. Springer, Berlin, 1–49.10.1007/978-3-642-04581-3_1CrossRefGoogle Scholar
Eiter, T., Kaminski, T., Redl, C., Schüller, P. and Weinzierl, A. 2017. Answer set programming with external source access. In Reasoning Web: Semantic Interoperability on the Web– 13th International Summer School Tutorial Lectures (RW). Lecture Notes in Computer Science, vol. 10370. Springer, Berlin, 204–275.10.1007/978-3-319-61033-7_7CrossRefGoogle Scholar
Eiter, T., Lutz, C., Ortiz, M. and Simkus, M. 2009. Query answering in description logics: The Knots approach. In Proc. of the 16th International Workshop on Logic, Language, Information and Computation (WoLLIC). Lecture Notes in Computer Science, vol. 5514. Springer, Berlin, 26–36.10.1007/978-3-642-02261-6_3CrossRefGoogle Scholar
Enderton, H. B. 2001. A Mathematical Introduction to Logic, 2nd ed. Academic Press, San Diego, CA, USA.Google Scholar
Haarslev, V., Möller, R. and Wessel, M. 2001. The description logic ALCNHR+ extended with concrete domains: A practically motivated approach. In Proc. of the 1st International Joint Conference on Automated Reasoning (IJCAR), 29–44.Google Scholar
Hoover, D. N. and Chen, Z. 1995. Tablewise, a decision table tool. In Proc. of the 10th Annual Conference on Computer Assurance Systems Integrity, Software Safety and Process Security (COMPASS). IEEE Computer Society Press, 97–108.Google Scholar
Horrocks, I., Kutz, O., and Sattler, U. 2006. The even more irresistible SROIQ. In Proc. of the 10th International Conference on the Principles of Knowledge Representation and Reasoning (KR), 57–67.Google Scholar
Horrocks, I. and Sattler, U. 2001. Ontology reasoning in the SHOQ (D) description logic. In Proc. of the 17th International Joint Conference on Artificial Intelligence (IJCAI), 199–204.Google Scholar
Krisnadhi, A., Maier, F. and Hitzler, P. 2011. OWL and rules. In Reasoning Web: Semantic Technologies for the Web of Data – 7th International Summer School Tutorial Lectures (RW). Lecture Notes in Computer Science, vol. 6848. Springer, Berlin, 382–415.10.1007/978-3-642-23032-5_7CrossRefGoogle Scholar
Lutz, C. 2002a. The Complexity of Reasoning with Concrete Domains. Ph.D. thesis, Teaching and Research Area for Theoretical Computer Science, RWTH Aachen.Google Scholar
Lutz, C. 2002b. Description logics with concrete domains – A survey. In Proc. of the 4th Conference on Advances in Modal Logic (AiML 2012), 265–296.Google Scholar
Motik, B. and Horrocks, I. 2008. OWL datatypes: Design and implementation. In Proc. of the 7th International Semantic Web Conference (ISWC). Lecture Notes in Computer Science, vol. 5318. Springer, Berlin, 307–322.Google Scholar
Motik, B., Parsia, B. and Patel-Schneider, P. F. 2012. OWL 2 Web Ontology Language structural specification and functional-style syntax, 2nd ed. W3C Recommendation, World Wide Web Consortium. Dec. URL: http://www.w3.org/TR/owl2-syntax/. [Accessed on Januaryl 7, 2019].Google Scholar
Motik, B. and Rosati, R. 2010. Reconciling description logics and rules. Journal of the ACM 57, 5, 30:1-30:62.10.1145/1754399.1754403CrossRefGoogle Scholar
Németi, I. 1986. Free Algebras and Decidability in Algebraic Logic. Ph.D. thesis, Mathematical Institute of The Hungarian Academy of Sciences, Budapest.Google Scholar
OMG. 2016. Decision Model and Notation (DMN) 1.1. URL: http://www.omg.org/spec/DMN/1.1/. [Accessed on Januaryl 7, 2019].Google Scholar
Ortiz, M. 2010. Query Answering in Expressive Description Logics: Techniques and Complexity Results. Ph.D. thesis, Vienna University of Technology.Google Scholar
Ortiz, M., Simkus, M., and Eiter, T. 2008. Worst-case optimal conjunctive query answering for an expressive description logic without inverses. In Proc. of the 23rd AAAI Conference on Artificial Intelligence (AAAI). AAAI Press, Palo Alto, 504–510.Google Scholar
Pan, J. Z. and Horrocks, I. 2003. Web ontology reasoning with datatype groups. In Proc. of the 2nd International Semantic Web Conference (ISWC). Lecture Notes in Computer Science, vol. 2870. Springer, Berlin, 47–63.Google Scholar
Pawlak, Z. 1987. Decision tables – A rough set approach. Bulletin of the EATCS 33, 85–95.Google Scholar
Pooch, U. W. 1974. Translation of decision tables. ACM Computing Surveys 6, 2, 125–151.10.1145/356628.356630CrossRefGoogle Scholar
Savkovic, O. and Calvanese, D. 2012. Introducing datatypes in DL-Lite. In Proc. of the 20th European Conference on Artificial Intelligence (ECAI). Frontiers in Artificial Intelligence and Applications, vol. 242. IOS Press, Amsterdam, 720–725.Google Scholar
Shearer, R., Motik, B. and Horrocks, I. 2008. HermiT: A highly-efficient OWL reasoner. In Proc. of the 5th International Workshop on OWL: Experiences and Directions (OWLED). CEUR Workshop Proceedings, vol. 432, http://ceur-ws.org/. [Accessed on Januaryl 7, 2019].Google Scholar
Sirin, E. and Parsia, B. 2006. Pellet system description. In Proc. of the 19th International Workshop on Description Logics (DL). CEUR Workshop Proceedings, vol. 189, http://ceur-ws.org/. [Accessed on Januaryl 7, 2019].Google Scholar
Tsarkov, D. and Horrocks, I. 2006. FaCT++ description logic reasoner: System description. In Proc. of the 3rd International Joint Conference on Automated Reasoning (IJCAR), 292–297.Google Scholar
Vanthienen, J. and Dries, E. 1993. Illustration of a decision table tool for specifying and implementing knowledge based systems. In Proc. of the 5th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE Computer Society Press, 198–205.Google Scholar
Vanthienen, J. and Dries, E. 1994. Illustration of a decision table tool for specifying and implementing knowledge based systems. International Journal on Artificial Intelligence Tools 3, 2, 267–288.10.1142/S0218213094000133CrossRefGoogle Scholar
Vanthienen, J., Mues, C. and Aerts, A. 1998. An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering 27, 3, 337–352.10.1016/S0169-023X(98)80003-7CrossRefGoogle Scholar
W3C OWL Working Group. 2012. OWL 2 Web Ontology Language document overview, 2nd ed. W3C Recommendation, World Wide Web Consortium. Dec. URL: http://www.w3.org/TR/owl2-overview/. [Accessed on Januaryl 7, 2019].Google Scholar
Zaidi, A. K. and Levis, A. H. 1997. Validation and verification of decision making rules. Automatica 33, 2, 155–169.10.1016/S0005-1098(96)00165-3CrossRefGoogle Scholar