Declarative Rules for Annotated Expert Knowledge in Change Management

Authors Dietmar Seipel, Rüdiger von der Weth, Salvador Abreu, Falco Nogatz, Alexander Werner

Thumbnail PDF


  • Filesize: 0.51 MB
  • 16 pages

Document Identifiers

Author Details

Dietmar Seipel
Rüdiger von der Weth
Salvador Abreu
Falco Nogatz
Alexander Werner

Cite AsGet BibTex

Dietmar Seipel, Rüdiger von der Weth, Salvador Abreu, Falco Nogatz, and Alexander Werner. Declarative Rules for Annotated Expert Knowledge in Change Management. In 5th Symposium on Languages, Applications and Technologies (SLATE'16). Open Access Series in Informatics (OASIcs), Volume 51, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


In this paper, we use declarative and domain-specific languages for representing expert knowledge in the field of change management in organisational psychology. Expert rules obtained in practical case studies are represented as declarative rules in a deductive database. The expert rules are annotated by information describing their provenance and confidence. Additional provenance information for the whole - or parts of the - rule base can be given by ontologies. Deductive databases allow for declaratively defining the semantics of the expert knowledge with rules; the evaluation of the rules can be optimised and the inference mechanisms could be changed, since they are specified in an abstract way. As the logical syntax of rules had been a problem in previous applications of deductive databases, we use specially designed domain-specific languages to make the rule syntax easier for non-programmers. The semantics of the whole knowledge base is declarative. The rules are written declaratively in an extension datalogs of the well-known deductive database language datalog on the data level, and additional datalogs rules can configure the processing of the annotated rules and the ontologies.
  • declarative
  • datalog
  • prolog
  • domain-specific
  • change management


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


  1. Serge Abiteboul. Datalog: La renaissance., 2012.
  2. Joachim Baumeister and Dietmar Seipel. Anomalies in ontologies with rules. Journal of Web Semantics, Science, Services and Agents on the World Wide Web, 8(1):55-68, 2010. Google Scholar
  3. Ivan Bratko. Prolog Programming for Artificial Intelligence. Addison-Wesley Longman, 4th edition, 2011. Google Scholar
  4. Stefano Ceri, Georg Gottlob, and Letizia Tanca. Logic Programming and Databases. Springer, Berlin, 1990. Google Scholar
  5. Upen S. Chakravarthy, Dan H. Fishman, and Jack Minker. Semantic query optimization in expert systems and database systems. In Proceedings of the 1st International Workshop on Expert Database Systems, pages 659-674, 1986. Google Scholar
  6. Luc De Raedt, Angelika Kimmig, and Hannu Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 2468-2473, 2007. Google Scholar
  7. Ramez Elmasri and Shamkant Navathe. Fundamentals of Database Systems. Benjamin Cummings, 7th edition, 2015. Google Scholar
  8. Martin Fowler. Refactoring - Improving the Design of Existing Code. Addison-Wesley, 1999. Google Scholar
  9. Martin Fowler. Domain-Specific Languages. Addison-Wesley, 2011. Google Scholar
  10. Bernardo Cuenca Grau, Evgeny Kharlamov, Egor V. Kostylev, and Dmitriy Zheleznyakov. Controlled query evaluation for Datalog and Owl 2 profile ontologies. arXiv:1504.06529, 2015. Google Scholar
  11. Michel Kifer and V. S. Subrahmanian. Theory of generalized annotated logic programming and its applications. Journal of Logic Programming, 12(4):335-368, 1992. Google Scholar
  12. Tomaž Kosar, Sudev Bohra, and Marjan Mernik. Domain-specific languages: A systematic mapping study. Information and Software Technology, 71:77-91, 2016. Google Scholar
  13. Laks V. Lakshmanan and Fereidoon Sadri. On a theory of probabilistic deductive databases. Theory and Practice of Logic Programming, 1:5-42, 2001. Google Scholar
  14. Jack Minker, Dietmar Seipel, and Carlo Zaniolo. Logic and databases: History of deductive databases. In Handbook of the History of Logic, volume 9, Computational Logic. North Holland, 2014. Google Scholar
  15. Robert E. McGrath and Joeg Futrelle. Reasoning About Provenance with Owl and Swrl Rules. In AAAI Spring Symposium, 2008. Google Scholar
  16. Dietmar Seipel. Practical applications of extended deductive databases in Datalog^*. In Proceedings of the 23rd Workshop on Logic Programming (WLP 2009), September 2009. Google Scholar
  17. Dietmar Seipel. Knowledge engineering for hybrid deductive databases. In Proceedings of the 29th Workshop on Logic Programming (WLP 2015), September 2015. Google Scholar
  18. Dietmar Seipel, Joachim Baumeister, and Marbod Hopfner. Declaratively querying and visualizing knowledge bases in XML. In Proceedings of the 15th International Conference on Applications of Declarative Programming and Knowledge Management, LNAI 3392, pages 16-31. Springer, 2005. Google Scholar
  19. Jeffrey D. Ullman. Principles of Database and Knowledge-Base Systems, Volume I. Computer Science Press, 1988. Google Scholar
  20. Jeffrey D. Ullman. Principles of Database and Knowledge-Base Systems, Volume II. Computer Science Press, 1989. Google Scholar
  21. Rüdiger von der Weth, Dietmar Seipel, Falco Nogatz, Katrin Schubach, Alexander Werner, and Franz Wortha. Modellierung von handlungswissen aus fragmentiertem und heterogenem rohdatenmaterial durch inkrementelle verfeinerung in einem regelbanksystem. Journal Psychologie des Alltagshandelns, 2016. Google Scholar
  22. Jan Wielemaker. An overview of the SWI-Prolog programming environment. In Proceedings of the 13th International Workshop on Logic Programming Environments (WLPE), pages 1-16, 2003. Google Scholar