API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9202)


API4KP (API for Knowledge Platforms) is a standard development effort that targets the basic administration services as well as the retrieval, modification and processing of expressions in machine-readable languages, including but not limited to knowledge representation and reasoning (KRR) languages, within heterogeneous (multi-language, multi-nature) knowledge platforms. KRR languages of concern in this paper include but are not limited to RDF(S), OWL, RuleML and Common Logic, and the knowledge platforms may support one or several of these. Additional languages are integrated using mappings into KRR languages. A general notion of structure for knowledge sources is developed using monads. The presented API4KP metamodel, in the form of an OWL ontology, provides the foundation of an abstract syntax for communications about knowledge sources and environments, including a classification of knowledge source by mutability, structure, and an abstraction hierarchy as well as the use of performatives (inform, query, ...), languages, logics, dialects, formats and lineage. Finally, the metamodel provides a classification of operations on knowledge sources and environments which may be used for requests (message-passing).


Knowledge Source Abstract Syntax Knowledge Resource Model Drive Architecture Common Logic 
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  1. 1.
    The distributed ontology, model, and specication language (dol).
  2. 2.
    Finin, T., Fritzson, R., McKay, D., McEntire, R.: KQML as an agent communication language. In: Proceedings of the Third International Conference on Information and Knowledge Management, CIKM 1994, pp. 456–463. ACM, New York (1994).
  3. 3.
    IFLA Study Group on the Functional Requirements for Bibliographic Records: Functional requirements for bibliographic records : final report (1998). (accessed: 2007–12-26)
  4. 4.
    Kozlenkov, A., Jeffery, D., Paschke, A.: State management and concurrency in event processing. In: Proceedings of the Third ACM International Conference on Distributed Event-Based Systems, DEBS 2009, Nashville, Tennessee, USA, July 6–9, 2009.
  5. 5.
    Mac Lane, S.: Categories for the Working Mathematician (Graduate Texts in Mathematics). Springer (1998)Google Scholar
  6. 6.
    Meijer, E., Fokkinga, M., Paterson, R.: Functional Programming with Bananas, Lenses, Envelopes and Barbed Wire, pp. 124–144. Springer-Verlag (1991)Google Scholar
  7. 7.
    Mellor, S.J., Kendall, S., Uhl, A., Weise, D.: MDA Distilled. Addison Wesley Longman Publishing Co. Inc., Redwood City (2004)Google Scholar
  8. 8.
    Moggi, E.: Notions of computation and monads. Selections from 1989 IEEE Symposium on Logic in Computer Science 93(1), 55–92 (1991). MathSciNetGoogle Scholar
  9. 9.
    Object Management Group (OMG): OntoIOp request for proposal.
  10. 10.
    Paschke, A., Athan, T., Sottara, D., Kendall, E., Bell, R.: A representational analysis of the API4KB metamodel. In: Proceedings of the 7th Workshop on Formal Ontologies meet Industry (FOMI 2015). Springer-Verlag (2015)Google Scholar
  11. 11.
    Paschke, A., Vincent, P., Alves, A., Moxey, C.: Tutorial on advanced design patterns in event processing. In: Proceedings of the Sixth ACM International Conference on Distributed Event-Based Systems, DEBS 2012, Berlin, Germany, July 16–20, 2012, pp. 324–334 (2012)Google Scholar
  12. 12.
    Paschke, A., Vincent, P., Springer, F.: Standards for Complex Event Processing and Reaction Rules. In: Palmirani, M. (ed.) RuleML - America 2011. LNCS, vol. 7018, pp. 128–139. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  13. 13.
    Rector, A.: Knowledge driven software and “fractal tailoring”: ontologies in development environments for clinical systems. In: Proceedings of the 2010 Conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010), pp. 17–28. IOS Press, Amsterdam (2010)Google Scholar
  14. 14.
    Rosemann, M., Green, P.: Developing a Meta Model for the Bunge-Wand-Weber Ontological Constructs. Inf. Syst. 27(2), 75–91 (2002). doi: 10.1016/S0306-4379(01)00048-5 CrossRefGoogle Scholar
  15. 15.
    Slota, M., Leite, J., Swift, T.: Splitting and updating hybrid knowledge bases. Theory and Practice of Logic Programming 11(4–5), 801–819 (2011). 27th Int’l. Conference on Logic Programming (ICLP 2011) Special IssueMathSciNetCrossRefGoogle Scholar
  16. 16.
    Wadler, P.: Comprehending monads. In: Mathematical Structures in Computer Science, pp. 61–78 (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Athan Services ( LafayetteUSA
  2. 2.RaytheonFort WayneUSA
  3. 3.Thematix Partners LLCNew YorkUSA
  4. 4.AG Corporate Semantic WebFreie Universitaet BerlinBerlinGermany
  5. 5.Department of Biomedical InformaticsArizona State UniversityTempeUSA

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