Building Out the System

  • Michael K. Bergman


Critical work tasks of any new domain installation are the creation of the domain knowledge graph and its population with relevant instance data. It is easier to implement and test an incremental approach. Most of the implementation effort is to conceptualize (in a knowledge graph) the structure of the new domain and to populate it with instances (data). In a proof-of-concept phase, the least-effort path is to leverage KBpedia or portions of it as is, make few changes to the knowledge graph, and populate and test local instance data. You may proceed to create the domain knowledge graph from pruning and additions to the base KBpedia structure, or from a more customized format. Some of our tasks in this area are to determine the domain and scope of the ontology; incorporate domain terminology; consider reusing existing ontologies; enumerate important terms in the ontology; define the types and the class hierarchy, especially into typologies; and define the attributes of the types. From the platform perspective, that means being able to select appropriate subsets from the knowledge base, process or transform them in some way, and then submit those result set to an external tool to conduct the designated work. Ongoing use and training demand that we adequately document all steps. If KBpedia is the starting basis for the modified domain ontology, and if incremental changes are tested for logic and consistency as they occur, then it should be possible to continue to evolve the domain knowledge graph coherently.


Installation Knowledge graph Knowledge base 


  1. 1.
    L. Galárraga, G. Heitz, K. Murphy, F.M. Suchanek, Canonicalizing Open Knowledge Bases (ACM Press, New York, NY, 2014), pp. 1679–1688Google Scholar
  2. 2.
    N.F. Noy, D.L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology (Knowledge Systems Laboratory, Stanford University, 2001)Google Scholar
  3. 3.
    M.K. Bergman, 30 Active ontology alignment tools, in AI3:::Adaptive Information.
  4. 4.
    O. Corcho, M. Fernandez, A. Gomez-Perez, Methodologies, tools and languages for building ontologies: Where is the meeting point? Data Knowl. Eng. 46 (2003)CrossRefGoogle Scholar
  5. 5.
    D.M. Jones, T.J.M. Bench-Caponand, P.R.S. Visser, Methodologies for ontology development, in Proceedings of the IT and KNOWS Conference of the 15th FIP World Computer Congress (1998)Google Scholar
  6. 6.
    E.P.B. Simperl, C. Tempich, Ontology engineering: a reality check, in On the Move to Meaningful Internet Systems (Springer, Berlin, Heidelberg, 2006), pp. 836–854CrossRefGoogle Scholar
  7. 7.
    E. Simperl, C. Tempich, D. Vrandečić, A methodology for ontology learning, in Frontiers in Artificial Intelligence and Applications 167 from the Proceedings of the 2008 Conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge (2008), pp. 225–249Google Scholar
  8. 8.
    E.P.B. Simperl, C. Tempich, Y. Sure, ONTOCOM: a cost estimation model for ontology engineering, in The Semantic Web—ISWC 2006, ed. by I. Cruz, S. Decker, D. Allemang, C. Preist, D. Schwabe, P. Mika, M. Uschold, L.M. Aroyo (Springer, Berlin, Heidelberg, 2006), pp. 625–639Google Scholar
  9. 9.
    E. Simperl, M. Mochol, T. Burger, Achieving maturity: The state of practice in ontology engineering in 2009. Int. J. Comput. Sci. Appl. 7, 45–65 (2010)Google Scholar
  10. 10.
    F. Giunchiglia, M. Marchese, I. Zaihrayeu, Encoding classifications into lightweight ontologies, in Proceedings of the 3rd European Semantic Web Conference (ESWC, 2006)Google Scholar
  11. 11.
    SKOS Simple Knowledge Organization System Reference: W3C Recommendation, World Wide Web Consortium (2009)Google Scholar
  12. 12.
    M. van Assem, V. Malaisé, A. Miles, G. Schreiber, A method to convert Thesauri to SKOS, in The Semantic Web: Research and Applications, ed. by Y. Sure, J. Domingue (Springer, Berlin, Heidelberg, 2006), 95–109Google Scholar
  13. 13.
    M. Poveda Villalón, Ontology Evaluation: A Pitfall-Based Approach to Ontology Diagnosis. Ph.D., Universidad Politécnica de Madrid, ETSI_Informatica (2016)Google Scholar
  14. 14.
    A. Halevy, M. Franklin, D. Maier, Principles of Dataspace Systems (PODS), in Proceedings of ACM Symposium on Principles of Database Systems (2006), pp. 1–9Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michael K. Bergman
    • 1
  1. 1.Cognonto CorporationCoralvilleUSA

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