Advertisement

Knowledge Graphs and Bases

  • Michael K. Bergman
Chapter

Abstract

We see physical and informational networks, connections, relationships, and links grow all around us. This chapter contemplates the universal graph structure at the core of these developments. Relations between nodes, different than those of a hierarchical or subsumptive nature, provide still different structural connections across the knowledge graph. Besides graph theory, the field draws on methods including statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. Graph theory and network science are the suitable disciplines for a variety of information structures and many additional classes of problems. Once we understand graphs as an excellent way to represent logic and data structures, the next step is to extend their applicability to knowledge representations and knowledge bases as well. We see the usefulness of graph theory to linguistics by the various knowledge bases such as WordNet (in multiple languages) and VerbNet. Domain ontologies emphasize conceptual relationships over lexicographic ones for a given knowledge domain. These constructs of semantic Web standards, combined with a properly constructed knowledge graph and the use of synonymous and related vocabularies in semsets, provide potent mechanisms for how to query a knowledge base. Furthermore, if we sufficiently populate a knowledge graph with accurate instance data, often from various knowledge bases, then ontologies can also be the guiding structures for efficient machine learning and artificial intelligence. We want knowledge sources, putatively knowledge bases, to contribute the actual instance data to populate our ontology graph structures.

Keywords

Graph Graph theory Networks Knowledge base Knowledge graph 

References

  1. 1.
    J.J. Sylvester, Chemistry and algebra. Nature 17, 284 (1878)CrossRefGoogle Scholar
  2. 2.
    L.A. Bunimovich, D.C. Smith, B.Z. Webb, Specialization models of network growth, arXiv:1712.01788 [physics] (2017)Google Scholar
  3. 3.
    G. Gilder, Metcalfe’s Law and Legacy (Forbes ASAP, 1993), p. 158Google Scholar
  4. 4.
    S.F. Peralta, Moore’s Law, Metcalfe’s Law, and the Dot Com Bubble (2011)Google Scholar
  5. 5.
    D.P. Reed, That sneaky exponential—Beyond Metcalfe’s law to the power of community building, in Services, in XVth International Symposium on Services and Local Access (Edinburgh, 1999)Google Scholar
  6. 6.
    B. Briscoe, A. Odlyzko, B. Tilly, Metcalfe’s law is wrong, in IEEE Spectrum (2006)Google Scholar
  7. 7.
    Y.J. Stein, The value of being linked in. http://www.dspcsp.com/pubs/linkedin.pdf
  8. 8.
    S. Harris, A. Seaborne, SPARQL 1.1 Query Language, World Wide Web Consortium (2013)Google Scholar
  9. 9.
    M. Kuba, OWL 2 and SWRL tutorial. https://dior.ics.muni.cz/~makub/owl/
  10. 10.
    D.L. McGuinness, Ontologies come of age, in Spinning the Semantic Web: Bringing the World Wide Web to its Full Potential, ed. by D. Fensel, J. Hendler, H. Lieberman, W. Wahlster (MIT Press, Cambridge, 2003), pp. 171–194Google Scholar
  11. 11.
    O. Bodenreider, F. Olken, Ontology Summit 2007 Communique, in Ontology Summit 2007 (Ontolog Forum, Gaithersburg, MD, 2007)Google Scholar
  12. 12.
    N. Guarino, Formal ontology and information systems, in Proceedings of FOIS’98 (IOS Press, Trento, 1998), pp. 3–15Google Scholar
  13. 13.
    M. Uschold, Ontology-driven information systems: past, present and future, in Proceedings of the Fifth International Conference on Formal Ontology in Information Systems (FOIS 2008), ed. by C. Eschenbach, M. Grüninger (IOS Press, Amsterdam, 2008), pp. 3–20Google Scholar
  14. 14.
    P.C. Costa, Bayesian Semantics for the Semantic Web, Ph.D., George Mason University (2005)Google Scholar
  15. 15.
    K.B. Laskey, MEBN: a language for first-order Bayesian knowledge Bases. Artif. Intell. 172, 140–178 (2008)MathSciNetCrossRefGoogle Scholar
  16. 16.
    F. Bobillo, U. Straccia, Fuzzy ontology representation using OWL 2. Int. J. Approx. Reason. 52, 1073–1094 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    F.M. Suchanek, G. Weikum, Knowledge bases in the age of big data analytics, in Proceedings of the VLDB Endowment (2014), pp. 1713–1714CrossRefGoogle Scholar
  18. 18.
    O. Medelyan, D. Milne, C. Legg, I.H. Witten, Mining meaning from Wikipedia. Int. J. Hum. Comput. Stud. 67, 716–754 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

Personalised recommendations