Grailog 1.0: Graph-Logic Visualization of Ontologies and Rules

  • Harold Boley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)


Directed labeled graphs (DLGs) provide a good starting point for visual data & knowledge representation but cannot straightforwardly represent non-binary relationships, nested structures, and relation descriptions. These advanced features require encoded constructs with auxiliary nodes and relationships, which also need to be kept separate from straightforward constructs. Therefore, various extensions of DLGs have been proposed for data & knowledge representation, including n-ary relationships as directed labeled hyperarcs, graph partitionings (possibly interfaced as complex nodes), and (hyper)arc labels used as nodes of other (hyper)arcs. Ontologies and rules have used extended logics for knowledge representation such as description logic, object/frame logic, higher-order logic, and modal logic. The paper demonstrates how data & knowledge representation with graphs and logics can be reconciled, inspiring flexible name specification. It proceeds from simple to extended graphs for logics needed in AI and the Semantic Web. Along with its visual introduction, each graph construct is mapped to its corresponding symbolic logic construct. These graph-logic extensions constitute a systematics defined by orthogonal axes, which has led to the Grailog 1.0 language aligned with the Web-rule industry standard RuleML 1.0.


Knowledge Representation Description Logic Graph Transformation Atomic Formula Line Style 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Ber73]
    Berge, C.: Graphs and Hypergraphs. North Holland (1973)Google Scholar
  2. [Bol92]
    Boley, H.: Declarative Operations on Nets. In: Lehmann, F. (ed.) Semantic Networks in Artificial Intelligence. Computers & Mathematics with Applications, vol. 23, pp. 601–637. Pergamon Press (1992)Google Scholar
  3. [Bol01]
    Boley, H.: Relationships Between Logic Programming and RDF. In: Kowalczyk, R., Loke, S.W., Reed, N.E., Graham, G. (eds.) PRICAI 2000 Workshop Reader. LNCS (LNAI), vol. 2112, pp. 201–218. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. [Bol11]
    Boley, H.: A RIF-Style Semantics for RuleML-Integrated Positional-Slotted, Object-Applicative Rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 194–211. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. [BPS10]
    Boley, H., Paschke, A., Shafiq, O.: RuleML 1.0: The Overarching Specification of Web Rules. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 162–178. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. [CM09]
    Chein, M., Mugnier, M.-L.: Graph-Based Knowledge Representation. Springer (2009)Google Scholar
  7. [HSTC11]
    Howse, J., Stapleton, G., Taylor, K., Chapman, P.: Visualizing Ontologies: A Case Study. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 257–272. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. [Ior10]
    Iordanov, B.: HyperGraphDB: A Generalized Graph Database. In: Shen, H.T., Pei, J., Özsu, M.T., Zou, L., Lu, J., Ling, T.-W., Yu, G., Zhuang, Y., Shao, J. (eds.) WAIM 2010. LNCS, vol. 6185, pp. 25–36. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. [Woo07]
    Woods, W.A.: Meaning and Links. AI Magazine 28(4), 71–92 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Harold Boley
    • 1
  1. 1.National Research Council, Security and Disruptive Technologies, Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada

Personalised recommendations