Automatic Strengthening of Graph-Structured Knowledge Bases

Or: How to Identify Inherited Content in Concept Graphs
  • Vinay Chaudhri
  • Nikhil Dinesh
  • Stijn Heymans
  • Michael Wessel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8323)


We consider the problem of identifying inherited content in knowledge representation structures called concept graphs (CGraphs). A CGraph is a visual representation of a concept; in the following, CGraphs and concepts are used synonymously. A CGraph is a node- and edge-labeled directed graph. Labeled (binary) edges represent relations between nodes, which are considered instances of the concepts in their node labels. CGraphs are arranged in a taxonomy (is-a hierarchy). The taxonomy is a directed acyclic graph, as multiple inheritance is allowed. A taxonomy and set of CGraphs is called a graph-structured knowledge base (GSKB).

A CGraph can inherit content from other CGraphs – intuitively, if C and D are CGraphs, then C may contain content inherited from D, i.e. labeled nodes and edges “from D” can appear in C, if D is a direct or indirect superconcept of C, or if C contains a node being labeled with either D or some subclass of D. In both cases, C is said to refer to D.

This paper contains three contributions. First, we describe and formalize the problem from a logical point of view and give a first-order semantics for CGraphs. We show that the identification of inherited content in CGraphs depends on some form of hypothetical reasoning and is thus not a purely deductive inference task, as it requires unsound reasoning. Hence, this inference is different from the standard subsumption checking problem, as known from description logics (DLs) [1]. We show that the provenance problem (from where does a logical atom in a CGraph get inherited?) strongly depends on the solution to the co-reference problem (which existentials in the first-order axiomatization of concepts as formulas denote identical domain individuals?) We demonstrate that the desired inferences can be obtained from a so-called strengthened GSKB, which is an augmented variant of the input GSKB. We present an algorithm which augments and strengthens an input GSKB, using model-theoretic notions. Secondly, we are addressing the problem from a graph-theoretic point of view, as this perspective is closer to the actual implementation. We show that we can identify inherited content by computing so-called concept coverings, which induce inherited content from superconcepts by means of graph morphisms. We argue that the algorithm solves a challenging (NP-hard) problem. Thirdly, we apply the algorithm to the large-scale biological knowledge base from the AURA project [2], and present a preliminary evaluation of its performance.


Prefer Model Equality Atom Concept Graph Concept Atom Relation Atom 
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.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vinay Chaudhri
    • 1
  • Nikhil Dinesh
    • 1
  • Stijn Heymans
    • 1
  • Michael Wessel
    • 1
  1. 1.Artificial Intelligence CenterSRI InternationalMenlo ParkUSA

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