Automatic white-box testing of first-order logic ontologies

Formal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization cannot only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of or even preventing the inference. In this paper, we introduce a novel, fully automatic white-box testing framework for first-order logic (FOL) ontologies. Our methodology is based on the detection of inference-based redundancies in the given axiomatization. The application of the proposed testing method is fully automatic since (i) the automated generation of tests is guided only by the syntax of axioms and (ii) the evaluation of tests is performed by automated theorem provers (ATPs). Our proposal enables the detection of defects and serves to certify the grade of suitability—for reasoning purposes—of every axiom. We formally define the set of tests that are (automatically) generated from any axiom and prove that every test is logically related to redundancies in the axiom from which the test has been generated. We have implemented our method and used this implementation to automatically detect several non-trivial defects that were hidden in various FOL ontologies. Throughout the paper we provide illustrative examples of these defects, explain how they were found and how each proof—given by an ATP—provides useful hints on the nature of each defect. Additionally, by correcting all the detected defects, we have obtained an improved version of one of the tested ontologies: Adimen-SUMO.
Egileak (ixakideak): 
Javier Álvez, Montserrat Hermo, Paqui Lucio, German Rigau
Artikuluaren erreferentzia: 
Journal of Logic and Computation, Volume 29, Issue 5, September 2019, Pages 723–751
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