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Module Extraction for Efficient Object Queries over Ontologies with Large ABoxes.

Jia Xu1, Patrick Shironoshita1, Ubbo Visser2

  • 1Electrical and Computer Engineering Department, University of Miami, Coral Gables, FL 33146.

Artificial Intelligence and Applications (Commerce, Calif.)
|February 6, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces ontology ABox modules for efficient querying of large datasets. These modules enable independent reasoning and faster data retrieval, improving ontology tractability.

Keywords:
ABox ModuleOntology QueryingSHIQ

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Area of Science:

  • Knowledge Representation and Reasoning
  • Ontology Engineering
  • Artificial Intelligence

Background:

  • Querying large ontology ABoxes (Assertion Boxes) presents tractability challenges.
  • Existing methods may struggle with scalability and efficiency for extensive ABoxes.

Purpose of the Study:

  • To formally define and develop ABox modules for independent reasoning.
  • To improve the efficiency of querying and retrieving data from large ontologies.

Main Methods:

  • Formal definition of ABox modules preserving facts about specific individuals.
  • Development of a theoretical approach and an approximation for SHIQ ontologies.
  • Evaluation of the ABox module approximation on diverse ontology types.

Main Results:

  • The proposed ABox modules allow for independent reasoning with respect to the ontology TBox (Terminological Box).
  • Isolated and distributed ABox reasoning becomes feasible and more efficient.
  • Extracted ABox modules are significantly smaller than the entire ABox on average.

Conclusions:

  • The developed ABox modules effectively address the tractability problem in ontology querying.
  • Significant improvements in ontology reasoning time are achieved using these modules.
  • This approach enhances data retrieval efficiency from large-scale ontology ABoxes.