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Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities.

Manuel Quesada-Martínez1, Jesualdo Tomás Fernández-Breis1, Daniel Karlsson2

  • 1Facultad de Informática, Universidad de Murcia, IMIB-Arrixaca, CP 30100 Murcia.

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|September 1, 2016
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Summary
This summary is machine-generated.

This study introduces a novel algorithm to detect missing relations in biomedical ontologies by analyzing lexical regularities in class labels. This quality assurance method aids in developing more robust and accurate ontologies for human and machine understanding.

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Area of Science:

  • Biomedical Informatics
  • Ontology Engineering
  • Natural Language Processing

Background:

  • The proliferation of biomedical ontologies necessitates robust quality assurance (QA) methods.
  • Existing ontologies often feature rich natural language descriptions but lack sufficient axiomatic content.
  • Bridging the gap between natural language and axiomatic content is crucial for ontology utility.

Purpose of the Study:

  • To investigate the relationship between natural language content and axiomatic structure in biomedical ontologies.
  • To develop and propose an algorithm for detecting missing relations using lexical regularities.
  • To analyze a specific lexical regularity in SNOMED CT, 'congenital stenosis'.

Main Methods:

  • Analysis of class labels to identify lexical regularities (LRs), which are shared word sets.
  • Formulating an assumption that classes sharing LRs should be logically related via axioms.
  • Developing an algorithm based on this assumption to detect potential missing ontology relations.

Main Results:

  • Identified lexical regularities as indicators of potential logical relationships between ontology classes.
  • Proposed a novel algorithm to leverage LRs for identifying missing axiomatic relations.
  • Applied the method to analyze the 'congenital stenosis' lexical regularity in SNOMED CT, a known problematic area.

Conclusions:

  • Lexical regularities in class labels can serve as a proxy for underlying logical relationships.
  • The proposed algorithm offers a practical approach to enhance the axiomatic completeness of biomedical ontologies.
  • This QA method can assist ontology engineers and domain experts in improving ontology quality and consistency.