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Computing semantic similarity between biomedical concepts using new information content approach.

Mohamed Ben Aouicha1, Mohamed Ali Hadj Taieb1

  • 1Multimedia InfoRmation system and Advanced Computing Laboratory, Sfax University, 3021, Tunisia.

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Summary
This summary is machine-generated.

This study introduces a novel Information Content (IC) method for computing semantic similarity in biomedical text. The new approach, utilizing MeSH ontology, outperforms existing measures in accurately reflecting expert human judgments.

Keywords:
BiomedicineDAG topological parametersInformation contentMeSHSemantic similarity

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Semantic similarity measures are crucial for processing clinical text and healthcare records in translational research.
  • Existing measures adapted for the biomedical field often incorporate domain information from clinical data or ontologies like MeSH.
  • Information Content (IC) based measures leverage taxonomic structure to define concept semantics.

Purpose of the Study:

  • To propose a new intrinsic IC computing method based on the topological parameters of a concept's ancestors' subgraph within a hierarchy.
  • To analyze topological parameters like depth and descendant subgraphs within the MeSH taxonomy.
  • To evaluate the accuracy of the proposed IC measure against established benchmarks and existing IC-based methods.

Main Methods:

  • Developed a novel intrinsic IC computation method using ancestral subgraph topological parameters.
  • Conducted a topological analysis of the MeSH (Medical Subject Headings) taxonomy, focusing on depth and descendant subgraphs.
  • Evaluated the proposed method's accuracy using MeSH as the input ontology and compared it against other IC-based measures on standard biomedical term benchmarks.

Main Results:

  • The proposed IC-based measure demonstrated superior performance compared to existing methods.
  • The study showed a strong correlation between the results from the proposed measure and human expert ratings for biomedical terms.
  • Analysis of MeSH taxonomy provided insights into semantic interpretation and parameter expression.

Conclusions:

  • The novel intrinsic IC computing method offers improved accuracy for semantic similarity in the biomedical domain.
  • The approach effectively utilizes topological features of ontologies like MeSH for enhanced semantic understanding.
  • This work contributes to more robust text processing and structuring of biomedical resources.