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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A methodology for extending domain coverage in SemRep.

Graciela Rosemblat1, Dongwook Shin, Halil Kilicoglu

  • 1National Library of Medicine, National Institutes of Health, Lister Hill Center, Cognitive Science Branch, 8600 Rockville Pike, Bethesda, MD 20894, USA.

Journal of Biomedical Informatics
|August 27, 2013
PubMed
Summary
This summary is machine-generated.

This study presents a domain-independent method to expand SemRep, a natural language processing tool, beyond biomedical texts. The approach successfully integrates new domains with the Unified Medical Language System (UMLS) knowledge sources.

Keywords:
Domain-independent ontology development methodologyNatural language processing applicationSemantic predicationsUMLS knowledge sources

Related Experiment Videos

Last Updated: May 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Natural Language Processing
  • Ontology Engineering
  • Medical Informatics

Background:

  • SemRep is a natural language processing application developed for biomedical texts.
  • SemRep relies on knowledge sources from the Unified Medical Language System (UMLS).
  • Extending SemRep to non-biomedical domains requires ontological and terminological adaptations.

Purpose of the Study:

  • To present a domain-independent methodology for extending SemRep coverage beyond the biomedical domain.
  • To detail a step-wise approach and implemented mechanisms for adapting SemRep to new knowledge areas.
  • To demonstrate the methodology's application and effectiveness through a case study in medical informatics.

Main Methods:

  • Adapted established ontology engineering phases for integration with UMLS knowledge sources.
  • Developed a semantic representation for a previously unsupported domain.
  • Integrated the adapted ontology engineering process with SemRep's existing UMLS-dependent architecture.

Main Results:

  • Successfully extended SemRep's application to a new domain.
  • Demonstrated the methodology's validity and usefulness through qualitative and quantitative results.
  • Validated the adapted ontology engineering phases for optimal integration with UMLS.

Conclusions:

  • The proposed domain-independent methodology effectively extends SemRep's capabilities beyond the biomedical field.
  • The integration of adapted ontology engineering with UMLS knowledge sources is feasible and beneficial.
  • The methodology provides a robust framework for semantic representation in diverse knowledge domains.