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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

The BioLexicon: a large-scale terminological resource for biomedical text mining.

Paul Thompson¹, John McNaught, Simonetta Montemagni

¹School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk

BMC Bioinformatics

|October 14, 2011

Summary

This summary is machine-generated.

The BioLexicon is a comprehensive biomedical resource that unifies terms and variants to improve information retrieval and text mining for biologists. It enhances the discovery of biological knowledge and events from scientific literature.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Computational Biology
Natural Language Processing

Background:

Biomedical literature is rapidly expanding, necessitating advanced search systems for biologists.
Existing resources are often specialized, dispersed, and fail to capture the full range of term variants and relationships.
There is a need for a unified resource that accounts for diverse terminology and facilitates event extraction.

Purpose of the Study:

To design, construct, and evaluate the BioLexicon, a large-scale lexical and conceptual resource for the biomedical domain.
To create a unified repository of biomedical terms, variants, and semantic relationships.
To support text mining tools in tasks such as entity recognition and event extraction.

Main Methods:

Integrated terms from multiple existing data resources into a single repository.
Automatically extracted new term variants from biomedical literature.
Included biologically relevant verbs and their grammatical/semantic patterns for event extraction.
Modeled the resource using the Lexical Markup Framework (LMF) for interoperability.

Main Results:

The BioLexicon contains over 2.2 million lexical entries and 1.8 million terminological variants.
It includes over 3.3 million semantic relations, with over 2 million synonymy relations.
The resource supports various text mining applications, including part-of-speech tagging and biomedical entity recognition.

Conclusions:

The BioLexicon is a valuable resource for improving biomedical text mining and information retrieval.
Its comprehensive nature and inclusion of event-related information benefit both application developers and end-users.
Integration into existing tools demonstrated performance improvements, highlighting its practical utility.