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medicX-KG: A Knowledge Graph for Pharmacists' Drug Information Needs

arXiv 2506.17959 ↗First posted 22 Jun 2025cs.AIcited by 0

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Abstract (arXiv)

The role of pharmacists is evolving from medicine dispensing to delivering comprehensive pharmaceutical services within multidisciplinary healthcare teams. Central to this shift is access to accurate, up-to-date medicinal product information supported by robust data integration. Leveraging artificial intelligence and semantic technologies, Knowledge Graphs (KGs) uncover hidden relationships and enable data-driven decision-making. This paper presents medicX-KG, a pharmacist-oriented knowledge graph supporting clinical and regulatory decisions. It forms the semantic layer of the broader medicX platform, powering predictive and explainable pharmacy services. medicX-KG integrates data from three sources, including, the British National Formulary (BNF), DrugBank, and the Malta Medicines Authority (MMA) that addresses Malta's regulatory landscape and combines European Medicines Agency alignment with partial UK supply dependence. The KG tackles the absence of a unified national drug repository, reducing pharmacists' reliance on fragmented sources. Its design was informed by interviews with practicing pharmacists to ensure real-world applicability. We detail the KG's construction, including data extraction, ontology design, and semantic mapping. Evaluation demonstrates that medicX-KG effectively supports queries about drug availability, interactions, adverse reactions, and therapeutic classes. Limitations, including missing detailed dosage encoding and real-time updates, are discussed alongside directions for future enhancements.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 78 references that are in the map. First posted on arXiv 22 Jun 2025.

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10 of 78 references.

References · 10

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  2. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  3. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  4. 2020Building a PubMed knowledge graphcited by 38
  5. 2021A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspectivecited by 38
  6. 2023OLaLa: Ontology Matching with Large Language Modelscited by 35
  7. 2025KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichmentcited by 27
  8. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  9. 2024CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completioncited by 4
  10. 2023Predicting Drug-Drug Interactions Using Knowledge Graphscited by 1

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