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LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs

arXiv 2204.09220 ↗First posted 20 Apr 2022cs.CL cs.AIcited by 15

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Abstract (arXiv)

The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures including medical triage, consultation, image-text drug recommendation and record. To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared with the other existing medical question-answering systems, our system adopts several state-of-the-art technologies including medical entity disambiguation and medical dialogue generation, which is more friendly to provide medical services to patients. In addition, we have open-sourced our codes which contain back-end models and front-end web pages at https://github.com/WENGSYX/LingYi. The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at https://github.com/WENGSYX/CMCQA are also released to further promote future research.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 20 Apr 2022.

Builds on

23 of 41 references.

References · 23

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11 more
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Citing papers per year: 2022: 1; 2023: 6; 2024: 5; 2025: 3.

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  1. 2023Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignmentcited by 23
  2. 2024T-RAG: Lessons from the LLM Trenchescited by 23
  3. 2023Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networkscited by 12
  4. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  5. 2024Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Modelscited by 8
  6. 2024Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graphcited by 6
  7. 2024SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generationcited by 5
  8. 2025Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challengecited by 5
  9. 2022Review of Natural Language Processing in Pharmacologycited by 3
  10. 2023Graph AI in Medicinecited by 3
  11. 2023LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Modelscited by 2
  12. 2023Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directionscited by 1
3 more
  1. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  2. 2024MedGo: A Chinese Medical Large Language Modelcited by 0
  3. 2025Towards Multimodal Graph Large Language Modelcited by 0