构建知识图谱网络体系，推动医疗领域智能推理应用
Logic Programming on Knowledge Graph Networks And its Application in Medical Domain
- 提出知识图谱网络系统性框架，融合逻辑推理与AI技术
- 在不确定、多模态等复杂条件下验证了推理有效性
- 适合医疗AI、知识推理方向研究者参考
知识图谱研究的快速发展为医学与健康领域带来强大推动力，但现有技术在知识图谱应用中仍存在明显短板，如未能充分运用先进逻辑推理、人工智能方法、专用编程语言及现代概率统计理论。尤其缺乏对多知识图谱协作与竞争机制的关注。本文系统构建了‘知识图谱网络’的概念体系，涵盖其定义、发展、推理、计算与应用，并在模糊、不确定、多模态、向量化、分布式、联邦等不同场景下提供真实数据案例与实验结果。每一类情形均展示可验证的应用效果，最终总结出创新性结论。
原文摘要 · Abstract (English)
The rash development of knowledge graph research has brought big driving force to its application in many areas, including the medicine and healthcare domain. However, we have found that the application of some major information processing techniques on knowledge graph still lags behind. This defect includes the failure to make sufficient use of advanced logic reasoning, advanced artificial intelligence techniques, special-purpose programming languages, modern probabilistic and statistic theories et al. on knowledge graphs development and application. In particular, the multiple knowledge graphs cooperation and competition techniques have not got enough attention from researchers. This paper develops a systematic theory, technique and application of the concept 'knowledge graph network' and its application in medical and healthcare domain. Our research covers its definition, development, reasoning, computing and application under different conditions such as unsharp, uncertain, multi-modal, vectorized, distributed, federated. Almost in each case we provide (real data) examples and experiment results. Finally, a conclusion of innovation is provided.
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