Logic Programming on Knowledge Graph Networks And its Application in Medical Domain
This work addresses the gap in applying logic programming and multi-graph cooperation to knowledge graphs, which is incremental as it builds on existing knowledge graph research.
The paper tackles the underutilization of advanced logic reasoning and AI techniques in knowledge graph applications, particularly in medical and healthcare domains, by developing a systematic theory and techniques for 'knowledge graph networks' with real data examples and experimental results.
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.