Logic Programming on Knowledge Graph Networks And its Application in Medical Domain

TL;DR

This paper develops a comprehensive framework for logic programming on knowledge graph networks, enhancing reasoning and application in the medical domain with real data experiments and addressing multi-modal, uncertain, and distributed scenarios.

Contribution

It introduces a systematic theory and techniques for knowledge graph networks, focusing on logic reasoning and applications in healthcare, including multi-modal and federated data handling.

Findings

Effective reasoning on uncertain and multi-modal knowledge graphs

Successful application in medical data analysis with real experiments

Innovative integration of logic programming with knowledge graph networks

Abstract

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.