Latent Clues Investigation in Discipline Inspection and Supervision Based on Knowledge Graph Analysis
Yang Gao, Jing Liu · 2021
The big data technology is reshaping the ecology of national governance, and also brought new opportunities for the development of discipline inspection and supervision system. Especially, in initial reviewing stage of the disciplinary inspection and supervision case investigation, the current processing methods are inefficient and invisible clues are difficult to find, and it takes a lot of manpower to conduct comparative investigations. In order to improve such latent clues discovering work, this paper well utilize the knowledge graph technology to propose a latent clues investigation method to promote the investigation efficiency for the anti-corruption cases in discipline inspection and supervision. Throughout the actual situation of such clues investigation, we proves that our proposed method could uncover meaningful latent clues during the initial reviewing stage of the investigation of corruption cases, thereby achieving the purpose of assisting the investigation of the discipline inspection and supervision.