A knowledge graph anti-fraud feature extraction method based on BFS and LPA

Abstract

The invention discloses a knowledge graph anti-fraud feature extraction method based on BFS and LPA, and the method comprises the steps: 1, carrying out the standardization of original data, converting the original data into labeled data under different dimensions, carrying out the cleaning and conversion, and forming data which conforms to the modeling of a knowledge graph; And step 2, constructing a knowledge graph model, including ontology construction, semantic annotation and information extraction. The method has the advantages that (1) a simple social relation is converted into a knowledge relation, different ontology knowledge is injected into a map, and a knowledge map representation method oriented to the consumer finance field is provided; (2) breadth-first search is introduced to find an entity touch black level, touch black information with different traversal lengths can be extracted after improvement, the feature level is enhanced, and the feature representation modes arediversified; And (3) for a fraudulent group problem in the anti-fraudulent field of consumer finance, entity subgroup information is mined by using an entity subgroup mining method based on label propagation, and a corresponding characteristic variable are extracted to show a relatively good distinguishing characteristic.

Description

technical field [0001] The present invention is based on BFS (Breadth-First Search, Breadth-First Search) and LPA (Label Propagation Algorithm, Label Propagation Algorithm-LPA) knowledge graph anti-fraud feature extraction method, relates to user fraud risk assessment technology in the financial field, specifically A knowledge map anti-fraud feature extraction method for asset management companies in the field of consumer finance. Background technique [0002] In recent years, Internet finance companies with P2P lending and consumer finance as their main businesses have emerged, establishing a huge emerging industry in areas that traditional financial industries cannot touch. But at the same time, all kinds of negative news came one after another, casting a shadow over the future of these Internet financial companies. Among them, the risk control capability has always been the lifeblood of these emerging technology financial companies. Only with good risk control technology...

AI Technical Summary

Problems solved by technology

However, at present, the core groups targeted by anti-fraud are the intermediary and group fraud models. Traditional features are mostly based on blacklist information such as whether the user is blacklisted or not, without considering the feature extraction of groups and knowledge graphs.

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