CPC G06N 5/02 (2013.01) [G06F 16/9032 (2019.01); G06F 17/18 (2013.01); G06F 18/231 (2023.01); G06N 20/00 (2019.01)] | 7 Claims |
1. A knowledge graph computer system, comprising:
at least one processor;
at least one database communicatively connected to the at least one processor; and
a memory storing executable instructions which, when executed, cause the at least one processor to perform operations including:
aggregating, from the at least one database, data associated with a plurality of entities, the aggregated data reflecting one or more relationships between two or more of the plurality of entities;
extracting, from the aggregated data, attribute information identifying loan amounts, property values, and appraisal sources;
converting the aggregated data into a knowledge graph database format;
populating one or more data structures with the extracted and converted attribute information;
generating a knowledge graph data structure having a plurality of subject nodes corresponding to the plurality of entities and a plurality of loan nodes corresponding to the extracted attribute information;
determining a geo-spatial neighborhood delineation using machine learning to classify a property's neighborhood membership based on the extracted attribute information, wherein the machine learning includes classification training through pruning noisy clusters through generalization;
updating, using machine learning analysis, the knowledge graph by changing an identified neighborhood for one or more homes represented as nodes on the knowledge graph;
generating a first statistical distribution of first attributes associated with a first appraisal source and a second statistical distribution of second attributes associated with a second appraisal source; and
detecting an anomaly in the first statistical distribution based on a comparison of the first statistical distribution and the second statistical distribution.
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