US 12,293,321 B2
Systems and methods for knowledge graph-enabled cross-domain analytics using machine learning
Nagarjuna Reddy Chevuru, Bangalore (IN); and Sanjay Baronia, South Grafton, MA (US)
Assigned to HONEYWELL INTERNATIONAL, INC., Charlotte, NC (US)
Filed by Honeywell International Inc., Charlotte, NC (US)
Filed on Nov. 21, 2022, as Appl. No. 18/057,712.
Claims priority of application No. 202211055080 (IN), filed on Sep. 26, 2022.
Prior Publication US 2024/0104480 A1, Mar. 28, 2024
Int. Cl. G06Q 10/06 (2023.01); G06N 5/02 (2023.01); G06Q 10/0639 (2023.01)
CPC G06Q 10/06393 (2013.01) [G06N 5/02 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
collating by one or more processors, data from a plurality of data sources into a first data repository;
identifying by the one or more processors, using a machine-learning model, class clusters and relationship clusters of the collated data in the first data repository, wherein the class clusters are identified based on one or more properties associated with the collated data, and the class clusters represent relationships of data and the relationship clusters represent relationships between class clusters;
generating by the one or more processors, a domain-specific semantic model as a graph-structured data model based on the identified class clusters and relationship clusters;
generating by the one or more processors, a data object model using the domain-specific semantic model and the collated data in the first data repository;
creating by the one or more processors, a first domain-specific knowledge graph by associating the data object model with the domain-specific semantic model;
creating by the one or more processors, a cross-domain analytics knowledge graph for deriving insights involving cross-domain analytics by merging the first domain-specific knowledge graph with a second domain-specific knowledge graph created from a second data repository, wherein said creating the cross-domain analytics knowledge graph comprises forming a relationship between at least one class of the first domain-specific knowledge graph and at least one class of the second domain-specific knowledge graph;
validating by the one or more processors, the identified class clusters and relationship clusters based on one or more inputs from a user received from a user interface, wherein validating the identified class clusters and relationship clusters comprises at least one of: modifying a name of a cluster by the machine-learning model based on a property of the cluster and modifying a relationship name of a relationship cluster by the machine-learning model based on a relationship between at least two clusters;
deriving by the one or more processors, one or more actionable insights corresponding to performance of one or more of an asset or a process in a facility based on utilization of the cross-domain analytics knowledge graph; and
displaying by the one or more processors, at least one actionable insight based on the one or more actionable derived insights for the cross-domain analytics knowledge graph.