Visa Entity Linking Using Subgraph Matching
Summary
The USPTO published patent application US20260099545A1 filed by Visa International Service Association for a system enabling entity linking through graph neural networks. The invention extracts attributes from unknown entities, generates graph representations, and matches them against known entity graphs using machine learning. The application, filed September 25, 2023 under application number 19115996, was published on April 9, 2026 with inventors Sheng Wang, Xutong Wang, Jie Yuan, Peng Wu, and Dan Wang.
What changed
The USPTO published Visa International Service Association's patent application for entity linking using subgraph matching technology. The system extracts attribute sets from unknown entities, generates graph representations, and uses a graph neural network to create embeddings that enable matching to known entities in a database.
This patent application does not create compliance obligations for third parties. It represents an intellectual property filing by Visa for graph-based entity resolution technology applicable to financial services operations. Entities in the financial services and technology sectors may reference this publication when developing similar technologies, as it establishes prior art in the graph neural network entity linking space.
Archived snapshot
Apr 18, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
ENTITY LINKING USING SUBGRAPH MATCHING
Application US20260099545A1 Kind: A1 Apr 09, 2026
Assignee
Visa International Service Association
Inventors
Sheng Wang, Xutong Wang, Jie Yuan, Peng Wu, Dan Wang
Abstract
Systems and methods for entity linking using a graph neural network are disclosed. In one aspect, a method for entity linking can include extracting a first attribute set of an unknown entity from an information source and retrieving second attribute sets of known entities from a database, wherein each of the second attribute sets corresponds to one of the known entities. The method can further include generating an unknown entity graph based on the first attribute set, generating known entity graphs based on the second attribute sets, generating an unknown entity graph embedding by applying the unknown entity graph to a graph neural network, and generating known entity graph embeddings by applying the known entity graphs to the graph neural network. The method can further include assigning the information source to one of the known entities based on the unknown entity graph embedding and the known entity graph embeddings.
CPC Classifications
G06F 16/9024 G06F 16/906 G06N 3/09
Filing Date
2023-09-25
Application No.
19115996
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