Wells Fargo Bank, N.a.

Omar B Khan

Alexander Joseph Kalinowski

Ramanathan Ramanathan

Abhijit Rao

Andrew J Garner, Iv

Michael Erik Meinholz

Ramesh Yarlagadda

Pierre Arbajian

Chao Chen

Nikolai Anisimov

Nathan Anthony Susanj

Pierre Arbadjian

Steven Daryl McCullough

Nisha Rama Krishnan

Cristian Cocos

Matthew R Ruble

A method may include storing a vector space representation of a set of concepts in a semantic ontology, wherein locations of the set of concepts in the vector space are based on weights of a neural network trained using triples from the semantic ontology; presenting a semantic search user interface including: a text input portion to receive a search query from an input device; a suggested search portion; and a search results portion; receiving, from a user computing device, the search query; converting, using at least one processor, the search query into a vector; computing, using the at least one processor, a set of possible concepts related to the search query based on a distance between the vector and other concepts in the vector space representation; and presenting the set of possible concepts related to the search query in the suggested search portion of the semantic search user interface.

Semantic entity search using vector space

A method may include presenting a user interface on a computing device, the user interface including: a service provider input element identifying a service provider; a service identifier input element identifying a service; and a graph presentation area; executing a knowledge graph database query using a combination of the service provider and the service as input to a knowledge graph database; receiving tuple results in response to the executing, the tuple results including an allocation value property of the service provider attributable to the service provider with respect to the service; and generating in the graph presentation area, an interactive graph based on the tuple results including: representations of entities including the service provider and the service in the tuple results as nodes in the interactive graph, wherein a representation of the service provider includes the allocation value; and links connecting the representations of entities.

User interfaces for data traversal of knowledge graphs

A method may include accessing a semantic map between a schema of a relational database (RDB) and a resource description framework (RDF) data model, the semantic map defined according to a first semantic ontology; applying the semantic map to data loaded from the RDB to generate a first set of semantic objects; storing the first set of semantic objects in a staging RDF triplestore; executing a script to transform the first set of semantic objects into a second set of semantic objects according to a second semantic ontology; and storing the second set of semantic objects into a production RDF triplestore.

Semantic translation of data sets

A method may include accessing, using a processing unit, a source chain datafile formatted in a first format, the source chain datafile including data entries identifying linkages between a service provider, a service receiver, and a component provider, of an enterprise; forming, using the processing unit, initial knowledge graph tuples including subject, object, and predicate components based on the linkages; generating, using the processing unit, a staging knowledge graph storing the initial knowledge graph tuples; translating, using the processing unit, the initial knowledge graph tuples into production knowledge graph tuples according to a source chain knowledge graph schema; and storing, using the processing unit, the production knowledge graph tuples in a production knowledge graph.

Systems and methods for data translation of source data files

A method may include presenting a user interface on a computing device, the user interface including: an input element configured to receive an input of a search concept; a similar concept selection element; and a concept comparison portion; executing a similarity search in a vector-space representation of a knowledge graph using the input; populating the similar concept selection element with a set of a similar concepts based on a result of the executed similarity search; receiving a selection of a comparison concept of the set of similar concepts from the similar concept selection element; generating a predicate comparison visualization in the concept comparison portion based on predicates in the knowledge graph of the comparison concept and the search concept; and presenting the predicate comparison visualization in the concept comparison portion.

Identity resolution in knowledge graph databases

Systems, methods, and computer program products are provided for disparate quantum computing (QC) detection. An example system includes QC detection data generation circuitry configured to generate a set of QC detection data. The example system further includes cryptographic circuitry configured to distort the set of QC detection data via a first PQC cryptographic technique, generate a pair of asymmetric cryptographic keys comprising a public cryptographic key and a private cryptographic key via a second PQC cryptographic technique, generate encrypted QC detection data from the distorted set of QC detection data based on the pair of asymmetric cryptographic keys, and destroy the private cryptographic key. The example system further includes data monitoring circuitry configured to monitor a set of data environments for electronic information related to the encrypted QC detection data.

Systems and methods for disparate quantum computing threat detection

Systems, methods, and computer program products are provided for quantum computing (QC) detection. An example QC detection system includes QC detection data generation circuitry that generates QC detection data. The QC detection system also includes cryptographic circuitry that distorts the QC detection data via a first post-quantum cryptographic (PQC) technique and generates a pair of asymmetric cryptographic keys including a public cryptographic key and a private cryptographic key. The cryptographic circuitry further generates encrypted QC detection data based on the pair of asymmetric cryptographic keys and destroys the private cryptographic key. The QC detection system further includes data monitoring circuitry that monitors a set of data environments for electronic information related to the encrypted QC detection data.

Systems and methods for quantum computing threat detection

A method may include retrieving, over a network device, data from a data source, the data formatted according to a relational database schema; converting, using at least one processor, the data from the data source into a knowledge graph; training, using the at least one processor, a neural network using triples from the knowledge graph, wherein a loss function of the neural network is based on a distance between a true triple from the knowledge graph and a corrupt triple; and after the training, storing weights of a hidden layer of the neural network as a vector space representation of entities in the knowledge graph.

Machine learning using knowledge graphs

A method may compiling, using at least one processor, a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object; generating a vocabulary of terms based on the corpus of training data; training a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms; and storing a set of word embeddings for the vocabulary of terms based on the trained semantic model.

Fuzzy search of graph database

Systems, methods, and computer program products are provided for disparate quantum computing (QC) detection. An example system includes QC detection data generation circuitry that generates a first set of QC detection data and generates a second set of QC detection data. The system also includes cryptographic circuitry that generates a first public cryptographic key and a first private cryptographic key via a first post-quantum cryptographic (PQC) technique and generates a second public cryptographic key and a second private cryptographic key via a second PQC technique. The cryptographic circuitry further generates encrypted first QC detection, second QC detection data, and destroys the first private cryptographic key and the second private cryptographic key. The system further includes data monitoring circuitry that monitors for the first encrypted QC detection data and the second encrypted QC detection data.

A method may include storing a vector space representation of a set of concepts in a semantic ontology, wherein locations of the set of concepts in the vector space are based on weights of a neural network trained using triples from the semantic ontology; presenting a semantic search user interface including: a text input portion to receive a search query from an input device; a suggested search portion; and a search results portion; receiving, from a user computing device, the search query; converting, using at least one processor, the search query into a vector; computing, using the at least one processor; a set of possible concepts related to the search query based on a distance between the vector and other concepts in the vector space representation; and presenting the set of possible concepts related to the search query in the suggested search portion of the semantic search user interface.

Systems, methods and computer program products are provided for layered quantum computing (QC) detection. An example system includes QC detection data generation circuitry that generates QC detection data via a first post-quantum cryptographic (PQC) technique. The system also includes cryptographic circuitry configured to generate a pair of asymmetric cryptographic keys including a public cryptographic key and a private cryptographic key via a second PQC technique, generate encrypted QC detection data based on the pair of asymmetric cryptographic keys, and destroy the private cryptographic key. The system further includes data monitoring circuitry configured to monitor a set of data environments for electronic information related to the encrypted QC detection data. In response to detection of the electronic information related to the encrypted QC detection data, the system may monitor a set of data environments for electronic information related to the QC detection data.

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Richmond, VA, United States of America

Omar B Khan