Abstract: Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementations, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.

Abstract: Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementation s, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.

Abstract: Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementation s, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.

Abstract: Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementations, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.

Abstract: Techniques are described herein to improve onboarding of third party entity data with existing knowledge graphs. In various implementations, a computing system associated with an existing knowledge graph may receive a request from a third party to onboard, with the existing knowledge graph, a plurality of entities. Each entity may have associated identifier(s) and relationship(s) with other entities of the plurality of entities. First third party entity data that describes the plurality of entities and associated identifiers/relationships may be received from the third party. The first third entity party data may be analyzed to identify semantic fingerprint(s) matching respective subsets of the entities. Results related to the analyzing may be determined. The results may include a statistic representing success or failure of applying rule(s) to a respective subset of entities that match a given semantic fingerprint. Remedial action(s) may be triggered based on the failure statistic.

Abstract: A new and improved translation circuit that accepts TTL signals and converts them to ECL levels while performing an AND/NAND function is provided, comprising at least two emitter-coupled transistor pairs, each coupled to an input terminal for receiving corresponding TTL signals and coupled to one another for performing the AND operation. Each emitter-coupled pair is also coupled to a bias drive for providing reference voltages that designate which one of each of the transistor pairs conducts, depending upon the state of the TTL signal received.