Entity-Event Modeling for Ubiquitous AI Knowledge Graphs
Franz’s Knowledge Graph approach encapsulates a novel Entity-Event Model, natively integrated with domain ontologies and metadata, and dynamic ways of setting the analytics focus on all entities in the system (patient, person, devices, transactions, events, operations, etc.) as prime objects that can be the focus of an analytic (AI, ML, DL) process.
Entity-Event Knowledge Graphs are about moving away from statistical averages and broad-based patterns in order to connect the many dots, from different contexts and throughout time, to support and recommend industry-specific solutions that can take into account all the subtle differences and nuisances of entities and their relevant interactions to deliver insights and drive growth.
To support ubiquitous AI, a knowledge graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). The rich functional and contextual integration of multi-modal, suitable for large scale analytics, predictive modeling, and artificial intelligence is what distinguishes AllegroGraph as a modern, scalable, enterprise analytic platform. AllegroGraph is the first big temporal knowledge graph technology that encapsulates this novel entity-event model natively integrated with domain ontologies and metadata.
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