→ knowledge graphs
A Knowledge Graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts. These “nodes” are then connect to each other by “edges”, that defines the relationship between the two nodes.
Ingentium uses the Ingentium Knowledge Graph Scaffold as a starting point to construct a disease or topic based knowledge graph. At this time there are more than 35 node types, with over 34M nodes in the scaffold. There are more than 20 edge types, with more than 17M edges in the scaffold.
Ingentium takes the information from a disease topic focused knowledgebase to create knowledge graphs, adding other sources when available. We use a novel big data cyber-infrastructure technology, leveraging machine learning and semantic analysis, to aggregate and refine the latest news and information into focused, disease-specific knowledge bases. Natural language processing (NLP) and semantic predication are used to identify relationships (edges) between evidence types in the knowledgeable (nodes) build contextualized knowledge graphs using learned content. Our knowledge bases are also used to deliver media to patients, physicians, and the medical research community in our Health Magazines, a component of the Ingentium Health Network, which updates daily. We update our knowledge graphs on a regular schedule, usually weekly.