Improving Data Processes With Knowledge Graphs

Wednesday, June 10: 4:00 p.m. – 5:00 p.m. (ET) / 1:00 p.m. – 2:00 p.m. (PT)

Knowledge graphs are on the rise at enterprises hungry for more effective ways to connect the dots between the data world and the business world. Paired with complimentary AI technologies like machine learning and NLP, knowledge graphs are enabling new opportunities to leverage data not possible before and are quickly becoming a fundamental component of modern data systems. Attend this session to learn how you can use these technologies to change the game at your company.

4:00 p.m. – 4:45 p.m.

We are at the juncture of a major shift in how we represent and manage data in the enterprise. Conventional data management capabilities are ill equipped to handle the increasingly challenging data demands of the future. This is especially true when data elements are dispersed across multiple lines of business organizations or sourced from external sites containing unstructured content. Knowledge Graph Technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge Graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.

This presentation will describe the operational capabilities and benefits of Knowledge Graph technology: “The Future of Enterprise Data”. We will discuss how ontologies are the way forward to better represent enterprise definitions and data. The Financial Industry Business Ontology (FIBO) will be described as an exemplar of a semantically modeled knowledge graph for finance. We will describe how semantic graphs can be further enriched with probabilistic associations from machine learning and data mining algorithms. We will discuss the evolution of data from strings and numbers, to first class semantic objects to distributed representations of concept embeddings in vector space. We will also describe how Knowledge Graph technology can provide a layer of “knowledge” over legacy data structures to obtain maximum understanding of data inventory and provide the foundational building blocks for powerful data catalogs and hubs. Knowledge Graph also positions organizations to better support customer 360, risk management, regulatory compliance, technology asset management and many other use cases.

4:45 p.m. – 5:00 p.m.

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). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.

The Entity-Event Data Model we present puts core entities of interest at the center and then collects several layers of knowledge related to the entity as ‘Events’. Using this novel data model approach, organizations gain a holistic view of customers, patients, students, or important entities and the ability to discover deep connections, uncover new patterns and attain explainable results.

During this presentation we explain and demonstrate how Entity-Event Knowledge Graphs are the future of AI in the Enterprise.