The Semantic Web is twenty years old, and like many twenty-year-old technologies, many of the original concepts that drove the technology have become increasingly outdated even as the need for a compelling graph technology story has only become greater.
Several technologies have arisen in the last few years, including JSON, machine learning systems, GraphQL, Lambdas, and others.
What's more, the first generation semantic web has struggled to assert itself while dealing with problems such as the failure of adoption of complex ontologies, data science workflows that have largely bypassed RDF as a mechanism for expressing rich data structures, slow adoption of even foundational technologies such as Property Graphs, RDF-Star, SHACL, or JSON-LD.
The focus of this talk is to explore the emergence of second-generation knowledge graphs, utilizing GraphQL as a means to abstract out data models and construct both set and hierarchical structures in a manner that makes sense to the data science, machine-learning, and web communities.
Additionally, such tools make mutational processing independent of platform, and even, utilizing such abstractions, make it possible to create truly functional and dynamic properties in the space.
This author hopes to explore how GraphQL and related paradigms (DIDs, machine learning models, web component frameworks, distributed ledgers and hyperstatial systems) all will impact the future of the knowledge graph over the course of the next decade and beyond.
About This Speaker
Kurt Cagle is the managing editor for Data Science Central and has been a regular writer, industry analyst and speaker on graph systems, machine learning, data science, artificial intelligence, IoT, metaverse, and related topics.
Kurt has has twenty four books and more than two thousand articles and blog posts in publication. He has consulted with a number of Fortune 50 companies and federal agencies on data technologies for the last decade. He lives in Issaquah, WA with his family and cats. ---
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