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Knowledge graphs can aid successful KM strategies

Knowledge graphs have become the go-to solution for greater automation and intelligence, enabling use cases across industries from fraud detection and chatbots to risk analysis and recommendation engines.

The increasing size and diversity of information accessible to workers today, alongside the growing demand for actionable insights, is making knowledge graphs a vital component of many KM strategies.

KMWorld recently held a webinar with Sean Martin, founder and CTO of Cambridge Semantics, and Ben Szekely, co-founder and SVP, field operations of Cambridge Semantics, who discussed leveraging knowledge graphs for better knowledge management.

There has been a rising demand for graph and data fabric heading into this year, Martin and Szekely explained.

A knowledge graph is a connected graph of data and associated metadata applied to model, integrate and access an organization’s information assets.

It represents real-world entities, concepts and events as well as all the relationships between them yielding a more accurate representation of a business’s data, they noted.

Knowledge graphs and ontologies work hand in hand to simplify access to complex data to address unanticipated questions. By incorporating ontologies, it quickly profiles, connects, and harmonizes data from multiple sources, including unstructured.

It can present tailored views, services, and experiences to different personas with conceptual models. Ontologies can flexibly accommodate new data sources and use cases on the fly, with minimal impact and scale horizontally to accommodate enterprise data fabric scale—cloud agnostic.

According to Martin and Szekely, Anzo’s graphmarts offer three ways to work with Knowledge Graphs in-memory:

  1. Load onboarded RDF graph data from disk
  2. Load data into memory directly from sources, APIs, streams
  3. Load data at query time through virtualized views

An archived on-demand replay of this webinar is available here.

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