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Onboarding to Enterprise Knowledge Graphs - DATAVERSITY

@machinelearnbot

Enterprise Knowledge Graph vendors are working hard to find their place in the heart of businesses, helping them do more with and get more out of their mountains of data. Recently, for example, Stardog has adopted its leading Knowledge Graph platform to be "FIBO-aware," mapping to the Financial Industry Business Ontology (FIBO) semantic standards out-of-the-box. GraphPath launched what it says is the first Knowledge-Graph-as-a-Service (KGaaS) platform. And Maana, with its Knowledge Graph-centered Knowledge Platform, has been talking up its partnerships with clients like Shell to drive digital transformation efforts. As part of these efforts, work is underway to make it easier for businesses to adopt these solutions – for experts like data engineers who will manage the graphs, of course, but also for the business users who will consume data from them via different applications that developers create.



Introducing a Graph-based Semantic Layer in Enterprises

@machinelearnbot

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.


The Many Shades of Knowledge Graphs: Let Me Count the Ways

#artificialintelligence

One of the most significant developments about the current resurgence of statistical Artificial Intelligence is the emphasis it places on knowledge graphs. These repositories have paralleled the contemporary pervasiveness of machine learning for numerous reasons, from their aptitude for preparing training datasets for this technology to pairing it with AI's knowledge base for consummate AI. Consequently, graph technologies are becoming fairly ubiquitous in a broadening array of solutions from Business Intelligence mechanisms to Digital Asset Management platforms. With tools like GraphQL gaining credence across the data landscape as well, it's not surprising many consider knowledge graphs one of the core technologies shaping modern AI deployments. As such, it's imperative to understand that all graphs are not equal; there are different types and functions ascribed to the various graphs vying for one another for the knowledge graph title.


2017 Trends for Semantic Web and Semantic Technologies - DATAVERSITY

#artificialintelligence

Are you hearing the term "Semantic Web" as often as you may have in the past? There's no denying the importance of the technologies, standards, concepts, and collaborations that define the Semantic Web proper and all that is affiliated with it or grown out of it. But if anything, the terms "Semantic Web" or "Semantic Web technologies" are receiving less attention, points out Amit Sheth, educator, researcher, and entrepreneur whose roles include being the executive director of Kno.e.sis--the Ohio Center of Excellence in Knowledge-enabled Computing. As we head into 2017, DATAVERSITY wanted to follow up the state of the Semantic Web and Semantic technologies (both standards-body related and not). In addition to Sheth, Michael Bergman, co-founder of knowledge-based Artificial Intelligence startup Cognonto (see our recent article here) and CEO of Structured Dynamics, and David Wood, CTO of 3 Round Stones, Director of Technology at Ephox TinyMCE and author of books including Linking Enterprise Data, also participated.