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.


How to keep graph databases both flexible and secure - Dataconomy

#artificialintelligence

Graph databases are now common within a range of industries such as life sciences, healthcare, financial services, government and intelligence. Graphs are particularly valuable in these sectors because of the complex nature of the data and need for powerful, yet flexible data analytics. In addition, graph databases allow visibility across the data – enabling organizations to share data and show how data is connected. "Enterprises want the flexibility of graph databases, but they also want the security they have come to rely upon with relational databases," said Dr. Jans Aasman, CEO of Franz Inc. "Our new Triple Attributes gives organizations an elegant mechanism to implement the ultimate in graph database security." On November 6th, Franz Inc announced Triple Attributes for its Semantic Graph Database, known as AllegroGraph.


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.


Semantic graph database underpins healthcare data lake

@machinelearnbot

Franz Inc., in partnership with Montefiore Health System, is bringing the data lake to health IT using Franz's semantic graph database technology. Until its venture into the healthcare and pharmaceutical industries over the past few years, the 31-year-old Oakland, Calif., company had done business mainly in the worlds of national defense and intelligence, into which it sold its artificial intelligence-based triple store database that uses semantic, instead of relational, database technology. The system Franz has adapted for health IT, with partners such as Montefiore in the Bronx, N.Y., is based on AllegroGraph, one of its flagship products. Montefiore is using the system, called the Semantic Data Lake for Healthcare, to perform sophisticated predictive analytics in a quest to improve patient care and lower hospital costs. AllegroGraph uses the resource description framework (RDF) standard known as a "triple" to process and represent data semantically, and graph visualization software for visual discovery.


Graph Data on the Web: extend the pivot, don't reinvent the wheel

arXiv.org Artificial Intelligence

This article is a collective position paper from the Wimmics research team, expressing our vision of how Web graph data technologies should evolve in the future in order to ensure a high-level of interoperability between the many types of applications that produce and consume graph data. Wimmics stands for Web-Instrumented Man-Machine Interactions, Communities, and Semantics. We are a joint research team between INRIA Sophia Antipolis-M{\'e}diterran{\'e}e and I3S (CNRS and Universit{\'e} C{\^o}te d'Azur). Our challenge is to bridge formal semantics and social semantics on the web. Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. In this position paper, we emphasize the need to extend the semantic Web standard stack to address and fulfill new graph data needs, as well as the importance of remaining compatible with existing recommendations, in particular the RDF stack, to avoid the painful duplication of models, languages, frameworks, etc. The following sections group motivations for different directions of work and collect reasons for the creation of a working group on RDF 2.0 and other recommendations of the RDF family.