GraphNews

If we fail to predict the future they call us a failure, and if we do it too well they call us a sorcerer.

If we fail to predict the future they call us a failure, and if we do it too well they call us a sorcerer.

Leveraging the Neo4j Community Graph For Social Discovery

Built-in Jupyter notebooks for Azure Cosmos DB are now publicly available. Developers, data scientists, engineers and analysts can use the familiar Jupyter notebooks experience to interactively run...

Looking for a way to connect your BI solution with ArangoDB? Qlik, Tableau, Power BI or Grafana

Mixing graph and relational data is now even more accessible. What does this mean for analytics, and for graph databases specifically?

When extracting information from documents using NLP, attempting to separate important terms from noise can be tricky. Calculating a TF-IDF (Term Frequency-Inverse Document Frequency) score allows you to identify the important terms in a document.

#CaLiGraph, a #knowledgegraph which comes with 5.6M instances and a rich ontology with 750k classes and 200k class restrictions compiled from the @DBpedia ontology and @Wikipedia Categories & Listpages announced by @heikopaulheim @nheist @dwsunima http://caligraph.org

Looking for a visualization element to add to your React application? Cambridge Intelligence today launched an Early Access Program for ReGraph, a brand new graph data visualization toolkit for React developers.

BOSTON (PRWEB) March 25, 2019 Cambridge Semantics, the leading provider of modern data discovery and integration software for enterprise data fabrics, today announced the addition of OpenCypher t

BOSTON (PRWEB) August 22, 2019 Cambridge Semantics, the leading provider of modern data management and analytics software, today announced significant updates to its award-winning Anzo® data disc

BOSTON (PRWEB) January 31, 2019 Cambridge Semantics, the leading provider of big data management and enterprise analytics software, today announces availability of its AnzoGraph Graph warehouse (

BOSTON (PRWEB) January 23, 2019 Cambridge Semantics, the leading provider of big data management and enterprise analytics software, announced that its AnzoGraph Graph Database product was rated h

Disclaimer: It is against my employer’s policy to endorse any specific vendor. This article is only a general observation of trends in the…

This week, Cambridge Semantics was named a Leader in The Forrester Wave™: Enterprise Data Fabric, Q2 2020, and we could not be more delighted. Forrester used 25 criteria to evaluate the 15 most significant enterprise data fabric vendors to show how each vendors' platforms measure up in their ability to accelerate data integration, minimize the complexity of data management, and quickly deliver use cases. Cambridge Semantics received top scores for Vision, Road Map, Solution Awareness, Data Preparation, Data Integration, Data Catalog, and Data Processing. This evaluation, we believe, validates Cambridge Semantics' breakthrough approach to the data fabric, and reflects the feedback we are hearing from customers and partners using our solution to integrate and manage large volumes of data quickly and at scale.

Cambridge Semantics AnzoGraph Recognized as 2019 Trend-Setting Product - Cambridge Semantics

A use-case study for Twitter users and the power of machine learning and graph neural networks to detect hateful online users.

based Technology Enables Companies in the Financial Sector to Quickly and Efficiently Integrate and Analyze the Explosion of Disparate Data from RegulatorsOver the past decade, the advance of digital innovation combined with the growing number of regulatory guidelines in all jurisdictions has sped up the rise of Regulatory Technology, or RegTech for short. Originally, its main application was to enhance regulatory processes in the Financial sector, but nowadays it is expanding more and more in to any regulated business. A good sign that this technology is steadily going mainstream is also the fact that more and more regulatory bodies are supporting RegTech development and implementation.According to a September 2019 report from Juniper Research, the value of global spending on RegTech is expected to jump to more than US $127 billion by 2024, up from US$25 billion spent in 2019. Automation in Know Your Customer (KYC) processes and background checks are set to be so

Many graph applications can benefit from the ability to capture changes to items stored in an Amazon Neptune database, at the point in time when such changes occur. Amazon Neptune now supports Neptune Streams, a fully managed feature of Neptune that reliably logs every change to your graph as it happens, in the order that […]

Case Study: In the geoscience community, collaboration is critical. Different disciplines — engineering geologists, geochemists, hydrologists — need to share their findings with each other to address big questions about the earth. Ontologies and knowledge graph technology is leveraged with COR to manage and exchange terms and vocabularies that assist scientists in publishing, discovering, and reusing data.

Roundup of #AWS Neptune #graphDB recent announcements, plus insights by @TonyBaer: Given much data, #IoT #socialnetworks etc, captured by graphs lives outside #datacenter, one more reason #cloud should be natural home for graph #databases. Getting there

Roundup of #AWS Neptune #graphDB recent announcements, plus insights by @TonyBaer: Given much data, #IoT #socialnetworks etc, captured by graphs lives outside #datacenter, one more reason #cloud should be natural home for graph #databases. Getting there

.@CausalyAI #AI #medical #research #startup gets $5M investment to enable discovery of causal evidence, insights from documents. Machine-reading platform turns text to causal #knowledgegraphs applies #machinelearning to surface new knowledge #EmergingTech

Cf. this discussion on the #schema.org mailing list kicked off by this post's author lists.w3.org/Archives/Publi…

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My new paper on ontologies in Data and SW engineering https://t.co/jqQAEMZbEl @nimonika— Christian Dirschl (@HaintzD) January 3, 2019

making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to i