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For those of you who have been struggling through my series of posts on the TDWG Standards Documentation Specification, you may be happy to ...

TigerGraph is offering free Cloud and Enterprise Edition use for applications requiring massive data or high computation needs for Coronavirus research.

imec from Belgium. Semantic reasoning over streaming data Semantic reasoning with streaming data does not necessarily require specialised formalisms and tools. You can perform stream reasoning using Stardog with a relatively simple Python extension, which I’ll demonstrate below. Industry 4.0: Hello data Companies around the world are connecting their equipment, machinery and even people to the worldwide web. The Internet of Things (IoT for short) defines these connected things as smart devices, all of which are producing floods of data. This data has become a target for business intelligence and analytics to generate important business insights. However the raw data is often too opaque to process thus it has become a popular approach to annotate it with metadata and inte

proof data model. Marit explains knowledge graphs in detail, emphasizing that they can reduce training of machine models, allow new data to be input easily, and make analyzing unstructured data much easier. Knowledge graphs take data that would normally only make sense to humans and in effect tran

First data modeling is based on two assumptions. The first

Unstructured content is ubiquitous in today’s business environment. In fact, the IDC estimates that 80% of the world’s data will be unstructured by 2025, with many organizations already at that volume. Every organization possesses libraries, shared drives, and content management systems full of unstructured data contained in Word documents, power points, PDFs, and more. Documents like these often contain pieces of information that are critical to business operations, but these “nuggets” of information can be difficult to find when they’re buried within lengthy volumes of text. For example, legal teams may need information that is hidden in process and policy documents, and call center employees might require fast access to information in product guides. Users search for and use the information found in unstructured content all the time, but its management and retrieval can be quite challenging when content is long, text heavy, and has few descriptive attributes (metada

We’ve just released a new version of our Stardog Studio IDE. Now we want to tell you all about it.

Subgraphing is a common use case when working with graphs. We often find ourselves wanting to take some small portion of a graph and then operate only upon it. Gremlin provides subgraph() step, which helps to make this operation relatively easy by exposing a way to produce an edge-induced subgraph that is detached from the parent graph.

Last year we concluded a ten-month pilot, called “Project Passage”, using a Wikibase instance to create and edit library metadata in collaboration with 16 U.S. libraries. Our upcoming report documenting …

RDF* is an extension to RDF that provides

Our interview with Teodora Petkova, sharing her views on weaving text into the semantic web and her recent book The Brave New Text.

LinkedSDGs: linking information resources & discovering connections to #SDGs 1) upload document 2) extract key concepts 3) find links to #SDGs & relevant stats #linkeddata & knowledge extraction resources @SustDev #opendata #data #tech #sustainability [LINK]https://www.linkedin.com/feed/update/urn:li:activity:6628584405906661376/ [LINK]https://media-exp1.licdn.com/dms/image/C5622AQELLgccoCKFCw/feedshare-shrink_800/0?e=1583366400&v=beta&t=pfry2nj7rsH7Ex5cOs36op-Wdn0Y1lyvsYVUhMzEj7k

To get insights from between the lines, you can correlate and categorize the tags of StackOverflow based on the questions they are…

Ontological metadata, being machine- as well as human-, readable, can drive processes, reduce data redundancy, and can be used to surface new information through inferencing and rich querying/updates. Ontologies will enrich taxonomies and make them more useful to the organization overall.

The distinctions between machine learning and semantics are disappearing - they are both simply tools for managing the metadata associated with the data that flows through every organization and domain.

Taxonomy Development in a #LinkedData World by @aaranged @cosmicangler at @tbc_london The big idea: Because the world we live in is big and complex, we need to go about the job of taxonomy development in a way that matches that

Ahren Lehnert looks at lifecycles and versioning. The basic governance practices are common when managing taxonomies and ontologies.

One might expect to see “Long Live Taxonomy!” following the above statement just to keep the tradition going of announcing a new king, but sadly that’s not the case. Taxonomy, as we’ve known it, built it, used it, sold it and relied on it for a living for a long time, is, according to Gartner’s 2018

sized – offer products and services to help in the fight.

/PRNewswire/ -- tellic graph.C19 visualizes 12 million connections between COVID-19 and related genes, drugs, phenotypes and variants Scientists at Empire...

What is an RDF database and what it brings to the table for companies when it comes to researching, authoring and publishing content.

world problems involving networks of transactions of various nature and social interactions and engagements are dynamic and can be modelled as graphs where nodes and edges appear over time. In this post, we describe Temporal Graph Network, a generic framework for deep learning on dynamic graphs.

An open source database for data people. Join the data-centric revolution!

Machine learning reveals knowledge of materials ‘hidden’ in papers.

Learn more about text-mined knowledge graphs and see how to build one with Grakn.

Photo by AndreyPopov on iStockWe all use the Google Knowledge Graph tens of times a day, but maybe not many of us are aware to be actually querying the Graph while making a simple search on Google.When you search for something, for example, “Goldman Sachs”, what you get is a list of snippets of web pages plus an Infobox next to the search results.The Knowledge Graph behind your Google search allows to enhance the search engine with specific and possibly useful features on the “entity” you are looking for (in this case Goldman Sachs), gathered from a variety of sources. So, allegedly, Google Knowledge Graph enhances the result of our search with semantics [4].Let’s now try to consider reasoning.Now, say we are studying Goldman Sachs for some reason and we wish to know whether there is some person x in Goldman Sachs board who is the CEO of some other company y that is in the Tech field?Or in other terms, in a ‘fancy’ logic conjunctive query fashion:∃ x y board(Goldm

Knowledge graphs and their relation to taxonomies, ontologies, and other knowledge organization systems.

WHAT THIS TALK IS ABOUT My personal take on the history of human label collection ter to you

Cases of the Wuhan coronavirus have been confirmed around the globe, reminding us that the capacity to quickly develop, test and deploy new drugs is essential to save lives. Unfortunately, experts say we still are years away from a vaccine for the virus. However, a type of artificial intelligence can speed up the drug development process that already exists, but it hasn’t yet been deployed at scale.

time.Therefore, we have recognised that building a “Talent Knowledge Graph” will allow us to effectively operate in our domain as well as to fully unlock the predictive power of artificial intelligence and provide unique insights from our heterogeneous data sources.After Google’s reveal of its Knowledge Graph platform in 2012, the term has been rapidly growing in popularit