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

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

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…

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.

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

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

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

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

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

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

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

I realized that I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules?

The best (and new) survey on the theoretical aspects of GNNs I'm aware of. So many illustrative examples of what GNN can and cannot distinguish.

OpenAI believes that huge computing power is key driver

If information does not exist in electronic form by now in your organization, it is very likely already lost.

#knowledgegraphs need #DLTs to secure keys, DLTs need knowledge graphs to provide context & provenance. Ultimately, knowledge graphs will end up being the integration point for a number of #technologies lumped under #AI #data #EmergingTech @kurt_cagle

“You must unlearn what you have learned” (Master Yoda) The “conceptual” level

.@shivamshaz #datascientist who rules #DataScience for Good competitions on #Kaggle wants to apply expertise in #ML to solve micro-finance problems for underbanked population in developing countries using network science, graph theory, unstructured data

The emerging landscape for distributed knowledge, #ontology, #semanticweb, knowledge base, graph based technologies and standards. Current trends related to graph based #technology #knowledgegeraph #analytics #graphDB #AI #datascience #longread @nfigay

Much (if not all) of the discussion about temporal issues in the last 30+ years have been based on the assumption of the necessity of SQL tables. The narrative for how to build “well-formed” SQL data models is the well-known “Normalization” procedure. Data modelers with my hair color will remember the poster, which was a give-away with the Database Programming and Design Magazine (Miller Freeman) in 1989. The title of the poster is “5 Rules of Data Normalization”. Here is a miniature image of it

Meetup organised by Connected Data London, 29th January 2019, London. Don't miss the first meet up of the year!

The Great(er) Bear - using @Wikidata to generate better artwork. Wikipedia holds structured #data about people and things. It uses SPARQL to query that data. @edent writes something to query Wikidata to generate a more accurate artwork #dataviz #graphDB

Lee — the computer scientist best known as the inventor of the World Wide Web — himself who dreamt of a place full of readable data, neatly linked. Years later, we are working towards that goal, thanks to a vocabulary called Schema. This article tells you a bit more about how we got here.