learning by itself is only half a solution.To explain this (and the relationship that graphs have to machine learning and AI), it's worth spending a bit of time exploring what exactly machine learning does, how it works. Machine learning isn't actually one particular algorithm or piece of software, but rather the use of statistical algorithms to analyze large amounts of data and from that construct a model that can, at a minimum, classify the data consistently. If it's done right, the reasoning goes, it should then be possible to use that model to classify new information so that it's consistent with what's already known.Many such system

While graph #tech #GraphDB graph computation will not be everything, true #digitaltransformation will only get underway once we begin building out #knowledgegraphs to mediate the hardest part of #datamanagement: integration #innovation #data #2020NewYear

file contained a list of properties separated by a unique character. Each subsequent row contained the val

#knowledgegraphs differ from relational DBs primarily in how information gets stored. KGs consist of index holding at least three values: subject, predicate, object (triple). In typical triple stores, the index is the database. This has several advantages

Property graphs seem inherently simpler. Semantic graphs put more emphasis on #datamodeling, treat node/edges as concepts bound by a universally unique identifier #data #tech #knowledgegraph #EmergingTech #datamodel #datascience #software @kurt_cagle [LINK]https://www.linkedin.com/pulse/state-graph-merger-property-graphs-semantic-kurt-cagle [LINK]https://media-exp1.licdn.com/dms/image/C5612AQF2DIFR7DjXKQ/article-cover_image-shrink_600_2000/0?e=1585785600&v=beta&t=tN9Z8ZbE1VeNHiPvhdFfDi34VwHW_pOHXPWv7Il8f7Q

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