In part 1 of this article series, I provided a quick primer on graph data structure, acknowledged that there are several graph based algorithms with the notabl…

.@DDIAlliance releases XKOS v1.2 (Extended Knowledge Organization System) specification →facilitates sharing & management of statistical classifications.(For building #Semantic Taxonomies to tag Datasets & #DataScience outputs)https://t.co/HZD1nl5Nu0#BigData #Ontologies #RDF pic.twitter.com/DDYbHqCvm7— Kirk Borne (@KirkDBorne) June 21, 2019

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We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades. Over the last few years, a number of new graph databases

based models have shown their unique advantages in knowledge graph (KG)

It was hard to top last year’s demonstration of the new Microsoft 365 experienced powered by the Microsoft Graph. The focus this year shifted to how the graph could break down application and device silos to make everyday applications more user-centric.

While the term “Knowledge Graph” is relatively new (Google 2012) the concept of “representing knowledge as a set of relations between entities - forming a “graph” has been around for much longer. 2019 marks, for example, the 20th anniversary of the publication of arguably the first open standard for

Discover what's new in the 3.5 release of the Neo4j graph database, including a Go language driver, full-text search and indexing, TLS encryption and more.

based Gruff Drives Infinite Data Integration, Holistic Insights and Complex Reasoning

“Q: which product taxonomies are used in the Shopping / Ad industries? Google's Product Taxonomy is a de facto standard but it lacks freshness, coverage and/or finesse in some areas. I'm also looking at product taxonomies from Amazon, Ebay, Walmart, Target, Groupon. What else?”

Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI

https://douroucouli.wordpress.com/2019/05/10/obographviz-0-2-2-released-now-with-visualization-of-equivalence-cliques/

I have submitted mine. Find it at https://t.co/tdWjN8NhdX"The RDF* and SPARQL* Approach to Annotate Statements in RDF and to Reconcile RDF and Property Graphs"— Olaf Hartig (@olafhartig) January 11, 2019

Keynote talk given at the 41st European Conference on Information Retrieval (ECIR 2019)

Obfuscation traditionally gives analysts a bit of a hard time. Graph databases can help look past the smoke and mirrors. Learn more on the G DATA TechBlog!

Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market

Norconex crawlers can now be used to harvest relationships from websites and filesystems and feed them to Neo4j.

Updates on #knowledgegraphs affect services built on top of them. But not all changes are the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation #research #award #iswc_conf

Querying the first RDF Data Cube based on an existing PC-Axis/PX data cube published by @swissstatistics. Thanks to @bergi_bergos for the great converter, will be made public later pic.twitter.com/4SJ3jpKg7e— Adrian Gschwend (@linkedktk) July 10, 2019

Representing Graph #Data Structures: Edge Lists, Adjacency Matrices, Adjacency Lists #datascience #analytics #tutorial #datamodel #softwareengineering #data #tech

A case for response time focused query processing by @olafhartig at #ISWC19 Rethinking optimization criteria for Web querying. Optimizing Query execution time is not the same as optimizing Query Response time #research #www #linkeddata #keynote

Learn advanced Cypher queries for one approach to analyzing data inconsistencies directly in your flexible and forgiving Neo4j graph database.

Trace the safety information throughout the entire drug development lifecycle in a single intelligent system with Ontotext's solution.

All organisations find it difficult to fully describe their organisation in data. One of the most common pitfalls is underestimating the number of human-driven connections between data which have never been captured digitally.

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

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

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

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?