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.

In social network analysis, a conventional approach relies heavily on available metadata, allowing to match a virtual entity (social network account) to...

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

IMO there's no need to mix-in #SPARQL into #SQL. There's SQL/MED. Only few things are missing in standard SQL to query distributed #LinkedData: 1. HTTP endpoint 2. SPARQL SERVICE alike SQL keyw. Who helps proposing a spec. about this? @danbri Is @w3c the right place to do this?— Stefan Keller (@sfkeller) February 23, 2019

Google's new search quality evaluator guidelines are out, dated yesterday https://t.co/owM4OJHGGV— Stephen Kenwright (@stekenwright) May 17, 2019

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.

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

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?

#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

This post presents the ecosystem of graph visualization. Read on to find the full list of graph visualization software and graph visualization libraries.

This post is a series of 3 articles about the GraphTech ecosystem. This article covers the graph analytics landscape: frameworks, libraries, query languages

tier_architectureHowever, knowledge graphs propose a paradigm shift to this design blurring the barrier between logic and data. By bringing some of the knowledge of the domain into a graph through rules a knowledge graph captures more than just the data in the system. As a result, rules can make the queries and requests much simpler to write and manage which in turns allows applications to be more flexible, less error prone and faster.This article will introduce a simple example to showcase the impact of rules on query design. The example will be ill

Boettiger, (2019). Ecological Metadata as Linked Data. Journal of Open Source Software, 4(34), 1276, https://doi.org/10.21105/joss.01276

thanks for making them available Ruben! twitter.com/RubenVerborgh/…

The most depicted humans in #paintings #SPARQL query on #Wikidata looks an #artwork itself https://t.co/MgWi4bCDPo (ht @sanseveria ) pic.twitter.com/qMjZtNkCFM— Maarten Dammers (@mdammers) January 18, 2020

Talk about the Semantic Web and Linked Data, and the conversation will usually turn towards the concept of inferencing. Inferencing is the…

In 2019, I was asked to write the Foreword for the book “Graph Algorithms: Practical Examples in Apache Spark and Neo4j“, by Mark Needham and Amy E. Hodler. I wrote an extensive piece on the power of graph databases, linked data, graph algorithms, and various significant graph analytics applications. In their wisdom, the editors of the book decided that I wrote “too much”. So, they correctly shortened my contribution by about half in the final published version of my Foreword for the book.

For more complex products such as new pharmaceutical drugs, medical equipment or healthcare treatments, identifying hubs of influencers among physicians and other healthcare providers requires deep analysis of patient claims data to uncover the referral relationships.

here's what's new in 20c: blogs.oracle.com/oraclespatial/… #oraclegraph #Analytics #DataScience https://t.co/hXlgPgxcxJ

For a domain with a strong focus on unambiguous identifiers and meaning, the Semantic Web research field itself has a surprisingly ill-defined sense of identity. Started at the end of the 1990s at the intersection of databases, logic, and Web, and influenced along the way by all major tech hypes such as Big Data and machine learning, our research community needs to look in the mirror to understand who we really are…

Connected Data London community cast. Gavin Mendel-Gleason: Quick overview of query language and how we got here

making, effectiveness and competitive edge.

How graph databases can be used to explore and analyse the decentralised XRP ledger, focusing on exploring networks of fraudulent accounts and distribution patterns.

This year, the @UGent Web Development course kicks off with a very special guest: @timberners_lee introduces to our students his invention that changed the world. Not “vague but exciting”—rather crystal clear and as passionate as ever. https://t.co/E4JQH3bFfM

Those interested in a comprehensive overview of the current status of Linked Data Integration (at large scale) can read our ACM Computing Surveys (!) article. Preprint: https://t.co/uwGeGcm1UHCongrats @mountantonakis #LinkedData #RDF, #Integration #KnowledgeGraphs pic.twitter.com/NyN4XJIAYo— Yannis Tzitzikas (@YTzitzikas) September 16, 2019

I’ve been thinking on and off about Rich Sutton’s piece The Bitter Lesson. Here are three corners that I think Sutton is cutting too quickly: 1. Moore’s Law is way too slow to mak…

TigerGraph 2.4 Enables Enterprises to Discover Deep Insights Missed by Competing Graph Databases