RDF vs. Property Graphs! hear @joshsh and @jansaasman of @Franzinc on @TheGraphShow episode #1https://t.co/yOaqdmNqWR#Ontology #GraphDatabases #Semantic #RDF #KnowledgeGraphs pic.twitter.com/NDBKxmrvJR— TheGraphShow (@TheGraphShow) November 2, 2020

"Inrupt ... has launched its first enterprise-ready Solid servers for use by more than a dozen partners, including the NHS, the BBC and NatWest Bank." https://t.co/HcW0uWJjUZ— Aaron Bradley (@aaranged) November 10, 2020

My new article for @TDataScience - Knowledge Graphs at a glance: incorporate human knowledge into intelligent systems, exploiting a semantic graph perspective https://t.co/t57sMJ0Maq #knowledgegraphs #semantics #semanticweb #ontologies #rdf pic.twitter.com/YXD5BpESMj— Giuseppe Futia (@giuseppe_futia) September 28, 2020

COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs https://t.co/2qUreVrazx pic.twitter.com/5K1rMPulth— Aaron Bradley (@aaranged) October 14, 2020

Guess which one might be the geometric space of choice for geometric representation learning with graphs? Keynote by Maximilian Nickel at #cssa2020 @cikm2020 https://t.co/EL3FgSRnpw pic.twitter.com/1PmlzkKZhD— Harald Sack (@lysander07) October 20, 2020

Just published by @WikimediaIL :https://t.co/JadkTTL6cJCould this be the best #SPARQL / @wikidata query tutorial ever? pic.twitter.com/yeyRum76ix— WikiCite (@Wikicite) October 21, 2020

(1/5) Thank you everyone who came to Graph-n-Code livestreams with @SonicDMG and I. 🙏This thread has all the links you need for FREE access to:📌 The code📌 The Images📌 The bookWe are cooking up more livestreams; stay tuned! pic.twitter.com/QFbvdSiAO9— Denise Gosnell, PhD (@DeniseKGosnell) September 8, 2020

“How do you select a graph database? Learn how in @itworldca. Read here: https://t.co/Z529PWdeW6 | #GraphDatabase #GraphDB #GraphAnalytics #DataScience #Developer #Analytics #BigData”

TopQuadrant CEO, Irene Polikoff, provides an overview of the two main graph models along with illustrations of their similarities and differences in graph diagrams in Part I of II in this article series from @TDAN_com https://t.co/CxOrTb3ELL#knowledgegraphs #datagovernance— TopQuadrant (@TopQuadrant) September 25, 2020

“My post on Mount Rainier’s Laplacian (https://t.co/ONdkZJdayX) is a 101 intro to aspects of spectral graph theory. This great talk by @mmbronstein shows how this theory also forms the basis of deep learning on graph-like structures. Read at least 5 papers today because of it. https://t.co/eMsV9...

I'll be discussing "Graph Queries with Gremlin Language Variants" at the Category Theory and Applications group meetup on October 6: https://t.co/MG1HpNEiGd Be prepared to see Gremlin in many different forms! #graphdb pic.twitter.com/OIOsfLvWze— stephen mallette (@spmallette) September 28, 2020

https://t.co/kSTvjW2oBv Such a thoughtful article on #knowledgegraphs! @TDataScience pic.twitter.com/BphubNoYwv— James Le (@le_james94) October 1, 2020

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

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.  All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl

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

VLog, a new rule based reasoner on #KnowledgeGraphs, with #opensource implementation on #Github #iswc_conf #research #sfotwareengineering h/t

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Gremlin Snippets are typically short and fun dissections of some aspect of the Gremlin language. For a full list of all steps in the Gremlin language see the Reference Documentation of Apache TinkerPop™. This snippet is based on Gremlin 3.4.7.Please consider bringing any discussion or questions about this snippet to the Gremlin Users Mailing List.

Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

powered applications with minimum friction.Broad compatibility with existing and future software developm

Ontotext releases GraphDB 9.2 featuring the anticipated support for RDF*/SPARQL* and improvements in the plug-ins for semantic similarity and versioning.

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

grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar featuring prominent researchers and industry practitioners working on different aspects of knowledge graphs. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs.The seminar will be offered over Zoom as per the planned schedule.The seminar is open to public. Remote participants may join the seminar through Zoom. To be

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

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enterprise context, the problem arises of managing information sources that do not use the same technology, do not have the same data representation, or that have not been designed according to the same approach. Thus, in general, gathering information is a hard task, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure are unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. Over the years, several data integration solutions have been proposed: