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The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog posts, we’ll take a look at ongoing ontologies projects and developments.

#Knowledgegraphs are a natural fit for knowledge management: they model domains to retain more context & meaning even as information is parsed and abstracted for digital representation. Information is modeled in a way that is more intuitive & useful

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

Causal reasoning is a necessary ingredient to human-level artificial intelligence. We're not there, yet.

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Alan Morrison's answer: Graph databases are often used to store knowledge graph data and the accompanying description, predicate and rule-based logic. Knowledge graph: A knowledge graph is a knowledge base that’s made machine readable with the help of logically consistent, linked graphs that tog...

#knowledgegraph-based #research to design a 20 questions game on Bollywood movies. Uses probabilistic learning model for template-based question generation & answer prediction. Dataset of interrelated entities represented as weighted knowledge graph #AI

An interactive map of all the world's disputed areas from #wikidata. Built in 10 minutes by @planemad, using SPARQL #dataviz #knowledgegraph #opendata #graphDB #data #tech #EmergingTech #datascience Map #visualization on left-side menu h/t @LearningSPARQL [LINK]https://t.co/a4Yngspwds[/LINK] [IMAGE]https://pbs.twimg.com/tweet_video_thumb/EKBb_NGW4AIYhEH.jpg[/IMAGE]

Heritage institutions are places in which works of art, historical records, and other objects of cultural or scientific interest are sheltered and made accessible to the public. The equivalent of that in the digital world, is already taking shape, through digitization and sharing of digital-born or

An Introduction to Graph Theory by @mpvenables Before diving in, we need to understand #data structure & networks in #machinelearning. Networks are useful for #apps, from driving directions to social networks #datascience #tutorial #analytics #AI [LINK]https://towardsdatascience.com/an-introduction-to-graph-theory-24b41746fabe[/LINK] [IMAGE]https://miro.medium.com/max/480/1*rnZ3FbSvWMVvcRP78fXeCg.png[/IMAGE]

Neural Networks aimed at effectively handling graph data.Photo by Alina Grubnyak on UnsplashGraph structured data is common across various domains, examples such as molecules, { social, citation, road } networks, are just a few of the vast array of data which can be represented with a graphs. With the advancements of machine learning we witness the potential for applying intelligent algorithms on the data which is available. Graph Neural Network is the branch of Machine Learning which concerns on building neural networks for graph data in the most effective manner.Notwithstanding the progress made with ML in the computer vision domain with convolutional networks, Graph Neural Networks (GNNs) face a more challenging problem, they deal with the awkward nature of graphs. Differently from images and text, graphs do not have a well defined structure. A graph’s node might have no connections or many, of which could be directed or undirected. Graphs in a dataset may have a variable

Etsy has an uncontrolled inventory; unlike many marketplaces, we offer an unlimited array of one-of-a-kind items, rather than a defined set of uniform goods. Etsy sellers are free to list any polic…

This guide will help you understand JSON-LD and structured data markup. Go beyond the online generators and prepare your web pages for the future of search!

Analyzing US Lobbying Data in #Neo4J #graphdatabase

https://t.co/7T0EOs6yG7 - Made this tiny tool to discover related subreddits.The graph is created based on jaccard similarity between two subreddits. Jaccard similarity is constructed from set of shared users.Source code https://t.co/J9r1jl1JjR pic.twitter.com/4hcg7mI4sg— Andrei Kashcha (@anvaka) January 10, 2019

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

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

Michael K. Bergman announces his new book, A Knowledge Representation Practionary: Guidance from Charles Sanders Peirce. The book applies this guidance to the question of how to best represent human knowledge to computers. The book's practical guidelines should be of interest to any enterprise KM ma

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

Personally, I am always most excited by technology when it can have a positive impact on society and the lives of ordinary people. Curing…

TerminusDB is an open source model driven graph database for knowledge graph representation designed specifically for the web-age.

We are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog. Need to know more […]

Graph Convolutional Networks (GCNs) generalize the idea of deep convolutional

Data Lakes will evolve into knowledge graphs. This article is aimed at explaining the meaning of Knowledge graphs based on semantic web and why it will eventually secure its rightful place in organizing enterprise knowledge.

Semantic standards have been with us since the birth of the web, when it became clear to inventors of the web like Tim Berners-Lee and many others that meaning could be systematically captured, organized, and exploited to do valuable tasks. Since then, the idea of applying formal semantics to enterprise data has come and faded […]

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