ClaimsKG, a #knowledgegraph of fact-checked claims, which facilitates structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. Harvests data from popular fact-checking sites, annotates w @DBpedia

With our semantic chatbot, semantize your data without even having to understand what data semantization is!

This is what the resultant 100 retail outlet isochrone map looks like, built using Spatia and @rapidsai #cuGraph SSSP using @OrdnanceSurvey Open Roads as road graph + drive times estimated from @transportgovuk road stats #opendata cc @puntofisso pic.twitter.com/rGhDinkaVX— John Murray (@MurrayData) May 28, 2019

.@jQAssistant is a #QA tool which allows definition & validation of project specific rules on a structural level. Built upon #Neo4j #graphdatabase, can be plugged into build process. Now w/ #PlantUML class diagrams #dataviz #softwareengineering

#W3CGraphWorkshop @b2ebs’s Keynote- Neptune seems to be favorite amazon product launch of 2018. People love graphs- “Graph let’s me integrate data like crazy”- View market as customers who could benefit from graphs- Devs from RDB find PG natural. Info arch find RDF natural pic.twitter.com/6tJDy0FKDz— Juan Sequeda (@juansequeda) March 4, 2019

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Visualize Kafka Streams with Neo4j by taking any data, turning it into a graph, leveraging graph processing, and piping the results back to Apache Kafka, adding visualizations to your event streaming applications.

KBpedia combines 6 knowledge bases (Wikipedia, Wikidata, schema.org, DBpedia, GeoNames, OpenCyc), has mappings to more vocabularies. Can be used for #machinelearning, #data interoperability, knowledge-based #AI #Newrelease #knowledgegraph #opendata [LINK]http://www.mkbergman.com/2284/kbpedia-continues-quality-improvements/[/LINK] [IMAGE]http://www.mkbergman.com/wp-content/themes/ai3v2/images/2017Posts/kbpedia-logo-244.png[/IMAGE]

The baseline KBpedia version 2.00, a computable knowledge structure that integrates seven leading public knowledge bases, has now been released as open source.

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…

#Knowledgegraphs are important resources for many #artificialintelligence tasks but often suffer from incompleteness. #research proposes to use pre-trained language models for knowledge graph completion. Novel framework named KG-BERT

The quality assurance of the knowledge graph is a prerequisite for various

.@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

Prototyping a simple knowledge graph application with JSONs, MongoDB and automatically generated GraphQL API.Continue reading on The Startup »

Prototyping a simple knowledge graph application with JSONs, MongoDB and automatically generated GraphQL APIThis post is a result of joint work with Anna Konieczna and Artur Haczek.Source: Noble ConnectionsSimple knowledge graph applications can be easily built using JSON data managed entirely via a GraphQL layer. We describe a quick recipe for prototyping one such demo, Noble…

Knowledge graph applications can be technically challenging and time-consuming to build, but enterprises are starting to find that the work can lead to positive returns.

There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granulari…

text generation take as input a few RDF triples or

1. Knowledge Graph

Intelligent systems designed using machine learning algorithms require a

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(Submitted on 4 Mar 2020) Abstract: In this paper we provide a comprehensive introduction to knowledge graphs,

#KnowledgeGraphs can be applied to a wide variety of #NLP tasks: question answering, dialogue systems etc. @michael_galkin summarizes EMNLP 2019 #research: Augmented Language Models, Dialogue Systems & Conversational #AI, KGs from Text, KG Embeddings

Part 2 of @michael_galkin review of #knowledgegraph related papers from EMNLP 2019: Question Answering over KG, NLG from KGs, Commonsense reasoning with KGs, and some old school Named Entity/Relation Recognition & Linking #research #AI #EmergingTech #data

This is the first in a short series introducing Knowledge Graphs. It covers just the basics, showing how to write, store, query and work with graph data using RDF (short for Resource Description Fo…

This installment leaves the CLI behind to show how we consume a knowledge graph within our programmatic environments. The framework I use to work with RDF is dotNetRdf.

"We might conclude that we don’t need to represent knowledge in graphs explicitly anymore, but one of the hottest fields right now involves using deep learning to learn embedding representations of the base knowledge graph." h/t @aaranged @gm_spacagna [LINK]http://datasciencevademecum.com/2019/12/19/knowledge-graphs-and-causality/[\LINK] [IMAGE]https://datasciencevademecum.com/wp-content/uploads/2019/12/dark_1x01_-_string_wall_full-825x510.jpg[\IMAGE]

My friend Arun Batchu and I have had several spirited discussions about a recent article in the July 2019 Scientific American about…

at least since Vannevar Bush’s 1945 seminal piece when he discussed why people of science should then turn to the massive task of making more accessible our bewildering store of knowledge to implement machine processes (http://bit.ly/45VBush). Following the importance of conceptual models in data management and knowledge representation in Artificial Intelligence (AI) in the 1980s, the 1990s saw the emergence of the concept of ontology i