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

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What does broccoli soup contain and Edamam’s live food knowledge graph of semantically interlinked data from various sources

Lee — to pioneer ways of giving people control over their own personal data. Solid maintains that people should be free to share “whatever they want with whomever they want.” The key to this mechanism of control is a personal data “Pod”

life connections will make the technology particularly useful for investment management in 2020 and the years to com

This post commemorates the first anniversary of the series where we examine advancements in NLP and Graph ML powered by knowledge graphs! 🎂 1️⃣

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

In 2012 I started my .NET implementation of R2RML and RDB to RDF Direct mapping which I called r2rml4net. It never reached the maturity it should have but now, 8 years later, I have little choice but to polish it and use it for converting my database to triples. A task I had originally intended but never really completed. Why is it significant? Because all those years later the environment around R2RML as a standard is almost as broken, incomplete and sad as it was when I started. Let’s explore that as an example of what is wrong with RDF in general. It has been brought to my attention that Morph is in fact actiavely maintained. I’ve updated it’s details and evaluation. Intro. What is R2RML? R2RML and Direct Mapping are two complementary W3C recommendation (specifications) which define language and algorithm respectively which are used to transform relation databases into RDF graphs. The first is a full blown, but not overly complicated RDF vocabulary which lets designers

Welcome to this week’s #GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight.

learning, understanding what an entity is will help you connect content and search engines better understand your website.

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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

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consuming because experts are required to pick out biomedical inte

What is more important than data consolidation? Making sense of that data and communicating it to others. With data volumes growing and time for analysis decreasing, this doesn't seem like an easy task. Data visualisation, however, may be the most reliable way to get on with this daunting process and make it seamless. Data visualisation and more specifically graph visualisation helps you organize all your data and make it readable and insightful.

quality knowledge and expertise. Modern organizations expose their knowledge with conversational interfaces such as bots and expert systems so customers, partners, and employees will have immediate access to the knowledge that drives success. We, data scientists and engineers, are responsible to make that happens. We need to answer a simple question: How do you represent business knowledge so it is easy and simple to consume? There are many approaches and possible strategies for exposing knowledge. in this article I want to dig into the good old Frequently Asked Questions system and discuss how to implement it with the latest AI technologies.In the prehistoric era, websites used to have this one FAQ page with a long and tedious list of useless questions. Only real optimists would ever search this list to find a possible remedy for an issue they face. Those years are gone. Today modern web sites have an integrated bot that user

#data stored in graphs mimic the way humans understand information. @ontotext built a Star Wars #Knowledgegraph using RDF. But…what do #software developers want? Many APIs are built using #GraphQL #JSON. “A query language for your API” #GraphDB #database

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]

A new formal framework based on category theory which provides formal definitions of the main basic features of RDF and SPARQL. Proposal to define notions of RDF graphs as well as SPARQL basic graph patterns as objects of some nested categories #research http://arxiv.org/abs/1910.07519

#knowledgegraphs differ from relational DBs primarily in how information gets stored. KGs consist of index holding at least three values: subject, predicate, object (triple). In typical triple stores, the index is the database. This has several advantages

A relational graph #neuralnetwork w bi-directional attention mechanism & hierarchical representation learning for open-domain question answering. Can learn contextual representation by jointly learning & updating query, #knowledgegraph, document #research

#ISWC2019 Reproducibility Initiative aims to enable easy sharing of code &experimentation setups, make more code & data available, highlight impact & increase credibility of #SemanticWeb #research, facilitate dissemination @michaelcochez @FrankVanHarmele

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

Literature review is complex, requires expert analysis of unstructured #data. #Automation of process presents opportunity for high throughput knowledge extraction & meta analysis. Gextext extracts #knowledgegraph of relationships from text #research #NLP

#Semantic interoperability is the key enabler for #digitaltransformation. But how do we achieve it? By having content understandable & available in a machine processable form. #Ontologies will play a key role in providing infrastructure to support this

#NLP is now good enough that we can measure how well systems reads text in terms of extracted knowledge: Knowledge Base Population @diffbot released KnowledgeNet reproducible #dataset to measure this, w #opensource state-of-the-art baseline #research #AI

Analyzing US Lobbying Data in #Neo4J #graphdatabase

SHACL2SPARQL is a prototype Java implementation of the algorithm described in #iswc19 best #research paper award winner Validating SHACL constraints over a SPARQL endpoint (Corman, FLorenzano, Reutter & Savkovic). #opensource #github #softwaredevelopment