Step-by-step tutorial: #RDF-ize tabular data, publish it and build an Angular front-end. Well done @ElvinDechesne! Clear and comprehensive explanations how to use OntoRefine for ETL in Part 1! #SPARQL #KnowledgeGraph https://t.co/ZF3XDw5wvV— Atanas Kiryakov (@kiryakov_ak) March 5, 2020

meters, human capital is almost always the largest single component. To this end, if you want to use smart building technologies to save costs or increase margins, the main use cas

AI has a critical role to play in fighting COVID-19 - @SemanticScholar has taken the lead in partnership with several groups to produce the COVID-19 Open Research Dataset (CORD-19), a free resource of 29K+ scholarly articles for the #AI research community: https://t.co/Pk94dvW6Kp— Allen Institute for AI (@allen_ai) March 16, 2020

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in la...

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

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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…

/PRNewswire/ -- Stardog, the leading Enterprise Knowledge Graph platform, today announced the release of the first data model template compatible with the...

user experience was also far from great and administrative features like access control were totally missing. At the same time, the mainstream web application architecture was relying on techn

file contained a list of properties separated by a unique character. Each subsequent row contained the val

"What is Trending on Wikipedia? Capturing Trends and Language Biases Across #Wikipedia Editions" people share interest for entertainment, and differences appear in topics related to local events or about cultural particularities.

an ontology representating the knowledge of the aviation sector

The interest in Freebase, Wikidata, and DBpedia since Wikidata's launch and geographically over the world (based on Google Trends).Fascinating to see the local distributions, and how slow the decline of Freebase is. pic.twitter.com/EstBNjCT2g— Denny Vrandečić (@vrandezo) February 27, 2020

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

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

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

And Find out who are the most Influencers in Game of ThronesContinue reading on Towards Data Science »

Automatic radiology report generation has been an attracting research problem

Recent successes in training word embeddings for NLP tasks have encouraged a

based technologies will soon shift fr

Check out my other blogs here!Microsoft gives a wonderful tutorial about Knowledge Graph in KDD 2018. If you are a machine learning engineer or an NLP engineer, I highly recommend reading this tutorial. It talks about what is knowledge graph (KG), the KG construction challenges for a large scale, and the…

Ahren Lehnert looks at lifecycles and versioning. The basic governance practices are common when managing taxonomies and ontologies.

Google, Microsoft, Facebook have their own #knowledgegraphs. But there's a larger body of publicly available KGs, such as @DBpedia or #Wikidata @heikopaulheim @svenhertling @n_heist @dwsunima provide overview & comparison #opendata #EmergingTech #research

Google is testing the ability for people to customize their own Knowledge Graph cards that are displayed in search results.The post Google Lets People Customize Their Profile Cards For Search Results via @MattGSouthern appeared first on Search Engine Journal.

Relation extraction from simple questions aims to capture the relation of a

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intelligence projects in military and security forces, Quid and RecordedFuture enable competitive analytics, Bottlenose and others enable online reputation analytics, Microsoft Graph enables new kinds of productivity apps for the

Edwin Toonen Edwin is a strategic content specialist. Before joining Yoast, he spent years honing his skill at The Netherlands’ leading web design magazine. Search engines love entities. Entities can be people, places, things, concepts, or ideas and they will often appear in the Knowledge Graph. Lots of search terms can be an entity, but specific search terms can also have different meanings and thus, be different entities. Take [Mars] for example; are you talking about the planet entity or the candy bar entity? The context you give these entities in your content determines how search engines see and file your content. Find out how to link entities to your content using Yoast SEO. Let’s talk semantics Semantics is the search for meaning in words. In theory, you could write an article about Mars without ever mentioning it directly. People would understand it if you provide enough context in the form of commonly used terms and phrases. To illustrate this, we’ll take