Building a knowledge graph the semantic data modeling way in 10 steps with the help of Ontotext's knowledge graph technology experts.

In this work, we propose Knowledge Integration Networks (referred as KINet)

At Madgex, we power job board technology for over 200 brands all around the world, and that number is growing all the time. With our scale and reach, we have a lot of data at our fingertips, so we evolved our Data Science team to look at Machine Learning and Knowledge Graph models to enhance the experience for users of our platform. We started by looking at jobs, and asking the basic question... when we talk about a ‘job’ what exactly do we mean?

scale incomplete knowledge graphs

based simulations in which an agent

Apply web scraping bots , computational linguistics, and natural language processing algorithms to build knowledge graphsContinue reading on Towards Data Science »

Wikipedia2Vec: #Python #opensource tool for learning word & entity embeddings from #Wikipedia. Demo: https://t.co/Gv5EBXWbuX #Research paper: https://t.co/GGbQjQolJe #datascience #AI #NLP h/t @aaranged

Facilitating #journalism #automation via #knowledgegraphs. KG nodes corresponding to news articles, arrows show their connections. Generated using @sentilecto_NLU, allows navigating the spacial representation of a set of related texts #AI h/t @aaranged

New from Google Research! REALM: https://t.co/kS2oTyxAAjWe pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open! pic.twitter.com/DYDFX69Td8— Kelvin Guu (@kelvin_guu) February 11, 2020

This is great, and don’t miss the other ontologies at this excellent subdomain name.

Our physical embedding model for #knowledgegraphs achieve quasi-linear scalability. Check out the video by @_CaglarDemir at https://t.co/9n8tkZoWXg #MachineLearning #OpenScience @knowgraphs— Axel Ngonga (@NgongaAxel) February 14, 2020

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At linked data-driven companies you will find it difficult to distinguish between “traditional” data workers and those in other functional areas who, at other companies, are less reliant on data.

This article shows how an RDF Graph CRUD application can be rapidly developed, yet without losing the flexibility that HTML5/JavaScript offers, from which it can be concluded that there is no reason preventing the use of RDF Graphs as the backend for production-capable applications.

Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France

Making decentralized Web app development fun ◆ While the Semantic Web community was fighting its own internal battles, we failed to gain traction with the people who build apps that are actually used: front-end developers. Ironically, Semantic Web enthusiasts have failed to focus on the Web; whereas our technologies are delivering results in specialized back-end systems, the promised intelligent end-user apps are not being created…

What is an application network? And why does it need a graph? MuleSoft's founder and CTO help explain today's announcement at Dreamforce 2018

Discover a new way to visualize and explore your connected data with SemSpect: a unique approach to graph visualization that doesn't depend on using random or best-guess Cypher queries in order to explore your data's meta-graph and that is compatible with Neo4j (including RDF datasets).

Learn how to use Neo4j to integrate a BigQuery public dataset with Stackdriver logs into a graph database, to surface new conclusions from complex data.

An evaluator's task is to connect the dots between program goals and its outcomes. This can be accomplished through surveys, research, and interviews, and is frequently performed post hoc. Research evaluation is hampered by a lack of data that clearly connect a research program with its outcomes and, in particular, by ambiguity about who has participated in the program and what contributions they have made. Manually making these connections is very labor-intensive, and algorithmic matching introduces errors and assumptions that can distort results. In this paper, we discuss the use of ident...

Graphs are a general-purpose #datamodel, as relational was a general-purpose #data model a generation ago. A supply chain is a graph. Knowledge is a graph. Graphs are very applicable in a wide range of use cases @jimwebber @TheRegister #GraphDB #tech [LINK]https://www.theregister.co.uk/2020/02/05/graph_database_neo4j_chief_scientist/ [LINK]https://regmedia.co.uk/2016/04/26/graph_database.jpg

Optimizing #algorithms on large scale labeled property graphs. Comparing optimization approaches, directly querying #graphdatabase. Aim: determine limiting factors of #GraphDBs. Speedup of a factor between 44 & 3839 #knowledgegraph #research #EmergingTech [LINK]https://arxiv.org/abs/2002.03686 [LINK]https://i.imgur.com/75Wxc5b.png

Mbabel #bot automatically generates article drafts based on information stored in #Wikidata. It puts generated content on a “user test page on Wikipedia”; users can expand it. Quality is influenced by heavy reliance on Wikidata #AI #opendata #semantics [LINK]https://www.vice.com/en_us/article/4agamm/the-worlds-second-largest-wikipedia-is-written-almost-entirely-by-one-bot [LINK]http://video-images.vice.com/articles/5e3c6732c62ac8009d1e9f27/lede/1581016882789-1_31_2020_THE_SECOND_LARGEST_WIKIPEDIA_SECTION_WRITTEN_BY_ONE_BOT_CV_ALT.jpeg

Building on the newbie’s guide to querying #Wikidata, @markhneedham learns all about the CONSTRUCT clause in SPARQL #softwareengineering #datascience #tutorial #opendata #linkeddata #knowledgegraph #GraphDB #data #tech

#SchemaOrg created FAQ Schema. #Google introduced filtering parameters that come w Search implementation. These act as commandments for Google’s algorithm which should be abided by for when markup is used in search results #SEO #semantics h/t @cyberandy

An Introduction to #Ontology Engineering: Version 1.5 of the textbook now available online #knowledgegraph #datamodel #tutorial #data #tech #datascience h/t @aarranged

#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