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Alan Morrison's answer to What areas of machine learning would you encourage startups to spend time innovating on? In others words, what tools are engineers missing (ie, better data labeling, etc.) to make their machine learning experience more streamline
Alan Morrison's answer to What areas of machine learning would you encourage startups to spend time innovating on? In others words, what tools are engineers missing (ie, better data labeling, etc.) to make their machine learning experience more streamline
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·quora.com·
Alan Morrison's answer to What areas of machine learning would you encourage startups to spend time innovating on? In others words, what tools are engineers missing (ie, better data labeling, etc.) to make their machine learning experience more streamline
Alan Morrison's answer to What is the difference between a knowledge graph and a graph database? - Quora
Alan Morrison's answer to What is the difference between a knowledge graph and a graph database? - Quora
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...
·quora.com·
Alan Morrison's answer to What is the difference between a knowledge graph and a graph database? - Quora
Alexander Lex on Twitter
Alexander Lex on Twitter
What are your options for visualizing a network with many attributes? We review the alternatives and introduce a typology in a new state of the art report. #eurovis #datavis https://t.co/96dhomPTRt w. @carolinanobre84 @miriah_meyer @marc_streit 1/8 pic.twitter.com/PCAk5ptBp3— Alexander Lex (@alexander_lex) June 4, 2019
·twitter.com·
Alexander Lex on Twitter
Amazon Neptune now supports TinkerPop 3.4 features
Amazon Neptune now supports TinkerPop 3.4 features
#Amazon Neptune #graphDB now supports @apachetinkerpop 3.4.1. @gfxman shows examples of new features in the Gremlin query/traversal language #softwaredevelopment #analytics #database #data #tech #tutorial #opensource #AWS [LINK]https://muawia.com/amazon-neptune-now-supports-tinkerpop-3-4-features/[/LINK] [IMAGE]https://s.put.re/NzhUFENd.png[/IMAGE]
·muawia.com·
Amazon Neptune now supports TinkerPop 3.4 features
Amazon Neptune now supports TinkerPop 3.4 features | AWS Database Blog
Amazon Neptune now supports TinkerPop 3.4 features | AWS Database Blog
Amazon Neptune now supports the Apache TinkerPop 3.4.1 release. In this post, you will find examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 […]
·aws.amazon.com·
Amazon Neptune now supports TinkerPop 3.4 features | AWS Database Blog
Amazon Neptune offers full-text search integration with Elasticsearch clusters
Amazon Neptune offers full-text search integration with Elasticsearch clusters
#graphDB #Amazon Neptune now supports full-text search integration with Elasticsearch clusters. Using Elasticsearch users can run full-text search query types such as match query, intervals query, query strings using extensions to Gremlin & SPARQL #AWS
·aws.amazon.com·
Amazon Neptune offers full-text search integration with Elasticsearch clusters
Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
The latest Amazon Neptune release brings together a host of capabilities that enhance developer productivity with graphs. This post summarizes the key features we have rolled out and pointers for more details. Getting started This new engine release will not be automatically applied to your existing cluster. You can choose to upgrade an existing cluster […]
·aws.amazon.com·
Amazon Neptune releases Streams, SPARQL federated query for graphs and more | AWS Database Blog
Amazon Neptune Workbench provides in-console experience to query your graph
Amazon Neptune Workbench provides in-console experience to query your graph
#Amazon Neptune #graphDB now offers a workbench, an in-console experience to query your graph. It lets users query Neptune w #Jupyter #notebooks using Gremlin or SPARQL #datascience #cloud #knowledgegreaph #AI #database AWSreinvent2019
·aws.amazon.com·
Amazon Neptune Workbench provides in-console experience to query your graph
An approach for semantic integration of heterogeneous data sources
An approach for semantic integration of heterogeneous data sources
enterprise context, the problem arises of managing information sources that do not use the same technology, do not have the same data representation, or that have not been designed according to the same approach. Thus, in general, gathering information is a hard task, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure are unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. Over the years, several data integration solutions have been proposed:
·peerj.com·
An approach for semantic integration of heterogeneous data sources
An interactive map of all the world's disputed areas from #wikidata
An interactive map of all the world's disputed areas from #wikidata
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]
·twitter.com·
An interactive map of all the world's disputed areas from #wikidata
An international Knowledge Base for all Heritage Institutions (Part 2*) – SocietyByte
An international Knowledge Base for all Heritage Institutions (Part 2*) – SocietyByte
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
·societybyte.swiss·
An international Knowledge Base for all Heritage Institutions (Part 2*) – SocietyByte
An Introduction to Graph Theory
An Introduction to Graph Theory
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]
·towardsdatascience.com·
An Introduction to Graph Theory
An introduction to Graph Neural Networks
An introduction to Graph Neural Networks
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
·towardsdatascience.com·
An introduction to Graph Neural Networks
An introduction to ontology engineering book
An introduction to ontology engineering book
This first general textbook An introduction to ontology engineering has as main aim to provide the reader with a comprehensive introductory overview of...
·linkedin.com·
An introduction to ontology engineering book