GraphNews

Facebook AI has created and is now open-sourcing PyTorch-BigGraph (PBG), a tool that makes it much faster and easier to produce graph embeddings for extremely large graphs.

Keith Hare has joined the Neo4j team to spearhead language standards efforts. My name is Philip Rathle, Neo4j’s VP of Product Management, and I got a chance to sit down with Keith to discuss his thoughts on databases, standards and the future of the industry. Philip Rathle: Welcome to the team, Keith. As the Convenor for the ISO committees for SQL and now for the GQL Project you’re a busy person. What have you been up to? Keith Hare: While I am spending a significant amount of time working with the Neo4j LANGSTAR (Languages, Standards, and Research) team, I am continuing in my roles as the Convenor of the ISO SQL and GQL standards committee, and as the President of JCC Consulting, Inc. Philip: What’s been your involvement in ISO, and what have you been working on? Keith: I got started in the US SQL standards process a bit over 30 years ago, partially because it was interesting and partially as a way of keeping track of what was happening in the database industry. In

scale incomplete knowledge graphs

KAUST shows a universal query engine for Big Data that works across computing platforms could accelerate analytics research

Querying @DBPedia #LinkedData From Jupyter Notebooks – Music Genres Related to Heavy Metal and Music Venues in England. #data #tech #dataviz #knowledgegraph #datascience @psychemedia h/t @aaranged

Part 1.

Querying the first RDF Data Cube based on an existing PC-Axis/PX data cube published by @swissstatistics. Thanks to @bergi_bergos for the great converter, will be made public later pic.twitter.com/4SJ3jpKg7e— Adrian Gschwend (@linkedktk) July 10, 2019

Last month in Populating a Schema.org dataset from Wikidata I talked about pulling data out of Wikidata and using it to create Schema.org triples, and I hinted about the possibility of updating Wikidata data directly. The SPARQL fun of this is to then perform queries against Wikidata and to see your data edits reflected within a few minutes. I was pleasantly surprised at how quickly edits showed up in query results, so I thought I would demo it with a little video.

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

Natural language question answering over #knowledgegraphs enables accurate answers in intuitive manner. But bridging the gap between unstructured questions & knowledge graphs is a challenge. Novel notion: structural query pattern #research #AI #NLP

In this paper, an overview of the state of the art on knowledge graph

.@NvidiaAI RAPIDS cuGraph #opensource library is on a mission to provide multi-GPU graph #analytics for billion/trillion scale graphs. Experimental results on release of a single-node multi-GPU version of PageRank: on average 80x faster than #ApacheSpark

The Data Scientist has a collection of techniques within their proverbial toolbox. Data engineering, statistical analysis, and machine…

The vision of RAPIDS cuGraph is to make graph analysis ubiquitous to the point that users just think in terms of analysis and not…

RavenDB, the open-source transactional NoSQL document database vendor, has added data replication and other features to the latest release along with the

Introducing Configurable Analyzers & ArangoSearch Upgrades: This RC post is dedicated to the four new features of ArangoSearch.

existing queries remain valid) even in the face of complex additio

RDFox is a high performance knowledge graph and semantic reasoning engine developed by Oxford Semantic Technologies. This short article will help you understand the key concepts behind RDFox and when to use them in your applications.Knowledge GraphsA knowledge graph is composed of a graph database to store the data and a reasoning layer to interpret and manipulate the data.Relational databases store data in structured records whereas graph databases store data points as nodes which are connected with edges if they share some form of relationship.Data stored in a graph can be accessed with a query which will “hop” along the edges to find the requested nodes.ReasoningReasoning is the process of materialising rules which apply to the data. Materialising a rule means adding new nodes or edges to the graph when it is satisfied. These new nodes and edges match the rule’s “pattern”.A rule can be as simple as an “If… then…” statement.For example: “If a city is located

Learn more about our announcement of Neo4j Enterprise Edition 4.0 Milestone Release 2, which is now ready for testing by the Neo4j community.

(Submitted on 18 Mar 2020) Abstract: In the active research area of employing embedding models for knowledge graph

A project on optical graph recognition with OpenCV and yFiles WPF.

A thorough introduction to knowledge graphs

sided issue when it comes to sourcing a pool of candidates. While they need to provide their hiring managers with a solid list of professionals, these professionals also need to be relevant, qualified and interested in the opportunity being offered – otherwise, they’re likely to drop out during the interviewing process because the job isn’t right for them, or they won’t respond to begin with. This sourcing challenge drastically grows when sudden growth or reorganization increases the number of roles that recruiters need to fill. And ev

.@RedisLabs has implemented openCypher query language over a specialized #data structure. Coverage currently ~60% growing -> 80% soon. Redis Labs is participating in effort to define new standard for graph query languages #graphDB #opensource #innovation

An #opensource #software framework to build Games with Purpose for #linkeddata refinement: Web applications to crowdsource partial ground truth, by motivating user participation through fun incentives #knowledgegraph #datascience #data #opendata #research

eBay researchers recently published a paper about a method for KG relation embedding using dihedral group. Experimental results on benchmark KGs show that the model outperforms existing bilinear form models and even deep learning methods.

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