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News broke early in October 2016 that the company behind RethinkDB was shutting down. RethinkDB was developed as a distributed document-oriented database to store JSON documents with dynamic schemas, and was designed for pushing updated query results to applications in realtime.

“We worked very hard to make RethinkDB successful, but in spite of all our efforts we were ultimately unable to build a sustainable business,” said RethinkDB’s founder, Slava Akhmechet, on the company blog post. Analysis of what went wrong is ongoing across the Internet. Some blame the failure on superior marketing by RethinkDB’s competitors, while others think that the core RethinkDB product was the root cause. RethinkDB will continue as an open source project, and we hope to find out more about how its community will operate in coming months.

Also early in October 2016, we came across a nice summary of the Financial Industry Business Ontology (FIBO), which discusses how banks are using FIBO to standardize content and meaning. FIBO semantically defines core financial industry concepts and relationships, serves as an expressive model for regulatory compliance, and supports financial and systemic risk intelligence. Companies such as Deutsche Bank and Nordea Bank are using it to enable rapid deployment of products and services and provide valuable analysis.

The Grakn Labs engineering team have been reading, sharing and discussing a paper recently submitted to arXiv.org this week. In “Foundations of Modern Graph Query Languages”, the authors review and discuss the features underlying modern graph query languages. Two popular graph data models — edge-labelled graphs and property graphs — are discussed, followed by the two most basic graph querying functionalities: graph patterns and navigational expressions. The authors examine a variety of semantics under which queries using graph patterns and navigational expressions can be evaluated, offering examples using SPARQL, Cypher and Gremlin. We are planning to reach out to the authors soon to discuss Graql with them.

Our founder and CEO, Haikal Pribadi, was at O’Reilly’s AI conference in New York in late September. At the same conference, Scientific American spoke with Oren Etzioni, CEO of the Allen Institute for AI, which has the mission of contributing to humanity through high-impact AI research and engineering in the field of artificial intelligence. An edited transcript of the interview published in Scientific American this month makes fascinating reading. Etzioni makes the case for addressing research in areas such as reasoning, and argues that despite recent remarkable advances in deep learning, we don’t have AI that can do something a 10-year-old can do, which is pick up a book, read a chapter and answer questions about what they read.

Haikal has been busy building up his air miles recently, and also attended the 15th International Semantic Web Conference, in Kobe, Japan. While there, he met up with Michele Pasin from Springer Nature, who recently published a comprehensive slide deck about what the scientific publishing behemoth is doing with Linked Data. In his presentation, Michele previews Scigraph, a Linked Data platform that enables searching the emerging web of linked science data for items, documents, people, places, relations that are of importance to the science and scholarly domain. Scigraph launches to the public later this year, and we’re looking forward to putting it through its paces. Michele will be presenting at the Open Data Institute summit this week (1st November 2016) and we hope to hear more about Scigraph there.

What we’ve been writing this month

This month, we published a range of blog posts:

What we’ve been reading this month

Aside from the recent articles above, we have also been catching up on these: