Cognonto, Companies

After More Than 10 years In Business

I delayed this blog post for far too long.

Almost exactly one year ago I had to take a heartbreaking decision for myself and for my long term business partner and friend Mike Bergman. I had to stop working on our business projects, Structured Dynamics and Cognonto such that can restart bringing incoming for my family.

For more than ten years Mike and I had the good fortune to be able to spend all our time working together on all kind of interesting projects and doing really mind challenging research into the field of the Semantic Web, and more recently, its applications to the field of the Artificial Intelligence.

Our last business project was a company called Cognonto, and more precisely its huge knowledge graph called KBpedia. This project, just like anything we did prior to it, was everything about research and development, trying to push ideas, concepts and principles to the market. We put all our thoughts, energy, time and [monetary] resources in pursuing our goals.

The problem is that we have never been able to monetize this new endeavor unlike the other projects we created in the previous decade.

After more than a year and a new baby boy, after spending all the resources I had available for the project I had to take a decision for me and my growing family.  I had to seek for a new job.

That is why I have been silent for so long. I had to reorganize my time after being self employed for about fifteen years and dealing with two young boys.

However, I had the good fortune to be contacted by Curbside about at the same time that I took that decision to seek for a new job. I now lead  the creation, design and development of their internal machine learning environment.

Good fortune being what it is, I really do enjoy my new work, the importance of it, the design and research I am putting in it and the wonderful team that I am part of.

We dissolved the Structure Dynamics company a few months ago. However not everything is done. Mike is working on a really personal and important project related to KBpedia for which we have important announcements to do in the coming months.

Lastly, I would like to take the time to say thank you to Mike Bergman for all the time we spend together working on those wonderful semantic web and knowledge base representation projects together. All those daily chats and calls we had discussing and arguing  to advance our ideas. And for all those things you taught me about research methodology, business and life. I owe you much my friend!



Cognonto, Artificial Intelligence, Semantic Web

A Machine Learning Workflow

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow.

The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc.

One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced. They surveyed about 80 data scientists to probe them to find out “where they feel their profession is going, [and] what their day-to-day job is like” To the question: “What data scientists spend the most time doing”, they answered:

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Cognonto, Artificial Intelligence, Semantic Web

KBpedia Knowledge Graph 1.40: Extended Using Machine Learning

I am proud to announce the immediate release of the KBpedia Knowledge Graph version 1.40. This new version of the knowledge graph includes 53,739 concepts which is 14,687 more than with the previous version. It also includes 251,848 new alternative labels for 20,538 previously existing concepts in the version 1.20, and 542 new definitions.

This new version of KBpedia will have an impact on multiple different knowledge graph related tasks such as concepts and entities tagging and most of the existing Cognonto use cases. I will be discussing these updates and their effects on the use cases in a forthcoming series of blog posts.

But the key topic of this current blog post is this: How have we been able to increase the coverage of the KBpedia Knowledge Graph by 37.6% while keeping it consistent (that is, there are no contradictory facts) and satisfiable (that is, checks to see if the candidate addition violates any existing class disjointness assertions), all within roughly a single month of FTE effort?

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Literate Programming, Programming, Emacs, Clojure

Literate [Clojure] Programming: Publishing Documentation In Multiple Formats

This blog post is the sixth, and the last, of a series of blog posts about Literate [Clojure] Programming in Org-mode where I explain how I develop my [Clojure] applications using literate programming concepts and principles.

This last post introduce a tool that leverage the side effect of having all the codes neatly discussed: it gives the possibility to automatically generate all different kinds of project documentation with a single key-binding. The documentation that we will generate are:

  1. Human readable HTML general project documentation
  2. Programming API documentation
  3. Book style complete project documentation in PDF

This series of blog posts about literate [Clojure] programming in Org-mode is composed of the following articles:

  1. Configuring Emacs for Org-mode
  2. Project folder structure
  3. Anatomy of a Org-mode file
  4. Tangling all project files
  5. Publishing documentation in multiple formats (this post)
  6. Unit Testing

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Cognonto, Artificial Intelligence

Measuring the Influence of Expanded Knowledge Graphs on Machine Learning

Mike Bergman and I will release a new version 1.40 of the KBpedia Knowledge Graph in the coming month. This new version of the knowledge graph will include roughly 15,000 new concepts and 150,000 new alternative labels and 5,000 new definitions for existing KBpedia reference concepts. This new release will substantially increase the size of the current KBpedia Knowledge Graph.

This extension is based on a new methodology that we began to cover in the Extending KBpedia With Wikipedia Categories Cognonto use case. The extension uses graph embeddings for each KBpedia reference concept and its linkage to the Wikipedia category structure to pre-select the Wikipedia categories that are most likely to be good candidates to fill [current gaps] in the KBpedia graph structure. The new reference concept candidates scored through this automated process were then reviewed for likely selection. These selections were then analyzed by re-generating the KBpedia Knowledge Graph, which includes routines for identifying, reporting and fixing consistency and coherency issues using the KBpedia Generator. Problematic assignments are either dropped or fixed. These steps reflect the general process Cognonto follows in mapping and incorporating new schema and ontologies.

In the coming month or two, I will write a series of blog posts that will analyze the impact of different important versions of KBpedia on different machine learning models that we have previously created for the Cognonto use cases. All of the current use cases have been created using version 1.20 of KBpedia. We are about to finalize the creation of an intermediate version 1.30 (for internal analysis only). We are separately identifying thousands of reference concepts that will be temporarily removed, since they are more properly characterized as ‘aspects‘ and not true sub-classes. This removal will allow us to then define a third variant for machine learning comparisons. Some of these ‘aspects’ will be re-introduced into the graph where proper parent-child relationships can be established. The next public release of KBpedia, tentatively identified as The version 1.40, will include all of these updates.

Each of these three variants (versions 1.20, 1.30 and 1.40) will enable us to analyze and report on the influence that different version of the KBpedia knowledge graph can have on different machine learning tasks. The following tasks will be covered:

  1. Creating graph embeddings to disambiguate tagged concepts
  2. Creating domain specific training corpuses to train word embeddings
  3. Creating domain specific training sets to classify text, and
  4. Checking relatedness between Knowledge Graph concepts and Wikipedia categories based on their graph embeddings.

Our goal at Cognonto is to make available the power of knowledge-based artificial intelligence (KBAI) to any organization. Whether if it is for help populating search or tagging indexes, for performing semantic query expansion, or for help with a broad series of machine learning tasks, knowledge graphs plus KBAI provide a nearly automated way for doing so. Our research and expertise is geared toward creating, linking, extending, and leveraging knowledge graphs and knowledge bases to empower new and existing systems. We will continue to report in specific detail how and with what impact knowledge graphs and knowledge bases lead to better machine learning results.