Yann Le Franc

The emerging field of neurosymbolic AI — combining LLMs and knowledge graphs in hybrid AI architectures — is new to a lot of people.

For Yann Le Franc, it's the story of his career — from his academic days as a computational neuroscientist to his current work creating enterprise systems that ground LLMs in ontology-backed knowledge graphs.

Over his extensive research and consulting career, Yann has honed his ontology design process, which he's now sharing in a platform that guides others in applying those time-tested ontology practices.

We talked about:

his work on the European Open Science Cloud, the LUMEN and GRAPHIA projects, and his KG and AI consultancy

his academic evolution from cellular biology to neuroscience and then to computational neuroscience, neural network research, and neuroinformatics — and how that led to his discovery of the power of ontologies

his current work to connect LLMs and ontologies

a presentation on ontology-backed AI that he delivered at the Cultive Ta Data (Cultivate Your Data) conference earlier this year

the refreshing emergence of the word ontology as a buzzword (as opposed to its "bad word" status only a couple of years ago)

how he applies his research and innovation work to improve enterprise information systems

his work to advance the practices of ontology design and knowledge graph building

the inspiration he has taken from Robert Stevens to approach ontology development in a more aligned (non-siloed) way

his ensuing adoption of the Linked Open Term methodology, an agile approach to ontology, and his broader work to reconnect "semantic silos"

his journey into proper philosophically grounded ontology practice, and the notable absence of a canonical source of best practices

the work at the LUMEN to develop a collaborative platform that guides practitioners to the tools available to each step in the ontology process

how an enterprise ontology is the "skeleton of your whole information system, a representation of your company" — and how it accelerates BI and discovery and retrieval

how much he's enjoying the return to neuroscience that comes with his work on neurosymbolic AI systems

Yann's bio

Yann Le Franc, PhD is the CEO and Scientific Director of e‐Science Data Factory S.A.S.U. Created in 2014, e-Science Data Factory is a French Innovation Center, aiming at proposing innovative solutions to transform data as key assets for industry and public organizations making their use of AI more efficient. The company leverages its participation into European Research Infrastructure projects to transfer knowledge and innovation to industry and public organizations.

Yann Le Franc has a PhD in Neurosciences and Pharmacology in 2004. After a postdoctoral experience in the US, he worked on data management projects for Neurosciences in the context of the International Neuroinformatics Coordinating Facility (INCF) where he developed a strong expertise in ontology design and semantic web technologies. He then contributed to several Research Infrastructure projects aiming at building EOSC, the European Open Science Cloud (EUDAT, EOSC-Hub,…) as an expert on Semantic Web, ontology design and FAIR Principles.

He is co‐chairman of the Research Data Alliance Vocabulary and Semantic Service Interest Group and the FAIR Mappings Working Group. He is the former co-chair of the FAIR Digital Object Forum Semantic Group and actively contributed to the EOSC Semantic Interoperability Task Force. He has been spearheading the FAIRification and standardization of semantic artefacts (i.e. ontologies, controlled vocabularies, …) and mappings/crosswalks in the context of FAIRsFAIR, OntoCommons and FAIR Impact projects. Through e-Science Data Factory, he is one of the co-founders of the Knowledge Graph Alliance. For the last 2 years and a half, Yann has been the Head of the EUDAT Secretariat, a pan-European e-Infrastructure, providing data management services, storage and computing services to European researchers. He is leading the creation of the EUDAT Node, one of the first nodes of the EOSC Federation.

Connect with Yann online

LinkedIn

email: ylefranc at esciencefactory dot com

Resources mentioned in this interview

European Open Science Cloud

LUMEN, GRAPHIA, and OPERAS

EUDAT

Video

Here’s the video version of our conversation:

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 54. One of the most interesting developments in AI architectures is the emergence of hybrid systems that integrate neural-network based LLMs with symbolic AI like knowledge graphs. For Yann Le Franc, this is very familiar terrain. He began his career in the neural network world as a computational neuroscientist but then quickly discovered the power of ontologies during a post-doc. He's delighted to see his interests re-converge in the current hybrid-AI era.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 54 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yann Le Franc. Yann is, as you can tell from his name, he's based in France. He's in Montpellier. He's the CEO and founder at e‐Science Data Factory. He also heads the EUDAT Secretariat. He's the co-chair of the Vocabulary and Semantic Serviced Interest Group at the Research Data Alliance Association and he co-chairs the Fair Mapping Project. I have no idea how he found the time to talk with me with all that going on, but welcome, Yann. Tell the folks a little bit more about what you're up to these days.

Yann:

Yeah, thank you, Larry, for having me on the podcast. Well, as you've said, a lot of things are going on. We are working a lot on European research project to help build the European Open Science Cloud. And of course, we specialize on semantic web services, knowledge graphs, and the connection with AI. And in some of these projects, so the two projects that are currently going on are called LUMEN, and the other one is GRAPHIA. In LUMEN, we are developing a platform for industrializing ontology development and in GRAPHIA we're building a federation of knowledge graph for social science and humanities.

Larry:

Yeah, that's actually how we met. There was a conference in Zagreb last fall and put together by, I guess OPERAS, the overarching org, I guess that-

Yann:

The coordinator, yes.

Larry:

Yeah. And one of the first things I want to talk about is that I think it was there we talked about your journey from, you started as a neuroscientist and here you are this world-class ontologist. How did that transition happen?

Yann:

Yes, indeed. I started as a cellular biologist to be correct that specialized in neuroscience. And then I get my finger hooked into computational neuroscience. I was fascinated by the idea that you can simulate brain cells in computers. So move forward and keep going on working on, how do you say, simulating small neural networks, realistic biologically grounded neural networks. And then while during that time, I generated my own data and I had to deal with the data from my colleagues and I discovered a hell of working with data generated by others and even sometimes your own data.

Yann:

And I ended up after my postdoc in the US to actually join a team in Belgium in Antwerp and work for the international neuroinformatics coordinating facility based in Sweden. And there, my job was to actually develop an ontology for computational neuroscience models. And that's how I discovered the world of ontology and realized the power of it in terms of helping you structuring your data, sharing with others with the included meaning, which means the other understands what's in the data more easily. And of course, the connection to knowledge graph, where in the end you can start interlinking all your data together in a more simpler way and a more flexible way.

Larry:

Nice. We had a close call there. We could easily have lost you to the whole neural network community with that kind of background, but I love the story of how the need to understand and work with your data drove this. That's great. But that was a little while ago. So you've seen a lot of this. You've seen how neural network stuff is informing this, the benefits of ontologies and knowledge graphs in managing science data and understanding it better. And now we're all of a sudden, fast-forward to this year and we're three and a half years into this crazy new generative AI era. What's your kind of take on the current landscape? How has it changed I guess your interests and your work and your...

Yann:

We kind of see that coming with the development of very major AI. I don't remember the name (AlphaGo), but the one that actually won against the Go champion and all these big advances. And then we had the LLMs coming up, which is interesting. It's an implementation of neural network, gigantic neural networks. It's like trying to simulate the full brain. Unfortunately, it's not a brain definitely because it's just a statistical model. So it's interesting to see the democratization of the usage of the AI, at least the LLMs. It's a bit scary because I think the naming is wrong if we should keep calling them LLMs and not AI because there's nothing intelligent in these systems. It's just statistical models that gives you some interesting outputs that can emulate intelligence, but they don't understand the meaning. And that's also why I'm happy to go back to neuroscience and neural networks because now we are working a lot also on connecting knowledge graphs and ontology to LLMs to make them smarter because they are fantastic models for language.

Yann:

I'm using them also for helping me writing text in English because I'm French, so I'm writing in Frenchlish. So it's nice to have an LLM that helps me correct some of my writing but still you realize that sometimes the answers are comp