fmichel

Franck Michel

Université Côte d’Azur, CNRS, Inria.

I am a CNRS research engineer involved in the integration of heterogeneous data and their publication and sharing as Knowledge Graphs on the Web, using knowledge engineering, the Semantic Web and Linked Open Data technologies.

I am a member of I3S laboratory, and a member of Inria's Wimmics team.

Research activities

Knowledge Graphs, Integration of Heterogeneous Data

I am involved in research activities meant to enable the integration of heterogeneous data based on a knowledge engineering approach, as well as the sharing and reuse of these data. My work addresses several research questions:

  • How to build Knowledge Graphs and foster data reuse by complying with the FAIR principles?
  • How to overcome data structural and semantic heterogeneity in order to reconcile and make sense of large datasets distributed at Web-scale?
  • How to enable the Web-scale discovery and consumption of data?
  • How to make sense of large scientific corpora and support Open Science by allowing researcher to explore and visualize information extracted from research articles?
  • How to “talk to the data” by translating natural language questions into queries in structured query languages such as SPARQL?
  • How to enrich existing knowledge bases by extracted knowledge graphs from texts?

These research questions are applied to various domains, in particular agronomy, agriculture and biodiversity, but also ancient literature and music.

Research projects and communities

Here are some projects I am or was involved in:

Here are the community projects I'm currently involved in:

Publications

Complete list of publications and communications: HAL CV.

Also find me on ReasearchGate.

Contact

Address:
Université Côte d’Azur, CNRS, Inria - I3S, UMR 7271
930 route des Colles - Bât. Les Templiers
BP 145 - 06903 Sophia Antipolis CEDEX - France

Email: franck [dot] michel [at] inria [dot] fr

Find me on: ResearchGate, Github, LinkedIn, Twitter, SlideShare, Flickr, Instagram

Selected talks

Open Science, reproducibible research, and the citation of articles, code and data alike

Given 2024-04-04.

ISSA: Generic Knowledge Model and Visualization tools to Help Scientists Make Sense of Archive

Wimmics Monthly Seminar 2022-12-15 / ISWC 2022 resource track replay

Covid-on-the-Web: Knowledge Graph and Services to Advance COVID-19 Research

Presented at the ISWC 2020 conference, resource track.

Bioschemas: Marking up biodiversity websites for data discovery & integration

TDWG webinar series, 2021-03.

Integration of biodiversity data from web pages to knowledge graphs, a computer scientist view point

DIADE research unit seminars (http://diade.ird.fr), 2021-04-13.

Creation/publication of datasets

ISSA Agritrop Dataset, Semantic index of the Agritrop open scientific archive. github DOI

TAXREF-LD, Linked Data knowledge graph of the French taxonomic register. Franck MICHEL, Catherine FARON, Sandrine TERCERIE, Olivier GARGOMINY. 2017(2022. github sparql article DOI

Covid-on-the-Web. Franck Michel, Fabien Gandon, Valentin Ah-Kane, Anna Bobasheva, Elena Cabrio, Olivier Corby, Raphaël Gazzotti, Alain Giboin, Santiago Marro, Tobias Mayer, Mathieu Simon, Serena Villata, Marco Winckler. 2020. github sparql article DOI.

WASABI RDF Knowledge Graph. An RDF representation of the WASABI corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. 2020. github sparql article DOI

WeKG-MF, Weather Knowledge Graph of Météo France Meteorological Observations. 2022. github sparql article DOI

WheatGenomicsSLKG, Wheat Genomics Scientific Literature Knowledge Graph. 2023. github sparql DOI

Software Development

ISSA visualization and search web application: Franck MICHEL, Youssef Mekouar (2022). Github: visu and backend

ISSA Processing Pipeline: Anna Bobasheva, Franck MICHEL (2022). github DOI

SPARQL Micro-Services: Querying Web APIs with SPARQL. Franck Michel. 2018. github

Morph-xR2RML: MongoDB-to-RDF translation and SPARQL rewriting: Franck Michel, Freddy Pryiatna. Implementation of the xR2RML mapping language for MongoDB databases. 2017. github DOI

The VO Administration and operations PORtal (VAPOR). Franck Michel, Flavien Forestier. 2014. web DOI

EGI Virtual Organisations Support Tools. Franck Michel. 2013. web DOI

NeuroLOG platform. Alban Gaignard, Franck Michel, Johan Montagnat, Javier Rojas Balderrama, Farooq Ahmad, Bacem Wali. 2008. web

Background and Position

Organizing and Program Committees

I was/am a member of the program committees for the following conferences and/or workshops:

  • SEMANTiCS 2024, 20th International Conference on Semantic Systems
  • ECAI 2024, European Conference on AI
  • ESWC 2024, The Extended Semantic Web Conference
  • The Web Conference 2024, The Extended Semantic Web Conference
  • SEMANTiCS 2023, 19th International Conference on Semantic Systems
  • ESWC 2022, The Extended Semantic Web Conference
  • KGCW 2022, Third International Workshop on Knowledge Graph Construction
  • SCG 2021, First workshop on Squaring the circle on graphs
  • KGCW 2021, Second International Workshop on Knowledge Graph Construction
  • ESWC 2021, The Extended Semantic Web Conference
  • ICCS 2020, The International Conference on Computational Science
  • IJCAI 2020, 29th International Joint Conference on Artificial Intelligence
  • SEMANTiCS 2019, 15th International Conference on Semantic Systems
  • Knowledge Graph Building (KBD), workshop of the Extended Semantic Web Conference 2019 (ESWC)
  • Hypermedia Multi-Agent Systems (HyperAgents 2019), workshop of the Web Conference 2019
  • EKAW 2018, 21th International Conference on Knowledge Engineering and Knowledge Management
  • SEMANTiCS 2018, 14th International Conference on Semantic Systems
  • ISWC 2018, 17th International Semantic Web Conference
  • ICCS 2018, 23rd International Conference on Conceptual Structures
  • WWW 2018, The Web Conference 2018
  • ISWC 2017, 16th International Semantic Web Conference
  • SEMANTiCS 2017, 13th International Conference on Semantic Systems
  • ICCS 2016, 22nd International Conferences on Conceptual Structures
  • SI&IA 2015, Systèmes d'Information et Intelligence Artificielle 2015

I was/am a member of the organizing committees for the following conferences and/or workshops:

Wild ideas

Large Language Models as the components of a conscious AI?

AIs, and LLMs in particular, are not conscious. They are reactive systems, they respond to an input by producing an output. By contrast, consciousness can be defined as the ability to form thoughts for oneself, without the need for external stimulus.

What if we fine-tuned several LLMs to collaborate together, following the model of human psyche.

  • The “conscious” model would be fine-tuned to remain in the realm of values, morality, norms, and logical thinking. This is the one that would interact with the “outside” world and provide material to the “unconscious” model.
  • The “unconscious” model (or “subconscious” depending on the definitions) would be fine-tuned to phrase drive, desires, regardless of any norms nor value system.
  • The “preconscious” model would be fine-tuned to filter/rewrite outputs of the “unconscious” to let only acceptable outputs make their way to the “conscious”, while also providing it with material in a feed-back loop.

This way, we could imagine being able to design some sort of a conscious AI system. But this raises multiple questions: How would it be bootstrapped? Individually, each of the 3 LLMs remains a question-answering system, it does not take the initiative of producing an output. So how to start this, and once this starts, how to control the flow? If we skip the conscious model (to try and simulate sleep) and leave the unconscious model talk with itself, could it come up with dreams?

Frugality by design: less is good

Multiple tools of the daily life consume energy and/or resource even though that's not the intend of the user. These tools must be redesigned with a specific bias towards frugality.

This can be implemented as a default behavior, as a nudge etc. Examples:

  • Mixer tap delivers heated water by default: In the middle position, most mixer taps mix half ambient temperature water and half heated water. Although users may not need heated water. These should be redesigned with a middle position that only delivers ambient temperature water, so that getting heated water will require a deliberate action from the user.
  • Public space fountain water delivers cooled by default: Fountains in public spaces usually deliver cooled water by default although users may not want that. These should be redesigned with a default ambient temperature water, so that getting cooled water will require a deliberate action from the user.
  • fmichel.txt
  • Last modified: 2025/01/17 11:20
  • by fmichel