About knowledge modelling and sharing
- Foster capability to share and democratize access to ontologies and to the methodologies needed to govern knowledge models,
- Allow advancing semantic interoperability by establishing shared guidelines and consistent modeling rules across industries and organizations.
- Develop methodologies for better ontology modeling within SLS,
- Bridge semantic web and AI to make both more powerful together.
About the SLS open-source platform
- Promote what has been achieved so far with SousLeSens so that precise, rigorous tooling of W3C standards becomes accessible to everyone:
- Regard the SLS platform as a pivotal tool for embedding best practices in knowledge engineering; bridge the gap between technical ontology development and practical, user-friendly applications, enabling non-experts to intuitively leverage semantic technologies and fostering collaboration while reducing redundancy to drive sustainable innovation in industrial and academic ecosystems,
- Fill the need for knowledge models that are accessible to both professionals and machines,
- Strengthen the link between humans and machines through structured knowledge,
- SLS, as an integrated tool covering significant aspects of knowledge engineering, can bring to the field of knowledge management what spreadsheets did at the beginning of the development of office computing: a tool for democratizing new functionalities,
- Make SLS a robust and reliable tool.
About a community involvement
- Grow a large, active user community around SLS,
- Lower the barrier to semantic web technologies for technical professionals,
- Fostering a wider ecosystem and community for better collaboration around semantic-web technologies and their shared, open applications for mutual benefits.
About philosophical foundations
- Humanity shall overpass the civilization that always disappears (Egyptians, Romans, Mayas), and a sustainable way of transmitting knowledge and know-how is thus needed, but neither AI based nor engraving stones.
About intellectual foundations
- Relying on solutions built by mathematicians “playing with” statistical approaches is not satisfactory to address all “expected answers” we need in our today world, disregarding CPU/GPU requirements and capacities.
About industrial ambitions
- Most industrial companies are under-educated on above challenges, and it is our mission to give them advice and oversight.