hyperquest

HYPERQUEST aims at creating a tool for knowledge extraction from text documents via iterative contextual learning, for use by medium-sized and large companies where these needs are recurrent (automatic document analysis and editing, information retrieval from internal databases, etc.). It aims at overcoming technological barriers by creating intuitive interfaces to help users co-construct complex knowledge extraction and transformation tasks simply and with very few examples, at a level of specificity and rigor that current tools do not allow.

HQ is based on our innovation, semantic hyper-graphs, which recursively link an arbitrary number of concepts to better capture the expressiveness of natural language, beyond the rigidity of traditional knowledge graphs, and allow powerful flexibility in defining linguistic patterns for massive information extraction — at the confluence of symbolic and connectionist (machine/deep learning) Artificial Intelligence.

team

Camille Roth

Research director
Formalization

Telmo Menezes

Research
Technical architecture
Neuro-symbolic modeling

Florian Schmitt

UX / UI Design
Ergonomic interface design
Front-end integration

partners

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