from text to structure

HYPERQUEST is built on Semantic Hypergraphs, our approach to representing natural language as recursive semantic structures. Instead of treating sentences as flat sequences of words, it decomposes them into interconnected concepts, explicitly representing entities, actions, roles, qualifiers, and context.

Because the representation itself captures meaning, only a small set of intuitive patterns is needed to retrieve complex information. HYPERQUEST combines the transparency and explainability of symbolic AI with the contextual understanding of modern deep learning-based NLP.

semantic hypergraphs

Semantic Hypergraphs generalize traditional knowledge graphs by allowing relationships to connect any number of concepts - and even other relationships. This recursive design naturally represents nested statements, qualifications, and contextual information while remaining understandable to non-specialists.

The result is a semantic representation that is both highly expressive and easy to query, making sophisticated information extraction transparent rather than opaque.

Piano

Piano

Black piano

black

piano

Maria's piano.

Maria

's

piano

Maria's black piano.

Maria

's

black

piano

Maria plays piano.

Maria

plays

piano

Maria plays piano after school.

Maria

plays

piano

after

school

Pedro says that Maria plays piano after school.

Pedro

says that

Maria

plays

piano

after

school

Maria plays piano and Pedro watches movies.

Maria

plays

piano

and

Pedro

watches

movies

do you want to know more?

If you have any questions about the project or would like to get in touch with us, we would be pleased to hear from you.