Durham Zoo (DZ) is a project to design and operate a concept search engine for science and technology. In DZ a concept includes a solution to a problem in a particular context.

Concept searching is rendered complex by the fuzzy nature of a concept, the many possible implementations of a same concept, and the many more ways that the many implementations can be expressed in natural language. An additional complexity is the multitude of languages and formats in which concepts can be disclosed.

Despite on-going developments in Artificial Intelligence, humans understand language, inference, implication and abstraction, and hence concepts, much better than computers. We are 7 billion on the planet and we have the Internet as the backbone for Collective Intelligence. Computers on the other hand, are much better at storing and processing vast amounts of data. And so our concept search engine uses humans to store concepts via a shorthand that can be stored, processed and searched by computers: so humans IN and computers OUT.

The shorthand is classification: tags that define the content of a disclosure and that exist in a defined structure. The classification is designed to be powerful in terms of defining and searching concepts. It is also suited to a crowdsourcing effort: simple and intuitive to use, and adapted to distributed development.