Purpose – Durham Zoo (hereinafter – 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. Design – Concept searching is rendered complex by the fuzzy nature of a concept, the many possible implementations of the same concept, and the many more ways that the many implementations can be expressed in natural language. An additional complexity is the diversity of languages and formats, in which the concepts can be disclosed. Humans understand language, inference, implication and abstraction and, hence, concepts much better than computers, that in turn are much better at storing and processing vast amounts of data. We are 7 billion on the planet and we have the Internet as the backbone for Collective Intelligence. 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: metadata in a structure that can define the content of a disclosure. The classification is designed to be powerful in terms of defining and searching concepts, whilst suited to a crowdsourcing effort. It is simple and intuitive to use. Most importantly, it is adapted to restrict ambiguity, which is the poison of classification, without imposing a restrictive centralised management. In the classification scheme, each entity is shown together in a graphical representation with related entities. The entities are arranged on a sliding scale of similarity. This sliding scale is effectively fuzzy classification. Findings – The authors of the paper have been developing a first classification scheme for the technology of traffic cones, this in preparation for a trial of a working system. The process has enabled the authors to further explore the practicalities of concept classification. The CmapTools knowledge modelling kit to develop the graphical representations has been used. Practical implications – Concept searching is seen as having two categories: prior art searching, which is searching for what already exists, and solution searching: a search for a novel solution to an existing problem. Prior art searching is not as efficient a process, as all encompassing in scope, or as accurate in result, as it could and probably should be. The prior art includes library collections, journals, conference proceedings and everything else that has been written, drawn, spoken or made public in any way. Much technical information is only published in patents. There is a good reason to improve prior art searching: research, industry, and indeed humanity faces the spectre of patent thickets: an impenetrable legal space that effectively hinders innovation rather than promotes it. Improved prior-art searching would help with the gardening and result in fewer and higher-quality patents. Poor-quality patents can reward patenting activity per se, which is not what the system was designed for. Improved prior-art searching could also result in less duplication in research, and/or lead to improved collaboration. As regards solution search, the authors of the paper believe that much better use could be made of the existing literature to find solutions from non-obvious areas of science and technology. The so-called cross industry innovation could be joined by biomimetics, the inspiration of solutions from nature. Crowdsourcing the concept shorthand could produce a system ‘by the people, for the people’, to quote Abraham Lincoln out of context. A Citizen Science and Technology initiative that developed a working search engine could generate revenue for academia. Any monies accruing could be invested in research for the common good, such as the development of climate change mitigation technologies, or the discovery of new antibiotics.@en

The ‘Open Data’, ‘Open Knowledge’ and ‘Open Access’ movements promote the dissemination of information for societal benefit. Sharing information can benefit experts in a particular endeavour, and facilitate discovery and enhance value through data mining. On-going advances in Artificial Intelligence (AI) are accelerating the development of invention machines to which few individual information donors have access. Is the movement toward open information further empowering the few? Does open information promote collective intelligence, or does the collection of information both from and about many individuals present a collection of intelligence that can be leveraged by a very few? We propose the Durham Zoo project to develop a search-and-innovation engine built upon crowd-sourced knowledge. It is hoped that this will eventually contribute to the sharing of AI–powered innovation whilst funding academic research.@en

The ‘Open Data’, ‘Open Knowledge’ and ‘Open Access’ movements promote the dissemination of information for societal benefit. Sharing information can benefit experts in a particular endeavour, and facilitate discovery and enhance value through data mining. On-going advances in Artificial Intelligence (AI) are accelerating the development of invention machines to which few individual information donors have access. Is the movement toward open information further empowering the few? Does open information promote collective intelligence, or does the collection of information both from and about many individuals present a collection of intelligence that can be leveraged by a very few? We propose the Durham Zoo project to develop a search-and-innovation engine built upon crowd-sourced knowledge. It is hoped that this will eventually contribute to the sharing of AI–powered innovation whilst funding academic research.@en

Searching for concepts in science and technology is often a difficult task. To facilitate concept search, different types of human-generated metadata have been created to define the content of scientific and technical disclosures. Classification schemes such as the International Patent Classification (IPC) and MEDLINE's MeSH are structured and controlled, but require trained experts and central management to restrict ambiguity (Mork, 2013). While unstructured tags of folksonomies can be processed to produce a degree of structure (Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom enjoyed by the crowd typically results in less precision (Stock 2007). Existing classification schemes suffer from inflexibility and ambiguity. Since humans understand language, inference, implication, abstraction and hence concepts better than computers, we propose to harness the collective wisdom of the crowd. To do so, we propose a novel classification scheme that is sufficiently intuitive for the crowd to use, yet powerful enough to facilitate search by analogy, and flexible enough to deal with ambiguity. The system will enhance existing classification information. Linking up with the semantic web and computer intelligence, a Citizen Science effort (Good, 2013) would support innovation by improving the quality of granted patents, reducing duplicitous research, and stimulating problem-oriented solution design. A prototype of our design is in preparation. A crowd-sourced fuzzy and faceted classification scheme will allow for better concept search and improved access to prior art in science and technology. Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search | Request PDF. Available from: https://www.researchgate.net/publication/263544917_Crowd-Sourcing_Fuzzy_and_Faceted_Classification_for_Concept_Search [accessed Jul 26 2018].@en