The rapid growth and fragmented character of social media and publicly available structured data challenges established approaches to knowledge extraction. Many algorithms fail when they encounter noisy, multilingual and contradictory input. Efforts to increase the reliability and scalability of these algorithms face a lack of suitable training data and gold standards. Given that humans excel at interpreting contradictory and context-dependent evidence, the uComp project will address the above mentioned shortcomings by merging collective human intelligence and automated methods in a symbiotic fashion. The project will build upon the emerging field of Human Computation (HC) in the tradition of games with a purpose and crowdsourcing marketplaces. It will advance the field of Web Science by developing a scalable and generic HC framework for knowledge extraction and evaluation, delegating the most challenging tasks to large communities of users and continuously learning from their feedback to optimise automated methods as part of an iterative process. A major contribution is the proposed foundational research on Embedded Human Computation (EHC), which will advance and integrate the currently disjoint research fields of human and machine computation. EHC goes beyond mere data collection and embeds the HC paradigm into adaptive knowledge extraction workflows. An open evaluation campaign will validate the accuracy and scalability of EHC to acquire factual and affective knowledge. In addition to novel evaluation methods, uComp will also provide shared datasets and benchmark EHC against established knowledge processing frameworks. While uComp methods will be generic and evaluated across domains, climate change was chosen as the main use case for its challenging nature, subject to fluctuating and often conflicting interpretations. Collaborating with international organisations such as EEA, NOAA and NASA will increase impact, provide a rich stream of input data, attract and retain a critical mass of users, and promote the adoption of EHC among a wide range of stakeholders.

Project partners

• LIMSI-CNRS - Man-Machine Communication Department (France)
• MODUL University Vienna - Department of New Media Technology (Austria)
• The University of Sheffield - Department of Computer Science (United Kingdom)
• Vienna University of Economics and Business - Research Institute for Computational Methods (Austria)