Large Scale Semantic Computing\nSemantic Web technologies\ndistributed reasoning\nprobabilistic reasoning\nweb-scale inference\ninformation retrieval

Current Semantic Web reasoning systems do not scale to the requirements of their hottestapplications, such as analyzing data from millions of mobile devices, dealing with terabytes ofscientific data, and content management in enterprises with thousands of knowledge workers.We will build the Large Knowledge Collider (LarKC, for short, pronounced "lark"), a platformfor massive distributed incomplete reasoning that will remove these scalability barriers. Thiswill be achieved by:- Enriching the current logic-based Semantic Web reasoning methods with methodsfrom information retrieval, machine learning, information theory, databases andprobabilistic reasoning,- Employing cognitively inspired approaches and techniques such as spreading activation,focus of attention, reinforcement, habituation, relevance reasoning, and boundedrationality.- Building a distributed reasoning platform and realising it both on a high-performancecomputing cluster and via "computing at home".The consortium is an interdisciplinary team of engineers and researchers in Computing Science,Web Science and Cognitive Science, well qualified to realize this ambitious vision. The LargeKnowledge Collider will be an open architecture. Researchers and practitioners from outside theconsortium will be encouraged to develop and plug in their own components to drive parts of thesystem. This will make the Large Knowledge Collider a generic platform, and not just a singlereasoning engine.The success of the Large Knowledge Collider will be demonstrated in three end-user casestudies. The first case study is from the telecom sector. It aims at real-time aggregation andanalysis of location data obtained from mobile phones carried by the population of a city, in orderto regulate city infrastructure functions such as public transport and to provide context-sensitivenavigation information. The other two case studies are in the life-sciences domain, relatedrespectively to dr