Knowledge mining is emerging as the enabling technology for new forms of information access and multilingual information access (MLIA, which encompasses both terms), as it combines the last advances in text mining, knowledge acquisition, natural language processing and semantic interpretation. Question answering, information access based on entities, cross-lingual information access, and navigation via crossdocument relations are examples of new applications that are being adopted both by start-ups and consolidated companies such as Google, Yahoo and Microsoft. KNOW2 will emulate and improve current MLIA systems with research to enable the construction of an integrated environment allowing the cost-effective deployment of vertical information access portals for specific domains.

The goal of this project is to create new multilingual text-mining tools as a core for a new generation of search engines and text analyzers. These engines should be able to find the need behind the query and offer specialized services and interfaces suitable to the user's information requirements. And also not only the search of web-pages, semi-structurated and multimedia data sources but the automatic discovering and organization of all this data.

The goal of the CHIL project is to make everybody's daily life easier. We aim to realize computer services that are delivered to people in an implicit, indirect and unobtrusive way. This will free people to interact with people and reposition machines to be in the background and - like electronic butlers - attempting to anticipate and serve people's needs.

And so what does it mean? It means that we have an "intelligent meeting room" (which stands for "room full of microphones and cameras") and a rack of computers that try to work out what are the people doing and what do they need. Then it provides services such recognition of actions and faces, question answering, logging of activities for future usage like automatic summarization, etc.

• Question Answering
• Machine Learning
• Issues related to Automatic Speech Recognition in QA