Project Technical details

WeKnowIt consists of five layers of Intelligence that are: Personal Intelligence, Media Intelligence, Mass Intelligence, Social Intelligence and Organisational Intelligence (Figure 1). A short description of the five layers is presented below.


Layers of Intelligence in WeKnowIt


Figure 1 : Layers of Intelligence in WeKnowIt

Personal Intelligence
Methodologies and technologies will be designed, developed and tested for Personal Intelligence management where citizens or users are enabled to interact with the WeKnowIt system in terms of both uploading information and accessing available information. WeKnowIt will enable efficient interaction using devices such as personal computers (e.g. via internet), but also devices with limited capabilities in terms of interfaces (e.g. mobile phones, PDAs, etc.). A main requirement is the creation of an interaction modality able to anticipate users' needs in order to enable effective use for user interaction, especially in emergency situations where time is limited and attention can be easily diverted.
Strategies and modalities of interaction which will enable anticipation of user actions (hence reducing the interaction complexity) as well as a set of strategies to maintain and guarantee privacy. Usability will be empowered by content based analysis of input (images, text, videos), as well as on social, community and organisational analysis which will lead to more autonomous content actions, thereby reducing the interaction burden on the user.

Media Intelligence
Most of related knowledge and information originates from raw content, be it in the form of e.g. text, images, video, or speech. Human annotation or tagging used in social networks is a way to represent or handle the underlying knowledge, yet despite the human intervention, content remains highly unstructured and it is quite difficult to extract semantics and correlate to other sources of information.
Methods for single and cross-media analysis will use prior knowledge, either implicit, in the form of supervised learning from training data, or explicit, in the form of knowledge driven approaches to maximise extraction and overcome limitations of the current technology.

Mass Intelligence
Mass Intelligence is recognition and understanding of facts and trends by exploitation of massive user contributions. Mass Intelligence is always useful when the aggregation of data, metadata and behaviour from and of a large mass of users gives new insight that would not be possible by investigating the contributions at the individual level only. WeKnowIt will investigate four aspects of Mass Intelligence:

1. Mass question answering: How does question answering by masses of users give improved insights?
2. Mass interaction feedback: How does explicit and implicit feedback by usersduring interaction may be gathered and analyzed to improve overall importance ratings and rankings of media and metadata?
3. Mass classification and clustering: How may classifications of individuals be aggregated into meaningful overall classifiers and clusters of media and facts?
4. Mass evolution analysis: How may a change in the behaviour and usage of data affect the understanding of media and facts?

Social Intelligence
WeKnowIt will also target the analysis, recognition and understanding of needs and capabilities of communities and communication interaction patterns. It will exploit existing work in Social Network Analysis and the dynamics of social systems improving upon state-of-the-art in terms of visualisation, navigation, as well as community analysis and management techniques. More specifically, WeKnowIt will aim:

1. To improve community knowledge by the cross-usage of Personal, Mass and Organisational Intelligence as well as to provide community knowledge to fertilise Personal, Mass and Organisational Intelligence.
2. To develop an intuitive, easy to use, flexible and powerful community administration platform that supports self organisation of communities and a wide range of privacy protection policies.
3. To develop scalable community analysis tools, which are context sensitive and adhere to privacy protection policies.
4. To communicate efficiently community structure and knowledge in line with existing privacy policies.
5. To validate and demonstrate the feasibility of community services in the case studies under operating conditions.

Organisational Intelligence
Finally, WeKnowIt will bring the innovation of Web 2.0 technologies to the field of Organisational Intelligence where processes and workflows are set up in order to bring the right piece of knowledge at the right time to the right person in the organisation in order to support decision making. In WeKnowIt, however, this knowledge is not necessarily produced by the individual knowledge worker, but rather by the interaction with the layers of Personal, Media, Mass and Social Intelligence. The objective is to research new ways of setting up such a layer of Organisational Intelligence as a mixture of organisational structuring (social hierarchies, workflows, groups of interest, person roles) and self-organizing intelligence.

Seventh Framework Programme