Knowledge Mobilisation Joint Research1 KNOWLEDGE MOBILISATION RESEARCH, Part II Christer Carlsson IAMSR/Abo Akademi University Draft 1.5 August 4, 2006

Knowledge Mobilisation Joint Research2 Executive Summary Knowledge Mobilisation has five parts: (i) creating, building & forming knowledge; (ii) activating latent knowledge; (iii) searching for, finding and systematising hidden knowledge, (iv) making knowledge mobilisation operational with MAS technology, and (v) expanding the limits of the possible in the structures of everyday life by making knowledge mobilisation part of mobile value services and using mobile technology Proposal : (i) a start-up project in Finland on Knowledge Mobilisation, to form and activate (ii) a joint strategic research project on Knowledge Mobilisation, which (iii) will drive a European Centre of Excellence in Knowledge Mobilisation

Knowledge Mobilisation Joint Research3 Knowledge Mobilisation Potential Knowledge Management may be valid for a static world in which pre-specified inputs, processing logic and the expected outcomes represent an optimal mode of activities Knowledge Mobilisation [adapted from Malhotra] develops relevant technologies for –Intelligence in action which requires an active, affective and dynamic representation of knowledge as a dynamic construct Active: knowledge is adaptive to a changing context Affective: cognitive, rational and emotional [subjective interpretation] Dynamic: proactive and adaptive reinterpretation of data, information and assumptions –Continuous re-assessment of performance outcomes “Knowledge resides in the user and not in the collection” [Churchman]; “knowledge, unlike information, is about beliefs and commitment” [Nonaka-Takeuchi]

Knowledge Mobilisation Joint Research4 Possible Research Areas Knowledge Mobilisation:  Creating, building and forming knowledge with contributions to and enhancements of the semantic web  Activating latent knowledge, formalising it and using it for planning, problem solving and decision making with soft computing methods  Searching for, finding and systematising hidden knowledge in very large sets of data using data and text mining  Making knowledge mobilisation operational with intelligent, multi-agent systems technology  Expanding the limits of the possible in the structures of everyday life by making knowledge mobilisation part of mobile value services

Knowledge Mobilisation Joint Research5 Creating, Building, Forming Knowledge  Knowledge mobilisation is expected to work with semantic web technology; the technology developed within the project is expected to contribute to the W3C effort to build the Semantic Web (“the representation of data on the World Wide Web”)  The semantic web is based on the Resource Description Framework (RDF), which integrates a variety of applications using XML for syntax and URIs (Uniform Resource Identifiers), for naming  The RDF makes it possible to represent the semantics of a web page as metadata  The RDF and the Ontology Web Language (OWL) form the semantic web

Knowledge Mobilisation Joint Research6 Creating, Building, Forming Knowledge  “I see fuzzy logic and other heuristic systems as being used within agents that trawl the Semantic Web … it (fuzzy logic) cannot be the basis for the Semantic Web. [as] … you have to be able to follow links successively across the globe without getting fuzzier” [Tim Berners- Lee]  The key point is that fuzzy logic can be used in constructs to be used with or in the semantic web  Data type definitions have been built for linguistic variables; it has been proved that fuzzy information (defined with discrete or continuous fuzzy sets) with a description based on DTDs can be exchanged between application systems using XML

Knowledge Mobilisation Joint Research7 Creating, Building, Forming Knowledge  The Semantic Web being designed deals with hard semantics for handling crisp data; RDF cannot be used to represent soft semantics; it is possible that Semantic Web will be irrelevant for handling most of the information used in practice, which is built on soft semantics,  It is possible to extend the RDF by encoding fuzzy sets/fuzzy logic in the RDF format; the fuzzy component will simply have a URI to a system of fuzzy sets or fuzzy logic or fuzzy conceptual graphs (which is a promising way to deal with natural language applications)  We need to implement a fuzzy ontology structure [FOS] in OWL

Knowledge Mobilisation Joint Research8 Latent Knowledge Mobilisation  Activating latent knowledge, formalising it and using it for planning, problem solving and decision making with soft computing methods  Fuzzy real options valuation methods: incomplete time series, imprecise data, future windows  Uncertainty is shown, not treated as a black box  “At some stage precision and relevance become conflicting issues”  Tacit knowledge brought to light through mind mapping; structures shown in storyline  Storyline is fed through real time data collection and processing with a MAS; from new information to new knowledge

Knowledge Mobilisation Joint Research9 Volatility and Time Affect the ROV

Knowledge Mobilisation Joint Research10 Hidden Knowledge Mobilisation  Searching for, finding and systematising hidden knowledge in very large sets of data using data and text mining  Developing and using proper tools the data will reveal knowledge which has not previously been available and which will offer new and better descriptions, explanations and predictions  The self-organizing neural network (SOM) in financial performance benchmarking  Semi-automated qualitative analysis can be facilitated by intelligent text mining tools, allowing analysts to study large amounts of financial texts in a short period.  Text summarisation as a further enhanced step using multi-agent systems technology

Knowledge Mobilisation Joint Research11 Operational Knowledge Mobilisation  Making knowledge mobilisation operational with intelligent, multi-agent systems technology  We can suggest an agent taxonomy, which works with five areas: –Interaction range: data and information, perspective, resources –Interaction depth: roles, time, empathy, transparency, commitment –Learning and knowledge: language (fixed vs. fuzzy and learned), trust level, ontology, familiarity –Structure: stability, layers, diversity, reusability, flexibility –Quantity: amount (many vs. single), emergency

Knowledge Mobilisation Joint Research12 Expected Results Multi-agent system, shared (fuzzy) ontology and semantic web KnowMobile Agent Semantic web Fuzzy ontology

Knowledge Mobilisation Joint Research13 Impact of Knowledge Mobilisation  Expanding the limits of the possible in the structures of everyday life by making knowledge mobilisation part of mobile value services  Historical change when expanding the limits of the possible  Context aware, personalised, content-adapted knowledge mobilisation as part of mobile value services  Will have an impact on most arenas of life  Latent and hidden knowledge can be part of mobile value service

Knowledge Mobilisation Joint Research14 Knowledge Mobilisation  Demand driven knowledge acquisition and creation [cf. UC Berkeley Mobile Media Metadata projects]  First enabler: 3G and 4G mobile technology  Second enabler: the W3C drive to make semantic web available on smart phones  Third enabler: fuzzy ontology as part of the semantic web; soft computing as part of the activation of latent knowledge  But: the first two enablers will not work without the third …