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Knowledge Discovery and Expertise Location in Unstructured and Semi-structured Data in the Knowledge Management Context

The important technological aspect of knowledge management is its support for companies and institutions in their pursuit for structuring, filtrating, contextualization of document bases and additional knowledge artifacts and learning from them. Today, when significant amount of corporate knowledge resides in unstructured heterogeneous data sources (explicit knowledge) and in the minds of individuals (tacit and implicit knowledge), the importance of tools capable of unifying, structuring and contextualizing these isolated "islands", as well as discovering new connections and structures between them, emerges. Although the knowledge management and intellectual capital management are heavily discussed topics nowadays, practical functional solutions for enterprise are not very common yet. The knowledge maintenance requires preserving its context. And that is best expressed in the natural language. Therefore most of the contemporary knowledge management systems are built on unstructured or weakly structured documents. Aside from context, the knowledge management is unthinkable without people and their explicitly inexpressible knowledge (the tacit knowledge). The pragmatical conception of knowledge management system must therefore provide the connection between “those who know and those who needs to know”. The doctoral thesis presents the concept of systems designed for knowledge discovery and expertise location in unstructured and semi-structured data in context of knowledge management (shortly TAEL Text Analysis / Expertise Location). In the first part of the thesis the concepts of explicit, tacit and implicit knowledge are critically discussed. These are confronted with opinions derived from the state of the art knowledge management literature. Author then proposes his own categorization of software tools supporting knowledge management. The TAEL systems emerge from this categorization. The main part of the thesis discusses the TAEL systems, the motivators behind these systems and the description of the three main pillars of the TAEL systems corporate search, expertise location and text analysis. The description of mainand sub-processes, techniques and interfaces used by existing tools for knowledge discovery and expertise location is detailed, after which the general TAEL systems architecture follows. The focus in this part is given to cluster analysis, multidimensional taxonomies, the creation of expert profiles and knowledge maps, as well as to the importance of metadata for TAEL systems. The visualization possibilities and social network analysis are discussed as well. The second half of the thesis is dedicated to the practical applications of the TAEL systems and the typical structure of the TAEL project. Author describes the interconnections between TAEL and HR systems, TAEL and Business Intelligence and the use of TAEL systems for business processes and forensics support. Additional subchapter talks about the importance of TAEL systems for intellectual capital spreading and consequence with the KBPR (Knowledge Based Process Reengineering) methodology. The rest of the thesis presents the methodology for the TAEL systems evaluation. More than 150 criteria divided into 8 subgroups, as well as the methodological framework for the selection and criteria importance determination is defined. The uniqueness of this thesis is in the detailed coverage given to the questions of supporting the knowledge discovery and expertise location. Author believes, that TAEL systems represent the important qualitative shift towards the better use of software tools for knowledge management support.