The arrival of E-commerce systems have contributed greatly to the economy and have played a vital role in collecting a huge amount of transactional data. It is becoming difficult day by day to analyse business and consumer behaviour with the production of such a colossal volume of data. Enterprise 2.0 has the ability to store and create an enormous amount of transactional data; the purpose for which data was collected could quite easily be disassociated as the essential information goes unnoticed in large and complex data sets. The information overflow is a major contributor to the dilemma. In the current environment, where hardware systems have the ability to store such large volumes of data and the software systems have the capability of substantial data production, data exploration problems are on the rise. The problem is not with the production or storage of data but with the effectiveness of the systems and techniques where essential information could be retrieved from complex data sets in a comprehensive and logical approach as the data questions are asked. Using the existing information retrieval systems and visualisation tools, the more specific questions are asked, the more definitive and unambiguous are the visualised results that could be attained, but when it comes to complex and large data sets there are no elementary or simple questions. Therefore a profound information visualisation model and system is required to analyse complex data sets through data analysis and information visualisation, to make it possible for the decision makers to identify the expected and discover the unexpected. In order to address complex data problems, a comprehensive and robust visualisation model and system is introduced. The visualisation model consists of four major layers, (i) acquisition and data analysis, (ii) data representation, (iii) user and computer interaction and (iv) results repositories. There are major contributions in all four layers but particularly in data acquisition and data representation. Multiple attribute and dimensional data visualisation techniques are identified in Enterprise 2.0 and Web 2.0 environment. Transactional tagging and linked data are unearthed which is a novel contribution in information visualisation. The visualisation model and system is first realised as a tangible software system, which is then validated through different and large types of data sets in three experiments. The first experiment is based on the large Royal Mail postcode data set. The second experiment is based on a large transactional data set in an enterprise environment while the same data set is processed in a non-enterprise environment. The system interaction facilitated through new mashup techniques enables users to interact more fluently with data and the representation layer. The results are exported into various reusable formats and retrieved for further comparison and analysis purposes. The information visualisation model introduced in this research is a compact process for any size and type of data set which is a major contribution in information visualisation and data analysis. Advanced data representation techniques are employed using various web mashup technologies. New visualisation techniques have emerged from the research such as transactional tagging visualisation and linked data visualisation. The information visualisation model and system is extremely useful in addressing complex data problems with strategies that are easy to interact with and integrate.

Document clustering in information retrieval (IR) is considered an alternative to rank-based retrieval approaches, because of its potential to support user interactions beyond just typing in queries. Similarly, the Principle of Polyrepresentation (multi-evidence: combining multiple cognitively and/or functionally diff erent information need or information object representations for improving an IR system's performance) is an established approach in cognitive IR with plausible applicability in the domain of information seeking and retrieval. The combination of these two approaches can assimilate their respective individual strengths in order to further improve the performance of IR systems. The main goal of this study is to combine cognitive and cluster-based IR approaches for improving the eff ectiveness of (interactive) information retrieval systems. In order to achieve this goal, polyrepresentative information retrieval strategies for cluster browsing and retrieval have been designed, focusing on the evaluation aspect of such strategies. This thesis addresses the challenge of designing and evaluating an Optimum Clustering Framework (OCF) based model, implementing probabilistic document clustering for interactive IR. Thus, polyrepresentative cluster browsing strategies have been devised. With these strategies a simulated user based method has been adopted for evaluating the polyrepresentative cluster browsing and searching strategies. The proposed approaches are evaluated for information need based polyrepresentative clustering as well as document based polyrepresentation and the combination thereof. For document-based polyrepresentation, the notion of citation context is exploited, which has special applications in scientometrics and bibliometrics for science literature modelling. The information need polyrepresentation, on the other hand, utilizes the various aspects of user information need, which is crucial for enhancing the retrieval performance. Besides describing a probabilistic framework for polyrepresentative document clustering, one of the main fi ndings of this work is that the proposed combination of the Principle of Polyrepresentation with document clustering has the potential of enhancing the user interactions with an IR system, provided that the various representations of information need and information objects are utilized. The thesis also explores interactive IR approaches in the context of polyrepresentative interactive information retrieval when it is combined with document clustering methods. Experiments suggest there is a potential in the proposed cluster-based polyrepresentation approach, since statistically signifi cant improvements were found when comparing the approach to a BM25-based baseline in an ideal scenario. Further marginal improvements were observed when cluster-based re-ranking and cluster-ranking based comparisons were made. The performance of the approach depends on the underlying information object and information need representations used, which confi rms fi ndings of previous studies where the Principle of Polyrepresentation was applied in diff erent ways.

Promoting knowledge sharing has long been regarded as a very important aspect of the management of knowledge. However, knowledge sharing has its challenges due to the special nature of knowledge. Based on this, the researcher argues that it is knowledge seeking rather than knowledge sharing that plays a crucial role in knowledge management. However, there is no clear definition for knowledge seeking in existing literature. In the few studies of knowledge seeking research, knowledge has been viewed as a noun and as such knowledge seeking has been seen as no different to information seeking. The aim of this research has been to explore the knowledge seeking process in the workplace in order to conceptualise knowledge seeking by developing a theoretical model. A review of the literature concerning knowledge seeking has been conducted in order to clarify the concept of knowledge seeking. From the interpretivist’s perspective, a qualitative research approach has been taken, in which sense-making theory is employed as a methodological guide. Time-line interviews were carried out with construction engineers in China to collect primary data, and Template analysis was utilized. Based on the literature, this thesis defined knowledge seeking as a learning process, which consists of three major themes: experiential learning, information seeking and problem solving, based on which a preliminary framework was developed. Twenty six engineers were successfully interviewed. The findings from the data confirmed the links between the themes. Further codes were also identified to develop a final template, which evolved to a theoretical model illustrating the knowledge seeking process in the workplace. By promoting knowledge seeking rather than knowledge sharing, this research contributed innovatory insight into existing KM research. The new concept of knowledge seeking and the theoretical model developed thereafter contribute to knowledge by providing a theoretical framework for further research in this area. The specific combination of time-line interviews and template analysis has demonstrated good results in this research. Collecting primary data from China, this research applied Western theories onto engineers within a Chinese context, which has contributed to KM research in China. These contributions will result in many practical implications for KM practices.

The primary aim of this research is to investigate methods of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives. A foundational objective is to introduce a novel bidirectional, symmetrical fuzzy logic theory which may prove valuable to information retrieval, including internet searches of distributed data objects. A further objective is to design, implement and apply the novel theory to an experimental information retrieval system called ANACALYPSE, which automatically computes the relevance of a large number of unseen documents from expert relevance feedback on a small number of documents read. A further objective is to define a methodology used in this work as an experimental information retrieval framework consisting of multiple tables including various formulae which anow a plethora of syntheses of similarity functions, ternl weights, relative term frequencies, document weights, bidirectional relevance feedback and history adjusted term weights. The evaluation of bidirectional relevance feedback reveals a better correspondence between system ranking of documents and users' preferences than feedback free system ranking. The assessment of similarity functions reveals that the Cosine and Jaccard functions perform significantly better than the DotProduct and Overlap functions. The evaluation of history tracking of the documents visited from a root page reveals better system ranking of documents than tracking free information retrieval. The assessment of stemming reveals that system information retrieval performance remains unaffected, while stop word removal does not appear to be beneficial and can sometimes be harmful. The overall evaluation of the experimental information retrieval system in comparison to a leading edge commercial information retrieval system and also in comparison to the expert's golden standard of judged relevance according to established statistical correlation methods reveal enhanced system information retrieval effectiveness.