Relation discovery from web data for competency management

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

Transferring knowledge of manufacturing techniques in multinational corporations:preliminary findings

With the growth of the multinational corporation (MNC) has come the need to understand how parent companies transfer knowledge to, and manage the operations of, their subsidiaries. This is of particular interest to manufacturing companies transferring their operations overseas. Japanese companies in particular have been pioneering in this regard, with techniques such as the Toyota Production System (TPS) for transferring the ethos of Japanese manufacturing and maintaining quality and control in overseas subsidiaries. A great deal has been written about the process of transferring Japanese manufacturing techniques, but much less is understood about how the subsidiaries themselves, which are required to make use of such techniques, actually acquire and incorporate them into their operations. The research on which this paper is based therefore examines how, from the perspective of the subsidiary, knowledge of manufacturing techniques is transferred from the parent company. There is clearly a need to take a practice-based view to understanding how the local managers and operatives incorporate knowledge about manufacturing techniques into their working practices. In-depth qualitative research was, therefore, conducted in the subsidiary of a Japanese multinational, Denso Corporation, involving three main manufacturing initiatives (or philosophies), namely ‘TPS’, ‘TPM’ and ‘TS’. The case data were derived from 52 in-depth interviews with project members, moderate participant observations, and documentations. The aim of this paper is to present the preliminary findings from the case analyses. The research contributes to our understanding of knowledge transfer in relation to the circumstances of the selection between adaptation and replication of knowledge in the subsidiary from its parent. In particular this understanding relates to transfer across different flows and levels in the organisational hierarchy, how the whole process is managed, and also how modification takes place.

Utilising Intranet technologies in construction sector SMEs:building up knowledge bases for extreme weather event risk management

Intranet technologies accessible through a web based platform are used to share and build knowledge bases in many industries. Previous research suggests that intranets are capable of providing a useful means to share, collaborate and transact information within an organization. To compete and survive successfully, business organisations are required to effectively manage various risks affecting their businesses. In the construction industry too this is increasingly becoming an important element in business planning. The ability of businesses, especially of SMEs which represent a significant portion in most economies, to manage various risks is often hindered by fragmented knowledge across a large number of businesses. As a solution, this paper argues that Intranet technologies can be used as an effective means of building and sharing knowledge and building up effective knowledge bases for risk management in SMEs, by specifically considering the risks of extreme weather events. The paper discusses and evaluates relevant literature in this regard and identifies the potential for further research to explore this concept.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

Knowledge acquisition and research evidence in autism:researcher and practitioner perspectives and engagement

Background: Government policy and national practice guidelines have created an increasing need for autism services to adopt an evidence-based practice approach. However, a gap continues to exist between research evidence and its application. This study investigated the difference between autism researchers and practitioners in their methods of acquiring knowledge. Methods: In a questionnaire study, 261 practitioners and 422 researchers reported on the methods they use and perceive to be beneficial for increasing research access and knowledge. They also reported on their level of engagement with members of the other professional community. Results: Researchers and practitioners reported different methods used to access information. Each group, however, had similar overall priorities regarding access to research information. While researchers endorsed the use of academic journals significantly more often than practitioners, both groups included academic journals in their top three choices. The groups differed in the levels of engagement they reported; researchers indicated they were more engaged with practitioners than vice versa. Conclusions: Comparison of researcher and practitioner preferences led to several recommendations to improve knowledge sharing and translation, including enhancing access to original research publications, facilitating informal networking opportunities and the development of proposals for the inclusion of practitioners throughout the research process.

Knowledge maintenance and the frame problem

Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.

Techniques for automated taxonomy building: towards ontologies for knowledge management

Ontologies have become widely accepted as the main method for representing knowledge in Knowledge Management (KM) applica-tions. Given the continuous and rapid change and dynamic nature of knowledge in all fields, automated methods for construct-ing ontologies are of great importance. All ontologies or taxonomies currently in use have been hand built and require consider-able manpower to keep up to date. Taxono-mies are less logically rigorous than ontolo-gies, and in this paper we consider the re-quirements for a system which automatically constructed taxonomies. There are a number of potentially useful methods for construct-ing hierarchically organised concepts from a collection of texts and there are a number of automatic methods which permit one to as-sociate one word with another. The impor-tant issue for the successful development of this research area is to identify techniques for labelling the relation between two candi-date terms, if one exists. We consider a number of possible approaches and argue that the majority are unsuitable for our re-quirements.

User-centred ontology learning for knowledge management

Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Bridging the gap from the general to the specific by linking knowledge management to business process

A phenomenon common to almost all fields is that there is a gap between theory and practical implementation. However, this is a particular problem in knowledge management, where much of the literature consists of general principles written in the context of a ‘knowledge world’ that has few, if any, references to how to carry out knowledge management in organisations. In this chapter, we put forward the view that the best way to bridge this gap between general principles and the specific issues facing a given organisation is to link knowledge management to the organisation’s business processes. After briefly reviewing, and rejecting alternative ways in which this gap might be bridged, the chapter goes on to explain the justification for, and the potential benefits and snags of, linking knowledge management to business processes. Successful and unsuccessful examples are presented. We concentrate especially on the issues of establishing what knowledge is relevant to an organisation at present, the need for organisational learning to cope with the inevitable change, and the additional problems posed by the growing internationalisation of operations. We conclude that linking knowledge management in terms of business processes is the best route for organisations to follow, but that it is not the answer to all knowledge management problems, especially where different cultures and/or cultural change are involved.

Managing software engineers and their knowledge

This chapter begins by reviewing the history of software engineering as a profession, especially the so-called software crisis and responses to it, to help focus on what it is that software engineers do. This leads into a discussion of the areas in software engineering that are problematic as a basis for considering knowledge management issues. Some of the previous work on knowledge management in software engineering is then examined, much of it not actually going under a knowledge management title, but rather “learning” or “expertise”. The chapter goes on to consider the potential for knowledge management in software engineering and the different types of knowledge management solutions and strategies that might be adopted, and it touches on the crucial importance of cultural issues. It concludes with a list of challenges that knowledge management in software engineering needs to address.

Group perceptions of knowledge management

This paper reports preliminary results of a project investigating how staff in UK organisations perceive knowledge management in their organisation as a group. The group setting appears to be effective in surfacing opinions and enabling progress in both understanding and action to be made. Among the findings thus far are the importance of the knowledge champion role and the state of the “knowledge management life cycle” in each organisation, and continuing confusion between knowledge, information and mechanisms.

Using a simulation model for knowledge elicitation and knowledge management

The work reported in this paper is part of a project simulating maintenance operations in an automotive engine production facility. The decisions made by the people in charge of these operations form a crucial element of this simulation. Eliciting this knowledge is problematic. One approach is to use the simulation model as part of the knowledge elicitation process. This paper reports on the experience so far with using a simulation model to support knowledge management in this way. Issues are discussed regarding the data available, the use of the model, and the elicitation process itself. © 2004 Elsevier B.V. All rights reserved.

Knowledge management sans frontières

Knowledge management is a topic that crosses borders of various kinds, such as those between departments, between organisations or between countries. In this paper we will consider various issues relating to knowledge management, in the context where more than one department/organisation/country is involved. To do this, we place an emphasis on knowledge management as a process, rather than as an organisational system or, worse, as a piece of technology. This process involves trust, negotiation—and indeed some technological support. In this paper we wish to introduce the concept of ‘triangles of trust’, and to focus on where ‘the top meets the bottom’ in terms of knowledge management and organisational learning. Partial examples will be offered in support of our views, but no full and complete examples—knowledge management simply is not well enough understood or documented for that yet. Our overall conclusion is that there is no one best way to “do” knowledge management, but there are principles that ought to be applied.