Ontology-based knowledge capture and sharing in enterprise organisations

Despite years of effort in building organisational taxonomies, the potential of ontologies to support knowledge management in complex technical domains is under-exploited. The authors of this chapter present an approach to using rich domain ontologies to support sense-making tasks associated with resolving mechanical issues. Using Semantic Web technologies, the authors have built a framework and a suite of tools which support the whole semantic knowledge lifecycle. These are presented by describing the process of issue resolution for a simulated investigation concerning failure of bicycle brakes. Foci of the work have included ensuring that semantic tasks fit in with users’ everyday tasks, to achieve user acceptability and support the flexibility required by communities of practice with differing local sub-domains, tasks, and terminology.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

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.

Postsocial relations:towards a performative view of accounting knowledge

This paper discusses the impact and influences of the growth of postsocial relations on accounting practice. Aspects of the growth of knowledge cultures, which have been argued to impact social and organizational arrangements, are discussed. Extending this view to accounting, we see accountants forming a distinctive knowledge culture with their own unique rules of how knowledge is constituted. These rules are embedded in accounting systems and practices. This paper suggests the need to further develop a research program that seeks to investigate accounting practice in local settings. The discussion in the paper is based on views which posit the growth of intimate links with epistemic objects within organizations and society. This paper argues that such ideas lead to an increasing tendency for us to experience the changes in societal relations and social arrangements as a compression of time and space. The paper relates these ideas to developments in the accounting research literature.

New technology, industrial relations and white collar trade unions : the case of the National and Local Government Officers Association

The decade since 1979 has seen the most rapid introduction of microelectronic technology in the workplace. In particular, the scope offered for the application of this new technology to the area of white collar work has meant that it is a sector where trade unions have been confronted with major challenges. However the application of this technology has also provided trade unions with opportunities for exerting influence to reshape traditional attitudes to both industrial relations and the nature of work. Recent academic research on the trade union response to the introduction of new technology at the workplace suggests that, despite the resources and apparent sophistication of modern trade unions, they have not in general been able to take advantage of the opportunities offered during this period of radical technological change,the argument being that this is due both to structural weaknesses and the inappropriateness of the system of collective bargaining where new technology issues are concerned. Despite the significance of the Public Sector in employment terms, research into the response of public sector white collar trade unions to technological change has been fairly limited. This thesis sets out the approach of the National and Local Government Officers Association (NALGO), the largest solely white collar union in the world with over three quarters of a million members employed in a wide range of public service industries. The thesis examines NALGO's response at national level and, through detailed case studies, at local level in respect of Local Government and Water Industry NALGO members. The response is then evaluated and conclusions drawn in terms of a framework based upon an assessment of the key factors relevant in judging the ability of NALGO to respond effectively to the challenges brought about by the technological revolution of the last ten years.

An integrated environment for semantic knowledge work

In this demonstration, we will present a semantic environment called the K-Box. The K-Box supports the lightweight integration of knowledge tools, with a focus on semantic tools, but with the flexibility to integrate natural language and conventional tools. We discuss the implementation of the framework, and two existing applications, including details of a new application for developers of semantic workflows. The demonstration will be of interest to developers and researchers of ontology-based knowledge management systems, and semantic desktops, and to analysts working with cross-media information. © 2011 ACM.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.