Ontology-Based Knowledge Capture and Sharing in Enterprise Organisations

Ontology-Based Knowledge Capture and Sharing in Enterprise Organisations

Aba-Sah Dadzie (University of Sheffield, UK), Victoria Uren (University of Sheffield, UK) and Fabio Ciravegna (University of Sheffield, UK)
DOI: 10.4018/978-1-60960-625-1.ch011
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Abstract

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.
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Introduction

Knowledge management (KM) in enterprise organisations involves the analysis of very large scale, distributed, multi- and cross-media data, in order to extract the information contained, and convert this to the knowledge required to support timely, effective decision-making. The X-Media project1 (Ciravegna & Staab, 2005) aimed to enhance KM in such environments by harnessing the power of Semantic Web (SW) technology. In this chapter we describe the use of SW technology to support the KM lifecycle, from the retrieval of existing knowledge and the generation of new knowledge, through its capture and storage, to manipulation in use and the resulting enrichment of the knowledge, and finally to sharing and reusing the rich knowledge gained.

The issue investigation scenario used to guide the presentation of this chapter involves teams with varied expertise, working independently and collaboratively to identify the causes of mechanical issues. Findings during the course of an investigation and the lessons learnt when it is concluded are shared with interested parties. Flexible methods for each step of the knowledge lifecycle are therefore necessary to satisfy the variations in requirements of different user communities, to support the multiple perspectives each brings to their normal activities (Wenger, 2004).

Ontologies provide a useful tool for formalising, enriching and disseminating knowledge. KM and analysis structured around SW technology starts with the modelling of domain ontologies to provide formal definitions of users, their environments and the knowledge-intensive activities they engage in. We employ a modular ontology design, so as to allow a clear distinction between the sub-parts of each domain, and to allow reuse of relevant public ontologies (such as OWL-Time2 to record event occurrence). Using the ontology, we formalise and capture the information end users interact with, and store it in shared semantic (knowledge) repositories. We also make use of ontology-based annotation to enrich knowledge capture. Further, we use the ontologies to guide exploratory knowledge discovery and analysis from the shared semantic knowledge repositories and other related sources of information (e.g., databases) and knowledge (e.g., as derived by human experts).

An evaluation of our approach with different groups of target end users in industrial settings confirmed the enhanced KM that results. Participants in the usability evaluations reported an increase in ability to retrieve information from distributed resources and collect this in a single workspace, allowing their analysis to be grounded in the context of relevant evidence. The participants especially valued the increased “intelligence” of the knowledge framework evaluated – the interpretation of the domain knowledge captured to the ontologies used to support KM and the analytical activity. Overall, the participants noted increased confidence in decision-making based on the output of the semantic, context-driven KM and analysis.

This chapter is structured as follows: we summarise the limitations of the traditional approach to KM in large, complex organisations, and the potential claimed by SW technologies for enhancing KM. We then discuss the tools and functionality available to support SW-based KM and analysis. We introduce the Issue Resolution process and a test case developed during X-Media to demonstrate the research carried out. This leads to a review of the state of the art, with a focus on visual solutions. We continue to describe the ontology which serves as the spine around which we structure the interactive construction of knowledge workspaces that support intuitive information retrieval and analytics. This leads to a detailed description of the ontology-based knowledge creation, use and enrichment enabled, throughout the different phases of the knowledge lifecycle. A brief description of the final evaluation of the integrated knowledge framework developed is followed by an examination of the challenges faced in semantic KM, and our approach to resolving these. We conclude with a brief look at future research directions and a summary of our contribution to enhanced semantic KM.

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