On the Relationship between Ontology-Based and Holistic Representations in a Knowledge Management System

This chapter presents a way for systematically combining ontology-based and holistic-based content descriptions in the context of knowledge management in order to increase recall while maintaining precision.

Managing Lessons Learned

An organization’s knowledge is built on the experience of its human resources, such as individual competencies, group working experiences, problem-solving abilities, and the lessons learned, which these resources use during the execution of tasks or processes. This organizational knowledge is stored in individual minds, or implicitly encoded in organizational processes, services, and systems. In the context of knowledge intensive organizations or learning organizations, besides labor, capital, and land, this experience-based knowledge has been recognized as an important productivity factor. For example, Lessons Learned (LL) are crucial to the learning organization’s continued success. LL were originally conceived as guidelines, tips, or checklists of what went right or wrong in a particular event or organizational business process. A Lessons Learned System (LLS) is a type of knowledge management system that serves the purpose of capturing, storing, disseminating, and sharing an organization’s verified LL, but Lessons Learned Systems have not been well studied and researched. Based on the literature review, the authors develop an evaluation framework for understanding LLS research. The framework identifies six dimension areas of emphasis in LLS research: LL process, LL representation, LL content, LL architecture, organization type, and type of processes. This chapter introduces the evaluation framework and concludes with a discussion of emerging issues, new research directions, and the practical implications of LLS research.

SEMblog

Information Technology (IT) is often employed to put knowledge management policies into operation. However, many of these tools require human intervention when it comes to deciding how the knowledge is to be managed. The Sematic Web may be an answer to this issue, but many Sematic Web tools are not readily available for the regular IT user. Another problem that arises is that typical efforts to apply or reuse knowledge via a search mechanism do not necessarily link to other pages that are relevant. Blogging systems appear to address some of these challenges but the browsing experience can be further enhanced by providing links to other relevant posts. In this chapter, the authors present a semantic blogging tool called SEMblog to identify, organize, and reuse knowledge based on the Sematic Web and ontologies. The SEMblog methodology brings together technologies such as Natural Language Processing (NLP), Sematic Web representations, and the ubiquity of the blogging environment to produce a more intuitive way to manage knowledge, especially in the areas of knowledge identification, organization, and reuse. Based on detailed comparisons with other similar systems, the uniqueness of SEMblog lies in its ability to automatically generate keywords and semantic links.

Ontologies in Expertise Finding Systems

Knowledge Management Systems that enhance and facilitate the process of finding the right expert in an organization have gained much attention in recent years. This chapter explores the potential benefits and challenges of using ontologies for improving existing systems. A modeling technique from requirements engineering is used to evaluate the proposed system and analyze the impact it would have on the goals of the stakeholders. Based on the analysis, an ontology-based expertise finding system is proposed. This chapter also discusses the organizational settings required for the successful deployment of the system in practice.

Representation of Action is a Primary Requirement in Ontologies for Interoperating Information Systems

Ontologies at the present time are generally rich data models. The interoperating information system engineering paradigm Service-Oriented Architecture recognizes that the key issue in interoperating information systems is the actions performed by these systems, not so much the data. Further, the organizationally heterogeneous nature of these interoperating systems means that the individual object is difficult to characterize by classes. This chapter investigates the problems raised by giving priority in ontology representation to individuals and actions over classes, outlining a number of significant research questions in representation languages for ontologies.

Supporting Domain Ontology through a Metamodel

A metamodel is a model that has the ability to create the languages of many domain models. Domain models are conceptual models of a domain under study and contain all the entities, attributes, relationships, and constraints of the domain. As the artifact of a metamodeling technique, a metamodel could generalize most of the concepts used in existing domain models by unifying the views and structuring the language of the domain. In relation to ontology, the creation of a metamodel could assist in understanding, structuring, and analyzing the ontology. Other than its potential to engineer new ontology and re-engineer existing ontology, a metamodel can also be used to facilitate communication among communities regarding the ontology. The authors present how a metamodel can structure and manage knowledge of a domain it models. Through the Disaster Management Metamodel, they create a language for the disaster management domain.

Establishing and Verifying a Risk Ontology for Surfacing ERP Post-Implementation Risks

This chapter aims to establish and verify a risk ontology for the post-implementation of Enterprise Resource Planning (ERP) systems. The risk ontology was initially constructed through the process of a critical literature review. It consists of 40 ERP exploitation risks related to diverse operational, analytical, organization-wide, and technical aspects. This theoretical risk ontology can be used as the basis for identifying and assessing ERP post-implementation risks within different organizational and national contexts. In order to illustrate the discussion, a previous ERP study in China is presented as an example. The study adopted a mixed-methods design, which involved a questionnaire survey and a follow-up case study. The questionnaire examined the suitability of the established risk ontology in the context of Chinese State-Owned Enterprises (SOEs). The follow-up case study then further explored and validated the questionnaire findings. By integrating the quantitative and qualitative findings, the original ontology was verified, revised, and extended.

Knowledge Management Processes in Enterprise Systems

Activities related to Enterprise Systems (ES) are knowledge-intensive tasks, and the management of ES-related knowledge has received much attention in the Knowledge Management (KM) field. A systematic literature review of empirical studies of KM processes in the ES lifecycle identifies the KM processes most widely explored and the ES-related knowledge most often addressed. From 350 relevant book chapters, journal articles, and conference papers, 49 papers discuss KM processes in the ES lifecycle. The KM process that appears most often in studies of KM in the ES context is knowledge transfer/sharing. The type of ES-related knowledge most often studied in the literature is knowledge of the client organization.

A Study of Ontology Construction

Ontology has been recognized, and prominently used, as tool to facilitate shared understanding (and knowledge sharing) in a particular domain. Ensuring that such an ontology is relevant to a particular domain, however, remains a challenging task to the ontology developer. Motivated by the lack of consistent holistic guidelines to assist development of ontologies that are industry-relevant, the goal of this chapter is to present such an approach. The presented approach is based on the synthesis of existing approaches and varied sources of academic and industry input. The approach follows a typical ontology development cycle and consists of incremental steps that need to be taken to assure industry-relevance of the ontology. To provide a thorough discussion of the approach, the authors utilize a previously completed ontology development project that followed the developed approach. The project was specifically aimed at developing an industry-relevant ontology for the compliance management domain and was based on three main inputs, namely, scholarly articles, industry expert/practitioner input and industry reports. Their experience indicates that the use of the ontology development approach promotes an ontology that is closely aligned with the needs of industry.

Ontology-Based Service Description, Discovery, and Matching in Distributed Embedded Real-Time Systems

The service composition process depends on effective service discovery, matching, and selection. Although, service discovery and matching has been thoroughly researched for software services, an effective service discovery and matching mechanism is needed for services representing device functionality. In this chapter, an ontology-based service description method and service discovery and matching mechanism is proposed for Distributed Embedded Real-Time Systems (DERTS). For service description, the existing ontology specification OWL-S is modified to make it suitable for DERTS. The proposed mechanism was applied to a scenario of Smart Home case study for testing. The proposed service description method and discovery and matching mechanism set foundations can be used for the dynamic service composition in DERTS and in any ubiquitous environment.