Ontology For Knowledge Management Research Articles

A Proposal for Basic Formal Ontology for Knowledge Management in Building Information Modeling Domain

The construction industry produces enormous amounts of information, relying on building information modeling (BIM). However, due to interoperability issues, valuable information is not being used properly. Ontology offers a solution to this interoperability. A complete knowledge base can be provided by reusing basic formal ontology (BFO). In previous studies, domain ontology was developed without BFO. Domain ontology requires loads of effort to reuse because domain ontology is too detailed. To increase the reuse rate and establish a complete knowledge base, it is necessary to develop BFO. This study has developed the BFO in the BIM domain to advance interoperability. First, unnecessary parts were omitted from the existing BFO development process, the process was simplified, and the base of hierarchy was created by extracting the most basic superclasses of the BFO model from Revit, the software of BIM. Based on that hierarchy, each child class was created, and the BFO model was completed by completing the relation of each class. After completion of the model, reliability, in addition to the completeness of the model, was evaluated through a query. Domain experts can reuse the BFO when defining relations between concepts and entities. The proposed BFO will be the foundation of future ontology developments in the BIM domain. This study facilitates future researchers to enhance interoperability in the BIM domain and make the ontology more complete to improve information sharing.

HBIM Modeling from the Surface Mesh and Its Extended Capability of Knowledge Representation

Built heritage has been documented by reality-based modeling for geometric description and by ontology for knowledge management. The current challenge still involves the extraction of geometric primitives and the establishment of their connection to heterogeneous knowledge. As a recently developed 3D information modeling environment, building information modeling (BIM) entails both graphical and non-graphical aspects of the entire building, which has been increasingly applied to heritage documentation and generates a new issue of heritage/historic BIM (HBIM). However, HBIM needs to additionally deal with the heterogeneity of geometric shape and semantic knowledge of the heritage object. This paper developed a new mesh-to-HBIM modeling workflow and an integrated BIM management system to connect HBIM elements and historical knowledge. Using the St-Pierre-le-Jeune Church, Strasbourg, France as a case study, this project employs Autodesk Revit as a BIM environment and Dynamo, a built-in visual programming tool of Revit, to extend the new HBIM functions. The mesh-to-HBIM process segments the surface mesh, thickens the triangle mesh to 3D volume, and transfers the primitives to BIM elements. The obtained HBIM is then converted to the ontology model to enrich the heterogeneous knowledge. Finally, HBIM geometric elements and ontology semantic knowledge is joined in a unified BIM environment. By extending the capability of the BIM platform, the HBIM modeling process can be conducted in a time-saving way, and the obtained HBIM is a semantic model with object-oriented knowledge.

Developing a domain ontology for knowledge management technologies

PurposeThe purpose of this paper is to develop a new ontology for knowledge management (KM) technologies, determining the relationships between these technologies and classification of them.Design/methodology/approachThe study applies NOY methodology – named after Natalya F. Noy who initiated this methodology. Protégé software and ontology web language are used for building the ontology. The presented ontology is evaluated with abbreviation and consistency criteria and knowledge retrieval of KM technologies by experts.FindingsAll the main concepts in the scope of KM technologies are extracted from existing literature. There are 241 words, 49 out of them are domain concepts, eight terms are about taxonomic and non-taxonomic relations, one term relates to data property and 183 terms are instances. These terms are used to develop KM technologies’ ontology based on three factors: facilitating KM processes, supporting KM strategies and the position of technology in the KM technology stage model. The presented ontology is created a common understanding in the field of KM technologies.Research limitations/implicationsLack of specific documentary about logic behind decision making and prioritizing criteria in choosing KM technologies.Practical implicationsUploading the presented ontology in the web environment provides a platform for knowledge sharing between experts from around the world. In addition, it helps to decide on the choice of KM technologies based on KM processes and KM strategy.Originality/valueAmong the many categories of KM technologies in literature, there is no classifying according to several criteria simultaneously. This paper contributes to filling this gap and considers KM processes, KM strategy and stages of growth for KM technologies simultaneously to choice the KM technologies and also there exists no formal ontology regarding KM technologies. This study has tried to propose a formal KM technologies’ ontology.

Development of terminological resources for expert knowledge: a case study in mining

In this paper we outline an approach to application of ontology in knowledge management. The University of Belgrade Faculty of Mining and Geology research team has developed a terminological resource to support knowledge management in mining engineering. Mining engineering, like all other engineering disciplines, needs comprehensive, consistent and standardized definitions of terms for efficient knowledge management and interoperability among various related IT applications. This goal can best be reached by terminological resources in electronic form organized as thesauruses or ontologies. The resource used to illustrate this approach, RudOnto, is a system of ontologies developed for mining engineering and their application in mining equipment and mine safety domains. Key benefits of applying ontologies in knowledge management, aside from securing interoperability, are enhancement of browsing/searching functions, and reuse and structuring capabilities. Through export to several specific formats, RudOnto ontologies offer the possibility of generating stand-alone terminological resources or ontologies in specific sub-fields, such as mining equipment, mine safety and geostatistics.

Deriving an ontology for knowledge management in collaborative innovation networks

Innovation and knowledge creation are tightly related concepts. Collaborative innovation networks (COINs) are very productive ecosystems for knowledge generation. Acknowledging their relevance for innovation, we propose a novel ontology for knowledge management (KM) in COINs, which builds on COINs related literature and is partially derived from topic-related and enterprise ontologies. The utility of the ontology is illustrated through an application case in a software house. The ontology can be applied in different cases involving KM in COINs, such as support the development of IT-based KM services for knowledge co-creation in COINs or, be used as an auditing tool in collaborative innovation processes.

Social Networking Ontology Engineering Walkthrough: Practical Approach for Non-Expert User Learning

The utilization of ontologies in knowledge management has become very well accepted in many approaches for instance database, semantics, agent, natural language processing, etc. Artificial intelligent researchers have originally applied the word "ontology" from Philosophy fields which means "being existence", then the word spread in many scientific domain and ontologies are now used in several developments. The purpose of this research is to present the simple methodologies and methods used to build/adapt/integrate ontologies from the system requirement or by reusing and re-engineering other ontologies; also a process of merging approach will be addressed. The normal ontology engineering will not be suitable for non-expert user; and there are some tools for helping the user to accomplish the ontology engineering task, however, it is not that simple. This paper mentioned the observation and figure out the practical methodology for non-expert user to learn and create simple ontology with little background of them. This paper mentioned the ontology and ontology engineering revisits. Also the main section in this paper mentioned the scenario walkthrough about reality of how non-expert user creates the simple ontology by using suggested and existing methodologies. The last section is recommendation and suggestion from the research experimental which explain the proper ontology developing methodology for non-expert user.

Ontology for knowledge management in software maintenance

The concept of ontologies has been widely reported in the literature, and has been described various ontological designs, but few works try to explain in practical terms how to design an ontology. On the other side, although it is widely accepted the importance of maintenance of software products, few ontological designs focus on applying techniques to manage knowledge to get it. This paper analyzes the ontologies proposed to address this need, in order to present information that can help to software development organizations in similar works. Besides, is described a methodological process to structure an ontology that can be applied to manage knowledge in software maintenance.

A formal ontology of knowing and knowledge

The paper presents a formalisation of one of the leading ontologies in Knowledge Management by the name of Formal Knowledge Management Ontology. After elaborating why the latter ontology is not a formal ontology in the sense of logic and how it might benefit from being logic-based, we detail a logical theory Formalised Formal Knowledge Management Ontology (F2KMO) that incorporates its main ideas and tenets. In particular, we specify the primitive, that is, undefined terms we assume, the axioms whose role is to fix the meanings of those terms, and a number of definitions that correspond to the definitions one may find in the latest exposition of Formal Knowledge Management Ontology. We show that F2KMO is consistent and prove its usefulness by defining the logical schema of a database based on the theory.

Apply fuzzy ontology and FML to knowledge extraction for university governance and management

The Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT) supported by the Taiwan government has annually executed universities’ assessment process similar to the one in the United States since 2005. The HEEACT holds numerous assessment workshops and develops many basic standards to encourage each university to set its educational goals, measure the success in achieving these goals, ask some tough questions for the university, and meet these questions’ requirements to continuous improvement in the university. In this paper, we apply the fuzzy ontology and fuzzy markup language (FML) to perform the knowledge extraction for university governance and management, and adopt National University of Tainan (NUTN), Taiwan, as an example to construct the FML-based fuzzy ontology for knowledge management. In addition, the FML-based knowledge management system which processes a simulation for NUTN self-assessment and forecasts whether NUTN passes the 2011 HEEACT university assessment or not is also presented in this paper. Finally, NUTN fully passed 2011 university assessment in Taiwan and the results of our simulations are similar to the opinions of the HEEACT reviewers. It shows that the proposed fuzzy ontology and FML-based knowledge management system can work effectively for university governance and management.

Reducing errors in the development, maintenance and utilisation of ontologies

Ontologies and ontology-based information systems are becoming more commonplace in knowledge management. For engineering applications such as product design, ontologies can be utilised for knowledge capture/reuse and frameworks that allow for the integration and collaboration of a wide variety of tools and methods as well as participants in design (marketing/sales, engineers, customers, suppliers, distributors, manufacturing, etc.) who may be distributed globally across time, location, and culture. With this growth in the use of ontologies, it is critical to recognise and address errors that may occur in their representation, maintenance and utilisation. Passing undetected and unresolved errors downstream can cause error avalanche and could diminish the acceptance, further development and promise of significant impact that ontologies hold for product design, manufacturing, or any knowledge management environment within an organisation. This paper categorises errors and their causal factors, summarises possible solutions in ontology and ontology-based utilisation, and puts forward an ontology-based Root Cause Analysis (RCA) method to help find the root cause of errors. Error identification and collection methods are described first, followed by an error taxonomy with associated causal factors. Finally, an error ontology and associated SWRL (Semantic Web Rule Language) rules are built to facilitate the error taxonomy, the root cause analysis and solution analysis for these errors. Ultimately, this work should reduce errors in the development, maintenance and utilisation of ontologies and facilitate further development and use of ontologies in knowledge management.

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed.

Knowledge Chain Activity Classes

Just as Porter’s value chain model identifies classes of business activity that can be performed in ways that contribute to a firm’s competitiveness, the knowledge chain model contends there are classes of knowledge management (KM) activity that can be performed in ways that enhance firm competitiveness. These KM activities pervade the value chain, being inherent in the implementation of each value chain activity. Derived from a collaboratively engineered ontology of knowledge management, the knowledge chain model is supported by anecdotal evidence and a survey has found support for the propositions that its activity classes are linked to enhanced productivity, agility, innovation, and reputation. Here, we present a study of leaders of KM initiatives that examines each of the nine knowledge chain classes in terms of its competitive impact and the extent to which its positive impact on competitiveness is associated with the importance of technology in performing activities within that class. The study provides confirming evidence that each of the knowledge chain activity classes can be performed in ways that contribute to competitiveness. Moreover, we find that for five of the activity classes there is a significant positive correlation between impact on competitiveness and the importance of computer-based technology in implementing the class’s activities.

DESIGN INFORMATION HANDLING IN A KNOWLEDGE BASED INTELLIGENT DESIGN SYSTEM

The advancements in internet technology have had a tremendous impact on the development of knowledge-based engineering systems that support concurrent engineering. Not only the conventional knowledge acquisition and representation techniques need to be improved to adapt the new situation, but the information retrieval and distribution have arisen to be new questions to answer. This article addresses these issues of management and handling of knowledge in a knowledge-based intelligent design system. A search algorithm based on the computation of a similarity index is proposed to retrieve a design case from the project library. An initial design report is used to distribute design information so that the information such as a designer's intent which cannot be included in a standard CAD file, can be retained. In addition, a simple constraint definition frame is presented to define the relationships between critical design parameters, and two information representation schemas, information matrix and constraint tree, are described. The presented research efforts in this article aim to provide promising tools to harness the full potential of ontologies in knowledge management within an intelligent design system.

Building and using a medical ontology for knowledge management and cooperative work in a health care network

In the context of a health care network, we describe our method for reconstitution of a medical ontology via the translation of a medical database (DB) towards RDF(S) language. Then we show how we extended this ontology among others through natural language processing of a textual corpus. Then, we present the construction of a tool called “Virtual Staff”, enabling a cooperative diagnosis by some of the health care network actors, by relying on this medical ontology and on the creation of SOAP and QOC graphs.

5.2.1 Architecting Ontological Systems

AbstractRecent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and the volume of data accessible to information management systems. In this new world, there is less emphasis on isolated devices and greater emphasis on information exchange. This shift is making knowledge management and application integration key issues in computer technology. In order to stay competitive, a company is held hostage by how it understands, maintains and accesses its data. Data comes from many sources, containing a variety of types and formats. One possible way to tackle this complex data problem is to implement an ontology, a unifying framework that facilitates communication through a shared understanding and vocabulary of a system. It enables communication between systems that are independent of system technologies, information architectures and application domain. This paper describes the importance of ontologies in knowledge management and provides the basis on how to architect such an ontological system.

Integrating descriptions of knowledge management learning activities into large ontological structures: A case study

Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management (KM) automation, and the conceptual foundations for them have been discussed in some previous reports. Nonetheless, such conceptual structures should be properly integrated into existing ontological bases, for the practical purpose of providing the required support for the development of intelligent applications. Such applications should ideally integrate KM concepts into a framework of commonsense knowledge with clear computational semantics. In this paper, such an integration work is illustrated through a concrete case study, using the large OpenCyc knowledge base. Concretely, the main elements of the Holsapple and Joshi KM ontology and some existing work on e-learning ontologies are explicitly linked to OpenCyc definitions, providing a framework for the development of functionalities that use the built-in reasoning services of OpenCyc in KM activities. The integration can be used as the point of departure for the engineering of KM-oriented systems that account for a shared understanding of the discipline and rely on public semantics provided by one of the largest open knowledge bases available.

Higher education leadership roles in knowledge processing

PurposeTo examine the critical roles of leadership in higher education and to define how such roles enable an environment that embraces the demands of a knowledge‐based organization.Design/methodology/approachThrough interviews with executive leadership in several higher education institutions, the ontology of knowledge management was established and the methods by which knowledge gaps were resolved were explored. The question of how leadership behaviors, policies, and programs enable or inhibit knowledge‐processing activities was analyzed.FindingsLeadership influence in six key areas emerged as significant in the study: environment manager, network manager, policy manager, crisis manager, knowledge gap manager, and future leader preparation. The authors confirmed that the leader has tremendous control over the knowledge‐processing environment and the role of leadership has broader influence than the resolution of knowledge gaps.Originality/valueBy understanding leadership roles in knowledge management, organizations and their leaders can enhance the learning capacity of the organization.

Exploring secondary activities of the knowledge chain

AbstractResearchers, marketers, and pundits often assert that knowledge management (KM) is an important, or even essential, basis for competitiveness. The Knowledge Chain Model has been advanced as a basis for understanding linkages between KM activities and organizational performance. Based on a general ontology of KM, this model identifies five primary and four secondary activities as focal points for enhancing an organization's competitive position. Both anecdotes and survey results support the model. A prior paper explored the primary classes of activity found in the Knowledge Chain Model, finding that each is comprised of distinct subclasse's that are candidates for enhancing organizational performance. Here, we similarly explore the model's secondary KM activity classes to uncover the subclasses of which they are comprised, thereby arriving at a more detailed understanding of their potential for launching competitive initiatives. A literature review yielded over 300 variations of the four secondary activities. We consolidate these into 29 distinct activity types and organize them into the four secondary activity classes. A typology of activity types is developed for each class. The result is a more in‐depth version of the Knowledge Chain Model that gives greater guidance to practitioners in assessing current KM initiatives or striving to incorporate KM into their organizations and their competitive strategies. Together with the earlier primary activity class paper, a total of 61 distinct activity types have been identified for the Knowledge Chain Model. Copyright © 2005 John Wiley & Sons, Ltd.

Exploring primary activities of the knowledge chain

AbstractIt is often claimed that knowledge management (KM) is important, or even essential, as a basis for competitiveness. The knowledge chain model was recently advanced as a first step in understanding the linkage between KM and organizational performance. Based on a general ontology of KM, this model identifies five primary and four secondary activities as focal points for enhancing competitiveness. Both anecdotes and survey results support the model. Here we further explore the primary KM activities found in the knowledge chain model by treating each as a class involving several distinct types of activity. A literature review yielded over 200 variations of the five primary activities. We consolidate these into 32 distinct activity types and organize them into the five primary activity classes. Within each class a typology of activity types is developed. The result is a more in‐depth version of the knowledge chain model. It gives greater guidance to practitioners striving to incorporate KM into their organizations and their competitive strategies. It also suggests avenues for future research. Copyright © 2004 John Wiley & Sons, Ltd.

A formal knowledge management ontology: Conduct, activities, resources, and influences

AbstractThis article describes a collaboratively engineered general‐purpose knowledge management (KM) ontology that can be used by practitioners, researchers, and educators. The ontology is formally characterized in terms of nearly one hundred definitions and axioms that evolved from a Delphi‐like process involving a diverse panel of over 30 KM practitioners and researchers. The ontology identifies and relates knowledge manipulation activities that an entity (e.g., an organization) can perform to operate on knowledge resources. It introduces a taxonomy for these resources, which indicates classes of knowledge that may be stored, embedded, and/or represented in an entity. It recognizes factors that influence the conduct of KM both within and across KM episodes. The Delphi panelists judge the ontology favorably overall: its ability to unify KM concepts, its comprehensiveness, and utility. Moreover, various implications of the ontology for the KM field are examined as indicators of its utility for practitioners, educators, and researchers.