Search results

The aim of the paper is to propose a methodology that allows researchers and practitioners to structure a small amount of data in a way which aids understandings and allows predictions to be made.

The paper explores how formal concept analysis can be combined with fuzzy reasoning to make predictions based on small datasets. A dataset of nine regions in Norway described by six attributes is used. The paper focuses on regional disparities in labour market outcomes such as unemployment and wages.

The paper finds that unemployment tends to be concentrated in the most prosperous parts of the study area. These regions have high incomes and experience population growth. More rural regions have virtually no unemployment. The methodology proposed allows these patterns to be seen. The authors made predictions with an accuracy rate of over 75 per cent.

A common response to high unemployment in urban areas is to stimulate employment growth. The findings suggest that this will simply increase migration towards the cities. The net result will be no change in unemployment but an accelerated depopulation of more rural regions.

To the authors' knowledge, this is the first application of fuzzy reasoning to the topic of regional disparities. The methodology aids in the interpretation of small datasets. The methodology should be of interested to practitioners at the local level, who are interested in analysing their own region, even when limited data are available.

The purpose of this paper is to explore the basics of semiotic analysis and concept theory that represent two dominant approaches to knowledge representation, and explore how these approaches are fruitful for knowledge organization.

In particular the semiotic theory formulated by the American philosopher C.S. Peirce and the concept theory formulated by Ingetraut Dahlberg are investigated. The paper compares the differences and similarities between these two theories of knowledge representation.

The semiotic model is a general and unrestricted model of signs and Dahlberg's model is thought from the perspective and demand of better knowledge organization system (KOS) development. It is found that Dahlberg's concept model provides a detailed method for analyzing and representing concepts in a KOS, where semiotics provides the philosophical context for representation.

This paper is the first to combine theories of knowledge representation, semiotic and concept theory, within the context of knowledge organization.

The purpose of this paper is to apply a mathematical method of formal concept analysis (FCA) to facilitate evaluation of potential partners, and to select the most appropriate partner for horizontal strategic alliances. Horizontal collaboration between ship design firms is important in relation to business cyclicality in the industry. The workload in ship design firms drops during the troughs of the shipbuilding cycle and increases dramatically during the peaks of the cycle.

The proposed method of partnership selection applies FCA, which is based on mathematical lattice theory. FCA allows firms to evaluate and select the best suitable partners for horizontal interfirm cooperation from several possible candidate firms. Utilization of FCA allows a firm to visually analyze a potential partner for a horizontal strategic alliance.

The contribution of this study to the literature is twofold. First, it contributes to the literature on the application of FCA in management field. Second, this study contributes to the partner selection literature. The contribution of the study is an alternative quantitative method for partner selection based on FCA. FCA compliments qualitative approaches in the process of alternatives evaluation and decision‐making regarding partner selection for horizontal collaboration.

Practitioners from ship design firms can use the FCA tool to facilitate decision‐making relating to the screening of potential partners for horizontal cooperation with regard to pre‐specified selected criteria.

FCA has been marginally applied to aid managerial decision making. The FCA tool is valuable for practitioners from ship design firms to manage the selection of partners for horizontal collaboration. The FCA tool is associated with numerous advantages, notably, relative simplicity and versatility of visual analysis when compared with other mathematical approaches such as the analytic hierarchy process, the analytic network process, optimization modeling, and fuzzy set logic.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

The aim of this paper is threefold: first, to examine the content of supply chain quality management (SCQM); second, to identify the structure of SCQM; and third, to show ways for finding improvement opportunities and organizing individual institution's resources/actions into collective performance outcomes.

To meet the goals of this work, the paper uses abductive reasoning and two qualitative methods: content analysis and formal concept analysis (FCA). Primary data were collected from both original design manufacturers (ODMs) and original equipment manufacturers (OEMs) in Taiwan.

According to the qualitative empirical study, modern enterprises need to pay immediate attention to the following two pathways: a compliance approach and a voluntary approach. For the former, three strategic content variables are identified: training programs, ISO, and supplier quality audit programs. As for initiating a voluntary effort, modern lead firms need to instill “motivation” into a supply chain quality system.

The findings based on the abductive model reveal numerous strategic and tactical enablers, key sequences to move firms from their current situation to their preferred one, and critical opportunities for supply chain‐wide quality system designs.

This study will be of great value to supply chain policy makers, supply chain operators, and decision makers in lead firms in a supply chain setting and their channel partners. The proactive use of the authors' proposed research procedure is indispensable to effective supply chain quality planning.

This paper presents a new solution for conceptually modeling the training‐domain knowledge of intelligent computerassisted instructional (ICAI) systems. Various cognitive demands from learners impose various modalities of presenting and structuring the knowledge to be taught. This paper is presenting a theoretical framework allowing to develop coherent, flexible models for the training‐domain knowledge of an ICAI system. A case study and an authoring‐system are also presented, in order to describe the main features of the modeling methods. This new approach in ICAI systems’ domain‐knowledge structuring and representation can offer a solution to the problem of adapting the instructional system interaction to users with different learning styles and needs.

Business environments and global transportation system have become more complex than ever due to complexity drivers of industries which create uncertainty and unpredictability to organizations. Like other industries, the maritime business faces different and difficult problems which threaten organizational survival. The ability to cope with those uncertainties, threats and problems shows the resilience ability of organizations that help to survive and prosper. The organizational resilience concept arises as a requirement to deal with problems and uncertainties of business environments which are swiftly changing. This study aims to suggest an organizational framework to show how maritime business organizations as the sea leg of global transportation system can develop resilient organizations via complex adaptive systems (CAS) approach if adequate design features of CAS could be defined and included in organizational properties.

A total of 15 CAS features were identified as the enablers of organizational resilience throughout the literature. An interpretive structural modeling (ISM) approach has been conducted to determine the mutual relation between the CAS features which constitute an organizational framework. These CAS features have been categorized by conducting MICMAC analysis.

This study proposes a framework that identifies CAS features as the enabler of resilient maritime business organizations. The CAS approach offers new managerial toolkit to realize current organizational situations and allows managers to understand that it is difficult to control their system in this dynamic environment where special management practices are required especially in volatile times rather than ordinary times. Also, organizations could not compete as a sole organization but as a web/system of organizations. CAS is more resilient than other systems because resilience is the emergent occurrence of the system formed from nonlinear, dynamic interactions with self-organized agents.

The research has some limitations, like organizational resilience studies are in the infant stage and further research into this area should be extended. This study uses the CAS approach to develop organizational resilience. Further studies could use different lenses and contemporary subjects in management field which should also be useful while developing resilience in organizations. This study uses ISM and MICMAC analysis where further studies could use quantitative design and methods like formal concept analysis or the decision making trial and evaluation laboratory to determine the relational weighs of CAS features while developing resilient organizations. Future studies may also focus on different maritime stakeholders like IMO or ILO, maritime agencies, freight forwarders or insurance underwriters regarding developing and enhancing resilience of the maritime system.

World trade and transportation systems are getting more uncertain and lean on complex relations where maritime transportation is a “vital backbone” of such operations. But becoming more complex structures leads to vulnerable systems and organizations. Most risk management applications are based on predicting the known risks where many of them are not enough to fight with unknowns. Coping with today's problems are difficult for organizations in any industry. But for maritime business stakeholders who work in such a global web of relations, it is much more challenging. So, stakeholders of the system like forwarders, ports or ship chandlers may easily apply those features to develop resilient organizations too. Legal authorities of the system and rule-makers like local Chambers of Shipping, IMO or Classification societies can benefit from this framework and provide supportive settings to develop system-wide resilient organizations.

By understanding environmental uncertainty and complexity better than others, organizations become resilient and cope with significant difficulties which make them more competitive as a substantial strategic advantage. Resilient management offers to break down points at the system and shows them ways to restore quickly while transporting goods while traditional risk assessments are not enough.

The originality of the study lies in two folds; first of all the key and most used features of CAS is linked to developing resilient maritime organizations and by maritime expert opinions, this study tries to determine which of these CAS features are the most effective to trigger other features to develop organizational resilience in the maritime business. And secondly, the concept of organizational resilience and the CAS approach are not analyzed in depth in the context of maritime business.

This study attempts to develop an approach with triangulation to explore the determinants of knowledge flows, as well as the relationships among them.

The proposed approach with the use of triangulation is designed to handle the issues that arise in the various interactions and numerous contexts among entities and/or activities that are associated with KM.

The proposed hybrid approach is found to be helpful in dealing with the issue of knowledge flow, especially involving various entities and complex interactions with numerous contents and dynamic contexts. In addition, the findings also show the presence of multi‐directional and adaptive interactions among determinants to knowledge flows, and also how these influence a firm's KM initiatives and performance.

The proposed hybrid approach can be considered as a way to overcome the limitations both of qualitative and quantitative methods, and the relationships among knowledge flows explored in this study provide some directions for future KM research.

The proposed approach focuses on the visualization of probable determinants to knowledge flows within an organization. This visualization can be utilized to evaluate the determinants and their relationships with each other, which may help managers to take actions to achieve more effective and efficient KM.

The results show the value of using a hybrid approach for dealing with research issues involving various entities and activities with complex content, such as knowledge flows.

The purpose of this study is to describe a framework of ontological techniques to restrengthen current content management systems of a natural science museum. The ontological approach is utilized to extend the service level from information to knowledge.

Two ontologies have been established to perform vascular plant and herbal drug knowledge bases that further facilitate knowledge integration and inference. Furthermore, this study presented ontology development processes, including knowledge acquisition, representation, and retrieval.

Empirical lessons related to development techniques are concluded as follows: first, the formal concept analysis can be used as a knowledge acquisition approach to acquire concepts and attributes from expertise. Second, the Ontology Web Language represents an XML‐based language which provides formal logic expressions for describing knowledge concepts. Finally, the Jena APIs can be further developed as an ontology reasoner to facilitate knowledge inference and retrieval.

The development of ontological knowledge base is time‐consuming and requires seamless collaboration among specialists, knowledge engineers, and information systems.

Empirical lessons indicate that ontological techniques provide potential approaches for library and museum communities to apply for next generation knowledge building.

This study indicates that ontological techniques have excellent potential for knowledge building.

This paper aims to propose a brand new ontology development methodology, called Yet Another Methodology for Ontology (YAMO) and demonstrate, step by step, the building of a formally defined large-scale faceted ontology for food.

YAMO is motivated by facet analysis and an analytico-synthetic classification approach. The approach ensures quality of the system precisely; it makes the system flexible, hospitable, extensible, sturdy, dense and complete. YAMO consists of two-way approaches: top-down and bottom-up. Based on YAMO, domain food, formally defined as large-scale ontology, is designed. To design the ontology and to define the scope and boundary of the domain, a group of people were interviewed to get a practical overview, which provided more insight to the theoretical understanding of the domain.

The result obtained from evaluating the ontology is a very impressive one. Based on the study, it was found that 94 per cent of the user’s queries were successfully met. This shows the efficiency and effectiveness of the YAMO methodology. An evaluator opined that the ontology is very deep and exhaustive.

The authors envision that the current work will have great implications on ontology developers and practitioners. YAMO will allow ontologists to construct a very deep, high-quality and large-scale ontology.

This paper illustrates a brand new ontology development methodology and demonstrates how the methodology can be applied to build a large-scale high-quality domain ontology.