Dutta, B. and Madalli, D.P. (2015), "Trends in knowledge modelling and knowledge management: an editorial", Journal of Knowledge Management, Vol. 19 No. 1. https://doi.org/10.1108/JKM-10-2014-0442
Emerald Group Publishing Limited
Trends in knowledge modelling and knowledge management: an editorial
Article Type: Editorial From: Journal of Knowledge Management, Volume 19, Issue 1
The goal of this editorial is threefold:
1. to briefly describe the main theme of this special issue;
2. to provide some background information of this issue, and how this issue was conceived including some statistical data on paper selection; and
3. to briefly but individually describe the papers published in this issue.
The topic is timely as there have been an increasing number of researchers, designers, practitioners and users of intelligent systems working in this area of knowledge, knowledge modelling and management aspects. The editors believe that, overall, this issue provides a common platform to people with a diverse background such as library and information science, computer science, artificial intelligence, management, linguistics, philosophy, cognitive science, information architecture, media, etc., who have a mutual interest in understanding and solving problems of knowledge modelling and knowledge management.
2. Theme of this issue
Knowledge, according to Davenport and Prusak (2000) “is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information”. The origin of knowledge is the human mind and it is applied back to the same. To communicate, share and distribute knowledge with others, the usual practice is to embed it into a document in various formats like text, audio, video, etc. In organizations, knowledge is not only embedded into documents or repositories, but also in their organizational activities, routines, processes, goodwill and so forth.
Knowledge is a valuable asset to an organization, be it an educational institution, a government organization, an industry, a corporate firm, an non-government organization or any other such organization. The obvious goal is to manage this knowledge in a way that it can be stored, shared, distributed and reused efficiently. The primary tasks of knowledge workers include the identification and selection of knowledge sources, and the capturing and manipulating the heterogeneous knowledge. The development of innovative techniques of knowledge extraction from variety of resources is an essential dimension of knowledge acquisition process. In this regard, techniques like crowdsourcing, natural language processing, etc. have drawn significant attention of the research community and funding from public and private organizations.
Knowledge modelling is a cross-disciplinary area that deals with approaches to acquire, refine, analyse, capture, model and describe knowledge in a way so as to facilitate its preservation and to ensure that it can be aggregated, substituted, improved, shared and reapplied. Modelling knowledge could be of general knowledge describing the general notions (for instance, space, time, events, matter, material and process), or it could be domain knowledge (for instance, food, biomedicine, music and disaster) describing the domain in terms of classes and properties, or it could be of application knowledge describing the tasks in terms of ordering the execution, reasoning and inferencing knowledge, etc. Essentially, modelling is done to create a machine-interpretable model of knowledge which would be used to stimulate intelligence. Modelling makes it possible to achieve the goals of integration, reusability and interoperability. The ability of reusing the knowledge in different areas of the same domain results in reducing development costs. Various modelling techniques and frameworks (Abdullah et al., 2002; Kingston and Macintosh, 2000) have been evolved over time. Also, to express knowledge, and to make the knowledge machine interpretable, many formal languages have been evolved in the recent past.
Knowledge management (KM) is concerned with the effective and efficient management of organization’s intellectual assets, consisting of tools, techniques and processes and routines that govern the entire process of identifying and creating knowledge, representing, distributing and utilization of knowledge (Davenport, 1994). The efficient management of knowledge would help the organizations to develop quality products and services, would help in leveraging the expertise and skills of workers across the organization, would help in solving the intractable problems, would help in increasing network connectivity between internal and external experts and would improve the capacity of managing innovation and organizational learning. The ultimate aim of KM is to achieve the organizational objectives, such as competitive advantage, sharing the lessons learned, innovation and continuous growth of the organization (Gupta and Sharma, 2004). Various management techniques and strategies have been evolved over time (Snowden, 2002). There are also many technology and solutions, for instance, groupware, workflow, scheduling, content management, e-learning and social media that have been evolved in the recent past.
The current issue is based upon papers and discussions from the International Conference on Knowledge Modelling and Knowledge Management (ICKM 2013), held in Bangalore, India, from 20-21 November 2013. As part of the ICKM 2013 conference, we have published a conference proceedings (Dutta and Madalli, 2013) consisting of 18 papers selected from as many as 41 original submissions after three blind reviews, with an acceptance rate of approximately 43 per cent. As a follow-up to the ICKM, this Special Issue of Journal of Knowledge Management (JKM) is published based on the best papers selected from ICKM plus a few invited papers. The best papers from ICKM have been selected based upon the presentation and discussions at the conference and the recommendations of the review panel. Papers were also invited from experts in the domain. All papers underwent three blind peer review process. The acceptance rate was approximately 41 per cent. This issue together consists of a total nine research articles, and each of them is briefly introduced in the following section.
4. Papers in this issue
Papers included in the issue fall under the broad theme of knowledge modelling dealing with topics such as ontology frameworks, domain based ontology modelling, vocabularies and semantic tools and techniques for KM. Paper on YAMO: Yet Another Methodology for large-scale faceted Ontology construction by B. Dutta, U. Chatterjee and D. P. Madalli discusses an innovative methodology for ontology development. The approach, which has been discussed in detail, demonstrates step-by-step the development of food ontology. The proposed methodology is motivated by the theory of faceted classification. Paper on Development of agricultural ontology from Indian agricultural e-governance data using Indo WordNet by B. Sinha, S. Chandra and M. Garg discusses a case study of the implementation of semantic web technology in the agriculture domain related to e-governance data of legacy systems. The study reveals the problems and difficulties in implementation of unstructured and unformatted unique datasets of multilingual local language-based electronic dictionary (IndoWordnet) for agriculture domain. Paper on Resolving authorization conflicts by ontology views for controlled access to a digital library by S. Dasgupta, P. Pal, C. Mazumdar and A. Bagchi offers a new KM strategy to cover the gradual proliferation of interdisciplinary subject areas. It provides a new architecture for digital libraries, which supports polyhierarchic ontology structure where a child concept representing an interdisciplinary subject area can have multiple parents. The fundamental contributions of this paper can be counted at three different levels: provides a better knowledge representation strategy for present-day digital libraries, proposes a new control access model and provides a client-based view generation algorithm. Paper on SKO types – an entity-based scientific knowledge objects metadata schema by H. Xu and F. Giunchiglia discusses SKO types, an entity-oriented theory for representing and linking scientific knowledge objects by defining entity, entity relationships and attributes of each entity. Paper on Semantic network edges by M. C. Pattuelli and M. Miller describes a multi-level approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. The study discusses the challenges and opportunities of combined machine-driven and human-driven approaches to the development of social networks from textual documents. The novelty of this paper lies in its application of semantic web technologies to the construction and enhancement of a social network with an underlying dataset that makes the data readable across platforms and linkable with external datasets. Paper on Optimization method of technological processes to complex products using knowledge-based genetic algorithm by Y. Yao, Y. Wang, L. Xing and H. Xu proposes a knowledge-based genetic algorithm (KGA), a combination of genetic algorithm with knowledge model, to the technological processes optimization of complex products. The paper shows that the effective integration of GA model and knowledge model greatly improves the optimization performance of KGA. Paper on Enabling organizations to implement smarter, customized social computing platforms by leveraging knowledge flow by R. S. Iyer, R. Chandra and R. Raman discusses the change in knowledge-sourcing behaviours by the current generation workforce and studies its impact on the knowledge flow patterns in organizations. The paper also discusses the means of improving the effectiveness of knowledge-sharing initiatives in the organization via social computing platforms to enable efficient workspace collaboration. Paper on Strategic human capital management for a new university by D. Thienphut, J. Sirasirirusth, S. Jiamprachanarakorn and R. Boonloisong studies the key success factors that determine the direction and context of a new university, namely, Suan Dusit Rajabhat University, Thailand, and proposes a generic strategic human capital management framework applicable to other academic and higher learning institutions. Paper on Fostering knowledge sharing behaviour among public sector managers by G. Tangaraja, R. Mohd Rasdi, M. Ismail and B. Abu Samah proposes a conceptual model of -haring behaviour among Malaysian public sector managers. The paper introduces a new approach in theorizing knowledge-sharing behaviour by integrating the general workplace commitment model, self-determination theory and social capital theory. The authors identified three potential predictor groups of knowledge-sharing behaviour among Malaysian public sector managers. The paper also provides a number of potential research questions, which may lead to additional proposition for future research.
The guest editors would like to thank Rory Chase, Chief Editor of Journal of Knowledge Management, for offering us the privilege of editing this special issue. The guest editors were allowed a generous time frame to compile this special issue. The following persons have acted as reviewers for papers submitted to this special issue and their assistance is deeply appreciated (ordered alphabetically): A.R.D. Prasad, Aditya Bagchi, Alessandro Agostini, Amitabha Chatterjee, Ananya Kanjilal, Animesh Dutta, Claudio Gnoli, Craig M. MacDonald, Cristina Pattuelli, David King, Debashis Mitra, Dong-Geun Oh, Feroz Farazi, Hui Yang, Irene Lopatovska, John Bordeaux, Nikos Manouselis and Renchu Guan.
Devika P. Madalli
Biswanath Dutta and Devika P. Madalli are both based at Department of Documentation Research and Training Centre, Indian Statistical Institute, Bangalore, India.
Abdullah, M.S., Benest, I., Evans, A. and Kimble, C. (2002), “Knowledge modeling techniques for developing knowledge management systems”, 3rd European Conference on Knowledge Management, Trinity College, Dublin, pp. 7-28.
Davenport, T.H. (1994), “Saving IT’s soul: human centered information management”, Harvard Business Review, Vol. 72 No. 2, pp. 119-131.
Davenport, T.H. and Prusak, L. (2000), Working Knowledge: How Organization Manage What They Know, Harvard Business School Press, Boston, MA.
Dutta, B. and Madalli, D.P. (Ed.) (2013), Proceedings of International Conference on Knowledge Modelling and Knowledge Management, Indian Statistical Institute, Bangalore, p. 184.
Gupta, J. and Sharma, S. (2004), Creating Knowledge Based Organizations, Idea Group Publishing, Boston, MA.
Kingston, J. and Macintosh, A. (2000), “Knowledge management through multi-perspective modeling: representing and distributing organizational memory”, Knowledge-Based Systems, Vol. 13 Nos 2/3, pp. 121-131.
Snowden, D. (2002), “Complex acts of knowing – paradox and descriptive self awareness”, Journal of Knowledge Management, Vol. 6 No. 2, pp. 100-111.
About the authors
Biswanath Dutta is an Assistant Professor at the Documentation Research and Training Centre, Indian Statistical Institute, Bangalore, India, and a Courtesy Professor at the University of Trento, Italy. In 2010, he received his PhD in Library and Information Science from University of Pune and the work carried out at Indian Statistical Institute, Bangalore. He was a post-doctoral fellow at the University of Trento from 2009-2012 and has been a Research Assistant in Dalhousie University, Halifax, Canada. He has published around 30 scientific papers. He actively participated in the LivingKnowledge European Union-funded research project. Currently, he is actively involved in India–Trento Programme for Advanced Research (ITPAR III) project. His present research interests are in the areas of ontology modelling, knowledge organization and representation, knowledge management, digital library and semantic web. Biswanath Dutta is the corresponding author and can be contacted at: mailto:firstname.lastname@example.org
Devika P. Madalli is an Associate Professor at DRTC, Indian Statistical Institute, Bangalore, and an adjunct faculty at DISI, University of Trento, Italy. She is on the advisory board of Universal Decimal Classification and a Co-Chair of the interest group on Agricultural Data at the Research Data Alliance. She has participated in European Union-funded project including LivingKnowledge, AgInfra and worked as a consultant to UNESCO and UNFAO. She has contributed to UNESCO’s Global Open Access Portal. Dr Madalli’s interest is in the areas of knowledge organization and application of facetization in information systems, information infrastructure, digital libraries, semantic web technologies, faceted ontologies and multilingual information services.