Trends in knowledge modelling and knowledge management

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 human mind and 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, good will and so forth. Knowledge is a valuable asset to an organization, be it an educational institution, a government organization, an industry, a corporate firm, Non Government Organization or any other such organization. The obvious goal is to manage this knowledge in a way it can be stored, shared, distributed and reused efficiently. The primary tasks of knowledge workers include the identification and selection of knowledge sources, and 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, the 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, process), or it could be domain knowledge (for instance, food, biomedicine, music, 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 the 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 the 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.