A societal knowledge management system : harnessing indigenous wisdom to build sustainable predictors for adaptation to climate change

As the climate changes globally time-honoured climate in a region may change and shift to another region. Consequently, local predictors of climate may no longer apply to the area where they were born, but may be invaluable in new regions where previously reliable predictors have become outmoded This paper is set on the proposition thaI traditional (indigenous) know/edge can be a strategic source in adapting to climate change, in these changing times. The research reported in this paper takes the Societal Knowledge Management approach where knowledge that rests within local communities, is harnessed to inform local communities and scientists regarding climate change impacts, so as to adapt to them accurately. A phased study was conducted that aimed at acquiring, synthesising and disseminating traditional knowledge regarding change in monsoon patterns in India. Traditional wisdom prevalent among fhe myriad communities of India, was collected, collated and classified into knowledge spheres such as Bio-Indicators, Wind Movement, Atmospheric Pal/ems, Astrological Methods, Festivals and Rituals, Direction, Characteristics of the Rain, Characteristics of Celestial Bodies etc and incorporaled into a knowledge portal, which is the basis for building the Societal Knowledge Management System (SKM). Subsequently, the SKM is to be harmonised with scientific predictors on seasonal weather patterns will allow researchers to identify if the existing indicators and monsoon pattems are subject to change, and if so how. Research in progress is aimed at integrating the knowledge with modern science and disseminating this knowledge through local knowledge centres, at village levels. Furthermore, this study is to be replicated in Australia, by harnessing indigenous knowledge, to build the SKM for Australia that could assist in building a better understanding of the factors that impact the environment, methods of building sustainable predictors for climate and approaches for adapting the climate changes. The research reported is expected to inform policy makers, scientists, governance institutions as well as researchers regarding the applicability of indigenous knowledge in building sustainable predictors for adaptation to climate change in the two countries cited and can be extended into other countries.

Fuzzy-neuro web-based multilingual knowledge management

This paper presents new methodology towards the automatic development of multilingual Web portal for multilingual knowledge discovery and management. It aims to provide an efficient and effective framework for selecting and organizing knowledge from voluminous linguistically diverse Web contents. To achieve this, a concept-based approach that incorporates text mining and Web content mining using neural network and fuzzy techniques is proposed. First, a concept-based taxonomy of themes, which will act as the hierarchical backbone of the Web portal, is automatically generated. Second, a concept-based multilingual Web crawler is developed to intelligently harvest relevant multilingual documents from the Web. Finally, a concept-based multilingual text categorization technique is proposed to organize multilingual documents by concepts. As such, correlated multilingual Web documents can be gathered/filtered/organised/ based on their semantic content to facilitate high-performance multilingual information access.

An intelligent content discovery technique for health portal content management

Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new contentMethods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current.