An approach to the development of commonsense knowledge modeling systems for land selection

The land use methods which are ergonomically and environmentally appropriate are determined first and foremost by characteristics and location. For instance, land selection in architectural construction domain is considered as an area in land use methods, which involves commonsense knowledge of architects. This is because land selection criteria are very personal and there is no theory behind how it should be done. Sometime, there are too many redundancies in the process selection of lands. In this paper we present an approach to modeling commonsense knowledge in a sub field of architecture domain of land selection to come up with land classifications as psychological, physical and social events. This gives three-phase knowledge modeling approach for modeling commonsense knowledge in, which enables holistic approach for land selection. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy

DEVELOPMENT OF FUZZY EXPERT SYSTEMS FOR TACIT KNOWLEDGE MODELING IN STRATEGIC DECISION-MAKING

Knowledge modelling gives the intention of knowledge engineering which is applicable for managing information systems. Tacit knowledge is the key issue of knowledge modelling aspect because all knowledge is rooted in tacit knowledge. In recognizing knowledge as a new resource in gaining organizational competitiveness, knowledge management suggests a method in managing and applying knowledge for improving organizational performance. Much knowledge management research has focused on identifying, storing, and disseminating process related knowledge in an organized manner. Applying knowledge to decision making has a significant impact on organizational performance than solely processing transactions for knowledge management. This paper presents a research that incorporates modelling of tacit knowledge for strategic decision-making. Here we have used fuzzy expert system for developing an approach for modelling tacit knowledge. We primarily used fuzzy logic together with statistical technique of principal component analysis as techniques for modelling tacit domains. Tacit knowledge in Ayurvedic sub-domain of individual classification has been acquired through a questionnaire and analysed to identify the dependencies, which lead to make tacit knowledge in the particular domain. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. Keywords: Fuzzy Expert System, Tacit Knowledge, Principal Component Analysis, and Strategic Decision-making, Ayurvedic MedicineFor full Paper: [email protected]

A FUZZY EXPERT SYSTEM FOR BUSINESS INTELLIGENCE

Business Intelligence (BI) is recognized as an increasingly important support for business decision making in emerging business environment, where a huge amount of data is growing fast and scattered around. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as commonsense knowledge can be represented in informal way and further modeling needed for BI. Acquiring useful Business Intelligence (BI) for decision-making is a challenging task in dynamic business environment. In this paper we present an approach for modeling commonsense knowledge in Business Intelligence. A fuzzy expert system based on principal component analysis (PCA) and statistical fuzzy inference system for modeling Business Intelligence in commonsense knowledge is introduced in, which enables holistic approach for disaster management. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy. Key words: Business Intelligence, Statistical inference system, Common sense knowledge, Principal component analysis and Ayurvedic medicineFor full Paper: [email protected]

Evaluating disaster management knowledge model by using a frequency-based selection technique

© Springer-Verlag Berlin Heidelberg 2012. Disaster Management (DM) is a multidisciplinary endeavour and a very difficult knowledge domain to model. It is a diffused area of knowledge that is continuously evolving and informally represented. Metamodel is the output artefact of metamodelling, a software engineering approach, which makes statements about what can be expressed in the valid models of the knowledge domain. It is an appropriate high level knowledge structure to facilitate it being communicated among DM stakeholders. A Disaster Management Metamodel (DMM) is developed. To satisfy the expressiveness and the correctness of a DMM, in this paper we present a metamodel evaluation technique by using a Frequency-based Selection. The objective of this technique is to evaluate the importance of the individual concepts used in the DMM, thus, the quality of the metamodel can be measured quantitatively