M. S. Ali

Bio: M. S. Ali is an academic researcher. The author has contributed to research in topics: Knowledge-based systems & Knowledge extraction. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Overview of Xml based Knowledge Representation using Scripts

Abstract: A knowledge representation (KR) is an idea to enable an individual to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. The knowledge acquired from experts or induced from a set of data must be represented in a format that is both understandable by humans and executable on computers. Knowledge representation research involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of fact within a knowledge domain. A symbol vocabulary and a system of logic are combined to enable inferences about elements in the knowledge representation to create new knowledge representation sentences by using various techniques.

Developing Web-based Semantic and Fuzzy Expert Systems using Proposed Tool

Abstract: Developing the expert system (ES) using conventional programming languages is very tedious task. Therefore, it is not surprising that tools have been developed that can support the knowledge engineer. Separate tools now exist to support the knowledge acquisition and to support the implementation. Fuzzy set theory is used to capture imprecision in inputs and outputs of models, and fuzzy expert systems are used as a method of reasoning with imprecision. Fuzzy expert system permits handling uncertainties, ambiguities, and contradictions in the knowledge. In this research, a tool is proposed for development of webbased expert systems and utilizes fuzzy logic and semantic web technology which permits the knowledge engineer and domain expert to define the knowledge without having to know anything about programming languages and AI. The knowledge can be conceptualized using WordNet. The tool can induce new rules based on the semantic similarity of the concepts using WordNet. During acquiring the knowledge by a proposed tool using domain expert, the fuzzification process can be performed for values of in the acquired knowledge, then, the fuzzy inference can be initiated that has derivation of the control outputs based on the calculated fire strength and the defined fuzzy sets for each output variable in the consequent part of each rule. Finally, defuzzification is performed that involves weighting and combining a number of fuzzy sets resulting from the fuzzy inference process in a calculation, which gives a single crisp value for each output. Using a proposed tool, the Web-based fuzzy expert system can be developed simply and takes short time and effort. The proposed tool is evaluated by using the diagnosis domain of air pollution diseases.

Knowledge Representation Structure for Cloud Platforms: SUMMUS Semantic Encoding Forest

Abstract: Big data era, modern knowledge representation structures do not have appropriate structural support for parallel computing, unable to meet the time requirements of large data processing. We proposed one knowledge representation structure for cloud platform called SUMMUS Semantic Encoding Forest. Through semantic concept further fine-grained decomposition to achieve a unified representation of semantic relations and make binary semantic content-based encoding, then from structure level support parallel computing of semantic reasoning. This structure have better capabilities than the traditional knowledge represent structure at easy programming, complexity of rules and better structural etc. aspects. The time complexity of knowledge reasoning is limited to O (n). Experiments show that knowledge inference time of SUMMUS Semantic Encoding Forest do not obviously increased with the consumption of entities' or events' number of conflicts linearly increase, time performance is better than other existing knowledge representation structures. Keywords-knowledge representation; big data; cloud platform; semantic encoding forest