Web Intelligence (WI) presents excellent opportunities and challenges for the research and development of new generation of Web-based information processing technology, as well as for exploiting Web-based advanced applications. Based on two perspectives of WI research: an intelligent Web-based business-centric schematic diagram and the conceptual levels of WI, we investigates various ways to study WI and potential applications.

Toward web intelligence

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

Dynamically organizing KDD processes

Currently available expert systems have a performance limit because of the lack of capability to describe problems and problem-solving methods. It is closely related with knowledge representation language, but this is not the only concern with this issue. Real world problems and problem-solving methods are not so simple as to be represented always in the same way by the same language. Their representations must be different depending on various factors involved in the problems themselves and the situations these problems are surrounded with. In this paper, the author discusses first the intrinsic nature of problem representation and problem-solving process representation. The requirements for and the conceptual framework of a knowledge-based system that is suited for dealing with various problems then become apparent quite naturally. The author asserts that a multiple meta-level architecture is necessary as well as a knowledge-representation language that can describe complex data structures as the basic framework of knowledge-based systems.

Framework of knowledge-based systems: Multiple meta-level architecture for representing problems and problem-solving processes

A knowledge-based system that can solve large-scale problems is discussed. It is provided with a new method which combines model building with hierarchical problem decomposition. The model-based approach to problem solving is a general method which is ordinarily used by humans. With this method, representations of the problem and the problem-solving process can be separated. Thus, computers provided with this method allows the end user to focus his/her attention on the description of the problem, without paying attention to the description of the method for solving this problem by computer. When a problem becomes large and complex, however, it becomes difficult to solve by this simple model-based method, and it must be combined with another technique to decompose the problem into a set of smaller ones, each of which is simple enough to deal with. This does not assume that the original problem can be decomposed into the same components, because, in general, a problem involves heterogeneous components.

A prototype of a system which could implement this idea has been developed, and its effectiveness has partly been proved. Experimentation is going to be carried out on the total system. The paper discusses a method of implementing the idea with the example of representation.

How can knowledge-based systems solve large-scale problems?: model-based decomposition and problem solving

We have been developing a methodology/system called GLS (Global Learning Scheme) for knowledge discovery in databases. The development of GLS has two main aspects. The first is to develop a multi-strategy system. That is, many kinds of discovery/learning methods are cooperatively used in multiple learning phases for performing multi-aspect intelligent data analysis as well as multi-level conceptual abstraction and learning. As a multi-strategy system, GLS is implemented as a toolkit composed of several sub-systems and optional parts with a multi-level structure. We have finished main parts belonging to this aspect, and have undertaken another aspect, i.e., extending GLS into a multi-agent, distributed and cooperative discovery system. We try to increase versatility and autonomy of GLS by multi-strategy and distributed cooperation. This paper briefly discusses these two aspects of GLS.

/pdf/toward-a-multi-strategy-and-cooperative-discovery-system-20kk4y3adi.pdf

Toward a multi-strategy and cooperative discovery system

A new generation computer is expected to be the knowledge processing system of the future. However, many aspects are yet unknown regarding this technology, and a number of fundamental concepts, directly concerning knowledge processing system design need investigation, such as knowledge, data, inference, communication, information management, learning, and human interface.

Multi-layer logic—A predicate logic including data structure as knowledge representation language

Hazard maps are irreplaceable in dealing with emergency disaster cases, and geography instructors utilize paper hazard maps for teaching at various educational levels. In recent years, web hazard maps have received attention in disaster risk reduction (DRR) education. This study aims to examine the effectiveness of web hazard maps in DRR education compared with conventional paper hazard maps, an aspect that has rarely been studied previously. The core technology of online web hazard maps is geographic information systems (GIS), and their combination is referred to as Web GIS. Online DRR educational materials for Japanese and Chinese high school students were constructed using Web GIS and geospatial data, including hazard information, to investigate the educational feasibility of web hazard maps. Important findings from questionnaire surveys on students who used both paper and web hazard maps showed that 1) most students thought that informative and easy-to-use web maps need to be introduced to DRR education, and 2) they could identify risk areas more accurately using web hazard maps, indicating that DRR education should utilize modern technologies. However, the surveys also indicated a few limitations, such as usability issues and technological constraints, suggesting that DRR education should ideally combine online materials and paper maps for maximum effectiveness. The results and observations may not only be used in future studies, but also in the formulation of effective DRR courses internationally. 

Application of web hazard maps to high school education for disaster risk reduction

17 Disaster risk reduction education (DRRE) is a strategy to mitigate the harmful effects of disasters. The 18 implementation of DRRE in schools is on the rise, and Web GIS technology in DRRE is becoming 19 increasingly prevalent. However, little knowledge exists about whether students can improve their 20 understanding of disaster risk reduction (DRR) through Web GIS technology and which factors affect 21 their learning of web hazard maps. This study has provided materials and curricula for DRR education 22 in Chinese and Japanese high schools, and utilized them eight times in the classroom between 2020 and 23 2022. These classes had three forms of implementation due to the COVID-19 pandemic: online, onsite, 24 and online-onsite mixed. The students first answered a pretest. Then they learned about DRR using the 25 explanatory web pages and the web hazard maps with answering quizzes shown on the pages. After that, 26 they answered a posttest and a questionnaire. Most students show improved results after using the digital 27 DRR materials. It is pronounced for the onsite implementations, whereas the online-onsite mixed 28 implementation was less effective. The pretest scores for the implementations using local disaster cases 29 are low, but the subsequent improvement is significant. The ability of students to utilize electronic 30 devices affects the learning of GIS-related content rather than other aspects. The frequency of daily usage 31 of online maps and daily attention to disaster prevention and mitigation affect learning the entire 32 materials. Increasing the use of online hazard maps is a key to realizing social DRR.

H Yamauchi

Papers

Multi-layer logic—A predicate logic including data structure as knowledge representation language

Application of web hazard maps to high school education for disaster risk reduction

Effects of Web GIS Technology and Curriculum Approaches on 1 Education for Disaster Risk Reduction 2