Showing papers by "Riichiro Mizoguchi published in 2004"

Ontology-based systematization of functional knowledge

Abstract: It has been recognized that design knowledge is scattered around technology and target domains. One of the two major reasons for it is that different frameworks (viewpoints) for conceptualization of design knowledge are used when people try to describe knowledge in different domains. The other is that several key functional concepts are left undefined or even unidentified. In this paper, we first overview the state of the art of ontological engineering, which we believe is able to make a considerable contribution to resolving these difficulties. We then discuss our enterprise aiming at systematization of functional knowledge used for synthesis. We discuss ontologies that guide conceptualization of artefacts from the functional point of view. The framework for knowledge systematization is based on an extended device ontology and a functional concept ontology built on top of the extended device ontology. This paper particularly discusses the extended device ontology and its application in the mechanical dom...

Tutorial on ontological engineering: part 3: Advanced course of ontological engineering

Abstract: This article deals with advanced topics of ontological engineering to convince readers ontology is more than a rule base of terminological problems and is worth to consider a promising methodology in the next generation knowledge processing research. Needless to say, ontology in AI is tightly connected to ontology in philosophy. The first topic here is on philosophical issues which are very important to properly understand what an ontology is. After defining class, instance and is-a relation, we point out some typical inappropriate uses of is-a relation in existing ontologies and analyze the reasons why. Other topics are basic ontological distinction, part-of relation, and so on. As an advanced example of ontology, an ontology of representation is extensively discussed. To conclude this tutorial, a success story of ontological engineering is presented. It is concerned with a new kind of application of ontology, that is, knowledge systematization. An ontology-based framework for functional knowledge sharing has been deployed into a company for two years and has been a great success. Finally, future of ontological engineering is discussed followed by concluding remarks.

Tutorial on ontological engineering Part 2: Ontology development, tools and languages

Abstract: Practical aspects of ontological engineering are discussed in this part. First topic is the methodology of ontology development. Next, ontology representation languages and support tools are discussed as well as ontology alignment and merging which are becoming practically important to cope with distributed development of ontologies. We next discuss several ontologies developed thus far including large-scale knowledge bases such as Cyc, practical domain ontologies such as Enterprise ontology and gene ontology and generic ontologies such as PSL: Process Specification Language and SUO: Standard Upper Ontology. The first topic of ontology applications is the semantic web in which semantic interoperability, metadata and web service ontology are described. e-Learning is also a good application area of ontology in which LOM: Learning Object Metadata and ontology-aware authoring systems are discussed followed by conclusion.

Part 3: Advanced course of ontological engineering

Abstract: This article deals with advanced topics of ontological engineering to convince readers ontology is more than a rule base of terminological problems and is worth to consider a promising methodology in the next generation knowledge processing research. Needless to say, ontology in AI is tightly connected to ontology in philosophy. The first topic here is on philosophical issues which are very important to properly understand what an ontology is. After defining class, instance andis-a relation, we point out some typical inappropriate uses ofis-a relation in existing ontologies and analyze the reasons why. Other topics are basic ontological distinction, part-of relation, and so on. As an advanced example of ontology, an ontology of representation is extensively discussed. To conclude this tutorial, a success story of ontological engineering is presented. It is concerned with a new kind of application of ontology, that is, knowledge systematization. An ontology-based framework for functional knowledge sharing has been deployed into a company for two years and has been a great success. Finally, future of ontological engineering is discussed followed by concluding remarks.

Learners’ Roles and Predictable Educational Benefits in Collaborative Learning

Abstract: To facilitate shared understandings of several models of collaborative learning, and collect rational models of effective collaborative learning, we have been constructing a system of concepts to represent collaborative learning sessions relying on existing learning theories. We call the system of concepts Collaborative Learning Ontology, and have been extracting and representing models inspired by the theories with the ontology. In this paper, as a part of the ontology, we concentrated on clarifying behavior and roles for learners in collaborative learning sessions, conditions to assign appropriate roles for each learner, and predictable educational benefits by playing the roles. The system of concepts and models will be beneficial to both designing appropriate groups for collaborative learning sessions, and interaction analysis among learners to assess educational benefits of the learning session.

OntoAIMS: ontological approach to courseware authoring

Abstract: In this paper we discuss how current ontology concepts can be beneficial for more flexible and semantic rich description of the authoring process and for the provision of authoring support of Intelligent Educational Systems (IES) with respect to the three main authoring modules: domain editing, course composition and resource management. We take a semantic perspective on the knowledge representation within such systems and explore the interoperability between the various ontological structures for domain, instructional and resource modeling and the modeling of the entire authoring process. We build upon our research on Authoring Task Ontology and exemplify it within OntoAIMS system. We present authoring scenarios and show their mapping with authoring task ontology. Further we discuss the OntoAIMS framework for management of electronic learning objects (resources) and their usage in the automatic generation of course templates for the authors. Finally, we describe our architecture, based on the ontological specification of the authoring process.

Ontology-Based Functional-Knowledge Modeling Methodology and Its Deployment

Abstract: Functionality is one of the key concepts in understanding an artifact and in engineering domain knowledge. Although the importance of sharing of engineering knowledge in industry has been widely recognized, from our experience with collaborative research with a production company, industrial engineers have had difficulty in sharing engineering knowledge including functionality. To promote the sharing of the engineering knowledge from the viewpoint of functionality, we have established an ontology-based knowledge modeling methodology for functional knowledge, which has been successfully deployed in a production company. It consists of two ontologies to capture functionality and the specifications for modeling processes. This paper summarizes these ontologies and its deployment, and discusses the modeling process based on the ontologies, which includes detailed modeling steps, types of functional knowledge, and ontological guidelines.

Ontological knowledge base reasoning with sort-hierarchy and rigidity

Abstract: Although sorts and unary predicates are semantically identical in order-sorted logic, they are classified as different kinds of properties in formal ontology (e.g. sortal and non-sortal). This ontological analysis is an essential notion to deal with properties (or sorts) of objects in knowledge representation and reasoning. In this paper, we propose an extension of an order-sorted logic with the ontological property classification. This logic contains types (rigid sorts), non-rigid sorts and unary predicates to distinguishably express the properties: substantial sorts, non-substantial sorts and non-sortal properties. We define a sorted Horn-clause calculus for such property expressions in a knowledge base. Based on the calculus, we develop a reasoning algorithm for many separated knowledge bases where each knowledge base can extract rigid property information from other knowledge bases (called rigid property derivation).

Organizing Role-concepts in Ontology Development Environment: Hozo

Abstract: In this research, we aim to enhance descriptive quality of role-concept in our ontology development system Hozo. Based on some fundamental theories of ontology, we can define a “concept” of a role which an object plays in a context, although that is treated as one kind of “property” in most of the ontology development systems. When constructing an ontology, discrimination of role-concepts from the other concepts helps to develop a theoretically sound ontology which describes definitions of concepts properly based on ontological theories. So, we have been developing a framework for definition of role-concepts in Hozo. In this paper, we extend the framework for the specification of a hierarchy of role-concepts such as teacher, husband, commodity, etc. differentiated from the other concepts such as human, apple, etc., and we present how to treat the relations between these two kinds of hierarchies of concepts.

Selecting Theories in an Ontology-Based ITS Authoring Environment

Abstract: This paper introduces the rationale for concrete situations in the authoring process that can exploit a theory-aware Authoring Environment. It illustrates how Ontological Engineering (OE) can be instrumental in representing the declarative knowledge needed, and how an added value in terms of intelligence can be expected for both authoring and for learning environments.

Leaner Model Ontology and leaner Model Agent

Abstract: In this paper we describe the learner model ontology and learner model agent in a multi-agent architecture of learning support systems. The learner model ontology facilitates 1) sharable specification of the functionalities of learner model agent; 2) fluent communication between the learner model agent and other agents. The example of a learner model ontology and learner model agent shows that with the help of ontology, a standard for sharable and reusable agents in learning support systems may be established. The goal of the research presented in this paper is to exemplify the benefit of the learner model ontology through the development of an ontology-based learner model agent and the exchange of information between the learner model agent and other agents.

Learners' Roles and Predictable Educational Benefits in Collaborative Learning An Ontological Approach to Support Design and Analysis of CSCL

Abstract: To facilitate shared understandings of several models of collaborative learning, and collect rational models of effective collaborative learning, we have been constructing a system of concepts to represent collaborative learning sessions relying on existing learning theories. We call the system of concepts Collaborative Learning Ontology, and have been extracting and representing models inspired by the theories with the ontology. In this paper, as a part of the ontology, we concentrated on clarifying behavior and roles for learners in collaborative learning sessions, conditions to assign appropriate roles for each learner, and predictable educational benefits by playing the roles. The system of concepts and models will be beneficial to both designing appropriate groups for collaborative learning sessions, and interaction analysis among learners to assess educational benefits of the learning session.

Coping With Unintended Behavior of Users and Products: Ontological Modelling of Product Functionality and Use

Abstract: The function-behavior representation language FBRL was originally devised for modeling and knowledge management of intended product behavior. This paper explores its potential for application to other-than-intended behavior in a product-use context by introducing consideration of the user and the environment. We found that slightly adapted building blocks from as-is FBRL can be applied to behavior that is unintended and/or not performed by the product. To support anticipation of unintended behavior in design, special attention has to be paid to the knowledge that connects product functions, user actions and environment behavior. We distinguish typical and atypical forms of unintended use. Some forms of typical unintended use can be directly derived from the intended use. Yet, most forms of unintended use require additional knowledge, e.g., from user observations. To include such knowledge, subsequent effort has to be put into its systematization. In this paper, an ontological scheme is presented for models of the product, the user and the environment and related use processes. We present an example and discuss how supporting tools can help designers to deal with unintended use. In the example case, a modeling schema for unintended behaviors of products is extended towards unintended behaviors of users.Copyright © 2004 by ASME

EASE: Evolutional Authoring Support Environment

Abstract: How smart should we be in order to cope with the complex authoring process of smart courseware? Lately this question gains more attention with attempts to simplify the process and efforts to define authoring systems and tools to support it. The goal of this paper is to specify an evolutional perspective on the Intelligent Educational Systems (IES) authoring and in this context to define the authoring framework EASE: powerful in its functionality, generic in its support of instructional strategies and user-friendly in its interaction with the author. The evolutional authoring support is enabled by an authoring task ontology that at a meta-level defines and controls the configuration and tuning of an authoring tool for a specific authoring process. In this way we achieve more control over the evolution of the intelligence in IES and reach a computational formalization of IES engineering.

Towards Evolutional Authoring Support Systems.

Abstract: The ultimate aim of this research is to specify and implement a general authoring framework for content and knowledge engineering for Intelligent Educational Systems (IES). In this context we attempt to develop an authoring tool supporting this framework that is powerful in its functionality, generic in its support of instructional strategies and user-friendly in its interaction with the author. The framework that we offer is an ontology-based authoring environment, since we see the use of ontologies as effective means to have structured content and knowledge engineering. The key contribution of our research is the meta-level Authoring Task Ontology (ATO) specifying authoring tasks, goals and activities, and knowledge about the process of engineering IES. It enables us to enable the specification of "evolutional" authoring support system, as a meta-authoring tool that defines and controls the configuration and tuning process of an authoring tool for a specific authoring process. The role of ATO is to help the IES authoring system to "evolve" by defining such a meta-tool, which "knows" the system's ontological structure. In our approach it works analogously to an authoring tool when it generates a concrete learning support system. The ATO also provides a shared vocabulary between all system components, and allows for better interoperability in a modularised architecture. In this way we have the benefit to monitor and assess the authoring process, and to prevent and solve inconsistencies and conflicting situations. Through ontological engineering, a computational formalization of the intelligent systems authoring, we also give our scientific contribution to the in-depth understanding of what the authoring process is.

What is Metacognitive Skill? Collaborative Learning Strategy to Facilitate Development of Metacognitive Skill

Abstract: Recently, how to support development of a learner’s metacognitive skill is one of the most important topics in the research area of educational support systems. However, it is difficult for the researchers in the area to share research findings and reuse them each other, because there is a lack of common vocabulary to represent metacognitive phenomena and shared understandings of them. It causes some confusion in the studies about metacognition and metacognitive skill. In this paper, we provide a common platform towards constructing common vocabulary and shared understandings of them. Then, we introduce our model of metacognitive skill and describe difficulties in learning and executing the skill. Based on our model, we also propose a learning support environment where learners develop their metacognitive skills through collaborative learning. Introduction The term metacognition has been used mainly in psychological area since the publication of Flavell’s paper (1976). Many researchers have been believed that metacognition is associated with intelligence (e.g., Borkowski, et al. 1987; Sternberg, 1984, 1986a, 1986b) and they refer metacognition to higher order thinking which involves active control over the cognitive processes engaged in learning (Livingston, 1997). So, recently, to support development of learners’ metacognitive skills is one of the most important topics in the research area of educational support systems. The purpose of our study is to support processes in which learners develop their metacognitive skills. Metacognition is often simply defined as “thinking about thinking” (Livingston, 1997), or “cognition of cognition” (Flavell, 1976). However, defining metacognition is not simple. Although the term has been part of the vocabulary of educational psychology for the last couple of decades, there is still much debate over what metacognition exactly means. One reason for this confusion is the fact that there are several terms currently used to describe the same basic phenomenon (e.g., self-regulation, executive control), or an aspect of that phenomenon (e.g., meta-memory), and these terms are often used interchangeably in the literature (Brown 1987, Carver & Scheier 1998, Davidson, et al. 1994, Flavell 1976, Hacker 1998, King 1999, Kluwe 1982, Livingston 1997, Lories, et al. 1998, Nelson & Narens 1994, Winne & Hadwin 1998, Yzerbyt, et al. 1998). Another reason is the confusion that is thrown by two approaches to metacognition. Some researchers consider metacognitive skill as somewhat special cognitive activity and trying to clarify its mechanism (Rivers 2001; Schraw 1998; Kluwe 1982; Nelson & Narens 1994). Other researchers suppose that metacognitive skill is similar process with cognitive skill (Livingston 1997; Lories, et al. 1998). While there are some distinctions between definitions (Van Zile-Tamsen, 1994, 1996), all emphasize the role of executive processes in the overseeing and regulation of cognitive processes. To advance the study of metacognitive skill and of the methods how to develop the metacognitive skill, we should construct common vocabulary to represent cognitive and metacognitive phenomena (Mizoguchi & Bourdeau 2000). The common vocabulary of metacognitive skill makes us to share mutually understandings, store the result of our studies, and reuse and reconstruct the results of the studies. In this paper, first, we clarify the target of our educational support system through defining some concepts related to metacognition. Although our definition may be rough and insufficient, we believe it will be useful to share the concepts. Next, we propose our model of metacognitive skill and consider difficulties in mastering the skill. Finally, we propose a collaborative learning strategy as an appropriate method to facilitate development of learners’ metacognitive skill. What Is “Metacognitive Skill” ? Livingston (1997) describes that metacognition is one of the latest buzz words in educational psychology, but what exactly is metacognition? Flavell’s simple definition has already been not adequate to consider how to support

Towards modeling design rational of supplementary functions in conceptual design

Abstract: The importance of sharing designer’s intention (socalled design rationale, DR) is widely recognized. This paper focuses on modeling DR of supplementary functions which prevent undesirable states of a required function (e.g., malfunctions). Although they do not essentially contribute to the achievement of the required function, they play an important role in design and design improvement of artifacts. Nevertheless, their DRs, that is, “what undesirable phenomena is avoided by them” and “how they avoid those phenomena”, often remain implicit. We aim at establishing ontologies for capturing such designer’s knowledge systematically in order to share such knowledge among designers. In this article, a modeling framework to explicate the design rationales of supplementary functions is proposed. For this purpose, we articulate concepts concerning a process of undesirable states and how a supplementary function prevents the process. By using such concepts, functionality of components and undesirable phenomena are integrated into an extended functional model. This model can be used in multiple tasks such as design, redesign, design review and reliability assessment. Besides, we present classification of supplementary functions based on the model and four display modes of the model according to the demand of a designer.

Cell Signaling Networks Ontology

Abstract: Although databases for cell signaling pathways include numbers of reaction data of the pathways, the reaction data cannot be used yet to deduce biological functions from them. For the deduction, we need systematic and consistent interpretation of biological functions of reactions in cell signaling pathways in the context of "information transmission". To address this issue, we have developed a functional ontology for cell signaling pathways, Cell Signaling Network Ontology (CSN-Ontology), which provides framework for the functional interpretation presenting some important concepts as information, selectivity, movability, and signaling rules including passage of time.

A design environment to articulate design intention of learning contents

Abstract: Design of learning contents is more difficult to support than design of a physical substance because its object is abstract and it is difficult to establish a framework to express it appropriately. To address this issue, we converted an abstract concept about learning content design to an ontology. Then, on the basis of that ontology, we developed iDesigner, a design support environment. iDesigner realises the following two points: the designers are compelled to make the implicit results of their work explicit in order to deepen thinking about the design of an abstract matter; and the designers are provided with basic information to verify content validity by simulating the change of understanding of a learner in the learning contents model at a conceptual level, which is the intermediate result of design.

Towards Shared Understanding of Metacognitive Skill and Facilitating Its Development

Abstract: Our research objective is to organize existing learning strategies and systems to support the development of learners’ metacognitive skill. It is difficult to organize them because the term metacognition itself is mysterious and ambiguous. In order to achieve the objective, we first organize activities in cognitive skill and metacognitive skill. It enables us to reveal what activity existing learning strategies and systems support as metacognitive skill or what activity they do not support. Next, we simplify existing learning strategies and systems by ontology. It helps us to understand what of learning strategies and support systems is respectively different, and what of them is respectively similar. It will contribute to a part of an instructional design process.

Development of a System that Provides Teachers with Useful Resources from Various Viewpoints Based on Ontology

Abstract: In Japan, teaching of the subject "Information" was started in the high schools in April 2003, and interest in IT/information education has continued to grow since then. It is very difficult to design IT/information education materials, and there are very few specialist teachers of IT/information education. For this reason, it is necessary and important to provide teachers of IT /information education with a variety of useful resources. To that end, we are building a system that supports teachers in designing instructional materials for IT/information education. Here, we describe a part of this system that reconstructs resources according to the various viewpoints which teachers require. This function of the system is realized by tagging each resource with the ontology of IT/information education in the Resource Description Framework (RDF).

Learners' roles and predictable educational benefits in collaborative learning an ontological approach to support design and analysis of CSCL

Abstract: To facilitate shared understandings of several models of collaborative learning, and collect rational models of effective collaborative learning, we have been constructing a system of concepts to represent collaborative learning sessions relying on existing learning theories. We call the system of concepts Collaborative Learning Ontology, and have been extracting and representing models inspired by the theories with the ontology. In this paper, as a part of the ontology, we concentrated on clarifying behavior and roles for learners in collaborative learning sessions, conditions to assign appropriate roles for each learner, and predictable educational benefits by playing the roles. The system of concepts and models will be beneficial to both designing appropriate groups for collaborative learning sessions, and interaction analysis among learners to assess educational benefits of the learning session.

Ontologies for authoring of intelligent educational systems

Abstract: The goal of this paper is to illustrate the beneficial role of ontologies in achieving efficient authoring support for Intelligent Educational Systems (IES). We present our ideas within an ontology-driven framework EASE offering power with respect to the functionality, generic approach for its support of instructional strategies and user-friendliness in its interaction with the author. A central function in it has an authoring task ontology that at a metalevel defines and controls the configuration and tuning of an authoring tool for a specific authoring process. In this way we achieve more control over the evolution of the intelligence in IES and reach a computational formalization of IES engineering.

Data retrieval system and data retrieval apparatus

Abstract: PROBLEM TO BE SOLVED: To support task processing by efficiently displaying, editing and retrieving data in a data base associated with the task processing. SOLUTION: In a data retrieving system, a client computer and a server computer are connected through a network, and data in a data base associated with the task processing of the server computer are inputted, displayed, edited and retrieved by a client computer. In this case, function data associated with the function of the task processing and system data are stored so as to be associated with each other in the data base, and data for display are prepared corresponding to the association, and the data of the data base are displayed with a function decomposition tree diagram 52 being a tree structure by the client computer. COPYRIGHT: (C)2004,JPO&NCIPI

Building of an Ontology of the Goal of IT Education and Its Applications

Abstract: In Japan, interest in IT education has continued to grow. Most goals of IT education involve meta-ability, which cannot be fully learned by traditional Japanese instructional methods. It is difficult to design effective IT education materials, and at present there are few specialists in IT education. For this reason, it is necessary and important to provide IT instructors with a powerful help system that can locate and provide access to a variety of useful information resources. To that end, we are building a system that reconstructs the resources according to the various viewpoints based on an ontology of IT education we built. Further, we propose a framework to make use of the results of another ontology by alignment of these ontologies based on Semantic Web technology.

Towards shared understanding of metacognitive skill and facilitating its development

Abstract: Our research objective is to organize existing learning strategies and systems to support the development of learners' metacognitive skill. It is difficult to organize them because the term metacognition itself is mysterious and ambiguous. In order to achieve the objective, we first organize activities in cognitive skill and metacognitive skill. It enables us to reveal what activity existing learning strategies and systems support as metacognitive skill or what activity they do not support. Next, we simplify existing learning strategies and systems by ontology. It helps us to understand what of learning strategies and support systems is respectively different, and what of them is respectively similar. It will contribute to a part of an instructional design process.

Ontological Engineering and ITS Research

Abstract: Ontology has attracted much attention recently. Semantic Web (SW) is accelerating it further. As far as the author is concerned, however, ontology as well as ontological engineering is not well-understood by people. There exist two types of ontology: One is computer-understandable vocabulary for SW and the other is something related to deep conceptual structure closer to philosophical ontology. In this talk, I would like to explain the essentials of ontological engineering laying much stress on the latter type ontology.

The JSAI and AI activity in Japan

Abstract: Japan has a long history of AI research, in fact, research on computer vision and natural language processing was already in progress in the early seventies. However, two events that started Japan's wave of AI research were the International Joint Conference on Artificial Intelligence (IJCAI) in Tokyo in 1979 and the Fifth Generation Computer System Project which the Ministry of International Trade and Industries started in 1982. Both events greatly impacted Japanese AI research-especially the FGCS project, a 10-year national project to build an inference machine based on logic programming. Japan established its Japanese Society for Artificial Intelligence in 1986. JSAI has about 3,500 members and several special-interest groups. It publishes bimonthly online journals and transactions. JSAI recently established a new technical paper category called AI Frontier. This category has distinct evaluation criteria: papers must deeply impress the six expert evaluators. JSAI established the Pacific Rim International Conference on Artificial Intelligence in 1990 to complement IJCAI and the European Conference on Artificial Intelligence.