Showing papers by "Riichiro Mizoguchi published in 2001"

Ontological Engineering: Foundation of the Next Generation Knowledge Processing

Abstract: Ontological engineering as a key technology of the next generation knowledge processing is discussed. After a brief introduction to ontological engineering with my speculation about its potential contribution, three major results of the practice of ontological engineering in my lab are presented. Then, paradigm shift in information processing is discussed followed by a future directions in the Web intelligence context.

Design and Analysis of Learners' Interaction based on Collaborative Learning Ontology

Abstract: We aim at supporting the complex instructional design process for collaborative learning. We adopt learning theories as a basis for designing, analyzing, and evaluating the collaborative learning session. We have brought the ontological engineering technique into our approach. Up to the present, we have built a collaborative learning ontology and formulated collaborative learning models in terms of the ontology. Currently, laying the ontology and the model as basis, we have been conducting a project aiming at developing various kinds of ID support system for collaborative learning. In this paper, we will describe an overview of our Collaborative Learning Ontology, and then present two systems to support the instructional design process for collaborative learning: a group formation support system, and an interaction analysis support system.

Foundation of Knowledge Systematization: Role of Ontoiogical Engineering

Abstract: The objective of the research described in this article is to give a firm foundation for knowledge systematization from AI point of view. Our knowledge systematization is based on ontoiogical engineering to enable people to build knowledge bases as a result of knowledge systematization. The basic philosophy behind our enterprise is that ontoiogical engineering provides us with the basis on which we can build knowledge and with computer-interpretable vocabulary in terms of which we can describe knowledge systematically. The ontology we developed includes device ontology and functional ontology both of which play fundamental roles in understanding artifacts by a computer. Redesign of a manufacturing process through its functional understanding in industrial engineering is taken as an example task to show how to use the ontologies as well as the way of knowledge systematization of manufacturing processes. A sample ontology of a manufacturing process is also built in terms of functional ontology from device ontology point of view.

203 A Framework for Systematization of Functional Knowledge based on Ontological Engineering

Abstract: It has been recognized that functional knowledge used in conceptual design is scattered around technology and target domains. One of its reasons is that different frameworks (viewpoints) for conceptualization are used when authors describe knowledge in different domains. The other one is there are several functional concepts without clear definitions. Aiming at systematization of functional knowledge for synthesis, we discuss ontologies that guides conceptualization of artifacts from the functional point of view. We propose a device-centered ontology and a functional concept ontology. The former provides a device-centered viewpoint for capturing a target domain in order to make models or knowledge consistent. The latter provides concepts representing functions of devices which are used as a common vocabulary in functional knowledge. Some systems based on these ontologies are also mentioned.

Supporting Cooperative Consensus Formation via Ontologies

Abstract: In this paper, we propose the Discussion Board system to support the nebulous communication between the users who do not clearly express the concepts intended. This is achieved by indicating the conceptual differences between the users through the users' direct creation of the concepts as ontologies and by showing other concepts obtained from World Wide Web.

Keyword spotting using F0 contour information

Abstract: Generally, keyword spotting is based on the phonemic information. Consequently, if the input speech contains a phoneme sequence which is similar to the keyword, it may be incorrectly detected as the keyword, even though the prosodic pattern such as accent differs greatly. In order to avoid such a false alarm, this paper presents a method where not only the phonemic information, but also the F0 information, are both used to determine the likelihood of the input speech as the keyword. The F0 contour of the keyword is registered beforehand as the template. The F0 contour of the input speech and the template are compared by DP matching, and the likelihood as the keyword is evaluated by the obtained dissimilarity and the phonemic likelihood. Based on an experiment using news broadcasts, it is seen that the false alarm rate is reduced by 30 to 50%, for the equivalent detection rate. © 2001 Scripta Technica, Syst Comp Jpn, 32(7): 52–61, 2001

A knowledge management support environment based on dual loop model

Abstract: It is necessary that the relations between individuals and organizations be properly understood and support learning for knowledge inheritance, and encourage the creation, the spreading and inheriting of new knowledge. This paper presents an framework of information systems for knowledge management focused on a learning system in organization. Major characteristics of this framework are derived from “Dual loop model” which represents the flow of knowledge in an organization. Since these two things enable the framework to grasp the meaning of knowledge and the progress of organizational learning, it can provides appropriate support for knowledge management Then we will introduce two systems as concrete examples, namely a knowledge management support environment: Kfarm.

A sales agent for website personalization

Abstract: In this paper we present the design and prototype implementation of a real-time, adaptive, ontology-based sales agent and recommendation engine. It supports the personalization of Internet services by taking into account product knowledge, sales expertise and customer preferences. Using a hybrid of ontology engineering and machine learning techniques, the sales agent resolves the start-up and knowledge management problems inherent in other web personalization technologies. It provides for the dynamic adaptation of customer profiles based on behavioral data and domain knowledge models. This approach is domain-independent and applicable to a wide-variety of web commerce and services sites.