The purpose of this research is to show the people's character of Japanese prefectures\nbased on Social Network Service. By using data that shows personalities based on participation\nof communities, we analyze communities participants' identities as the people's characters of each\nJapanese prefectures. Data is birthplace communities of Japanese prefectures. We analyze with following\nsteps, data cleaning, calculating coefficient of specialization, clustering with Self-Organizing\nMaps and network analysis. With these analysis, we find various characters of Japanese prefectures\nand clarify its properties.

We address the task of summarizing numerous short documents on microblogs including\nTwitter. On microblogs, thousands of short documents on a certain topic such as sports games\nor TV dramas are posted by users. Noticeable characteristics of microblog data are that documents\nare often very highly redundant and are aligned on timeline. There can be dozens of documents\non one event in the topic. Two very similar documents will refer to two distinct events when the\ndocuments are temporally distant. We examine the microblog data to gain more understanding\nof those characteristics, and propose a summarization model for numerous short documents on\ntimeline, along with an approximate fast algorithm for generating summary. We empirically show\nthat our model generates a good summary on the dataset of microblog documents on sports games.

The purpose of this paper is to propose a semantic database of Bunraku-puppet movements in\norder to analyze the meanings of movements by Bunraku-puppets. We started to construct a database\ncontaining the movies, movement analysis data and the meanings of the motions of Bunraku puppets. We\nhave a plan to create a system to offer the explanation for intentions of performances by Bunraku\npuppeteers.

Recently, human kansei is one of the important factors in product design. Several\nmehods to extract knowledge about impressions have been developed in the field of architectonics\nand kansei engineering. However, few studies have addressed management of such the knowledge,\nand no methods to be commonly used in the management have been established. Thus, our study\nproposes and implements a knowledge management method that can effectively provide knowledge\nof impressions. We introduce a framework for descriptions of impressions explained by perceptual\nfluency which can serve as an useful indicator of a pleasure. Since perceptions are closely related\nto awareness, we model a perception as well as awareness and a self-report for the framework\nbased on an ontology development environment Hozo and a top-level ontology YAMATO. We then\ninstantiate a case where a person has a good impression of a web page, and we describe relations\nbetween a perception and stimulus in such the case. Our approach demonstrates that ontological\nmodeling of impressions helps us to understand correspondences between affections and physical\nirritations.

Computational simulations play an important role in understanding of biological phenomena.\nHowever, it is difficult to understand mechanisms of biological phenomena based on only\nquantitative data empirically obtained, because the biological phenomena are controlled by factors\nmore complicated than those of physical phenomena. Therefore, it is indispensable in exploring\nbiological phenomena to construct computational simulations through reasoning from qualitative\nknowledge stored in literature. To do so, we have to translate the literature knowledge described\nin natural languages into knowledge representation available for computers with an ontology.To\nestablish a method to construct a simulation by an ontology, our study aims to introduce concepts\nof time for qualitative descriptions in the ontology. For implementation of the ontological simulation,\nwe incorporate concepts of time into knowledge descriptions of the ontology. We introduce a\nmethodology of qualitative physics to represent the concepts of time, which enables to understand\ncausal relationships and temporal intervals between concepts in a for each specific context.

In this paper, we propose a prototype system for community-driven loose ontology\nengineering using tagging and suggestion. Ontologies are key parts to realize the Semantic Web\nand CGM (Consumer Generated Media) is one of the most important information resources. So,\nit is important to support constructing ontologies for not only individuals or small teams but also\nmany participants. Our proposed system based on "property tagging" and "property suggestion"\ncan be used as an ontology development platform, reducing entry barriers for the participations in\nthe creation and maintenance of ontologies.

Recently, ontologies are being constructed in various technical domains. The quality of an ontology is one of the important factors that determines its utility. In order to assure its quality, not only form-based evaluation as to whether the ontology being constructed is written properly in terms of its form (syntax), but also content-based evaluation as to whether the ontology properly represents the target domain, whether the ontology actually serves for problem solving, etc. is necessary. In this study, we consider the quality assurance of ontologies in Hozo, which is an environment for building/using ontologies that is being developed by the authors. As to form-based evaluation, Hozo provides various assistance functions for properly editing an ontology in compliance with several guidelines and rules. As to content-based evaluation, Hozo introduces a method for supporting ontology evaluation thorough conceptual maps which are generated from the ontology according to the user's viewpoints.