In order to prepare the health care industry for an increasingly aging society, a ubiquitous health care infrastructure is certainly needed. In a ubiquitous computing environment, it is important that all applications and middleware should be executed on an embedded system. To provide personalized health care services to users anywhere and anytime, a context-aware framework should convert low-level context to high-level context. Therefore, ontology and rules were used in this research to convert low-level context to high-level context. In this paper, we propose context modeling and context reasoning in a context-aware framework which is executed on an embedded wearable system in a ubiquitous computing environment for U-HealthCare. The objective of this research is the development of the standard ontology foundation for health care services and context modeling. A system for knowledge inference technology and intelligent service deduction is also developed in order to recognize a situation and provide customized health care service. Additionally, the context-aware framework was tested experimentally.

In this paper, we propose an efficient resource discovery scheme for large-scale ubiquitous computing environments, which supports scalable semantic searches and load balancing among resource discovery resolvers. Here, the resources are described based on the concepts defined in the ontological hierarchy. To semantically search the resources in a scalable manner, we propose a semantic vector space and semantic resource discovery network in which the resources are organized based on their respective semantic distances. Most importantly, landmarks are introduced for the first time to reduce the dimensionality of the vector space. Computer simulation with CAN verifies the effectiveness of the proposed scheme.

In this paper, we present a novel method to incorporate metadata into data mining. The method has many advantages. It can be completed automatically and is independent of a specific database. Firstly, we convert metadata into ontology. Then input a rule set to a reasoner, which supports rule-based inference over the ontology model. The outputs of the reasoner describe the prior knowledge in metadata. Finally, incorporate the prior knowledge into data mining.

Despite the importance of domain-specific resource construction for domain ontology development, few studies have sought to develop a method for automatically identifying domain ontology-relevant web pages. To address this situation, here we propose a web page filtering scheme for domain ontology that identifies domain-relevant web pages from the web based on the context of concepts. Testing of the proposed filtering scheme with a business domain ontology on YahooPicks web pages yielded promising filtering results that were superior to those obtained using the baseline system.

A huge amount of information is being accumulated on the Internet as the Internet usage spreads and numbers of Web pages increase. However, it is also becoming very difficult to find required information, even when the information exists. The actual value of the Web is thus much lower than its potential value. In order to solve this problem, technologies which allow machines to handle Web content in an efficient, accurate, and flexible way by using machine-readable metadata are being developed. This paper is a survey of knowledge representation on the Web, and the utilization of metadata and ontology for data integration and information sharing, with a focus on the Semantic Web concept.

In this paper, we describe how to exploit a machine-readable dictionary (MRD) and domain-specific text corpus in supporting the construction of domain ontologies that specify taxonomic and non-taxonomic relationships among given domain concepts. In building taxonomic relationships (hierarchical structure) of domain concepts, some hierarchical structure can be extracted from a MRD with marked subtrees that may be modified by a domain expert, using matching result analysis and trimmed result analysis. In building non-taxonomic relationships (specification templates) of domain concepts, we construct concept specification templates that come from pairs of concepts extracted from text corpus, using WordSpace and an association rule algorithm. A domain expert modifies taxonomic and non-taxonomic relationships later. Through case studies with "the Contracts for the International Sales of Goods (CISG)" and "XML Common Business Library (xCBL)", we make sure that our system can work to support the process of constructing domain ontologies with a MRD and text corpus.

This paper presents a method for disambiguating word senses in Korean-Japanese machine translation by using a language independent ontology. This ontology stores semantic constraints between concepts and other world knowledge, and enables a natural language processing system to resolve semantic ambiguities by making inferences with the concept network of the ontology. In order to acquire a language-independent and reasonably practical ontology in a limited time and with less manpower, we extend the existing Kadokawa thesaurus by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. The former can be obtained by converting valency information and case frames from previously-built electronic dictionaries used in machine translation. The latter can be acquired from concept co-occurrence information, which is extracted automatically from a corpus. In practical machine translation systems, our word sense disambiguation method achieved an improvement of average precision by 6.0% for Japanese analysis and by 9.2% for Korean analysis over the method without using an ontology.

The OMG have made the trading service one of the basic CORBA services. A specification has been drawn up (OMG RPF5) but seems to have some problems in terms of scalability and complexity. This paper introduces an architecture called Contextual Clustering Using Service Properties (CCUSP) that deals with issues of scalability. It uses a contextual approach to clustering object service offers based on property commonalities. It also handles issues of scalability of trader federation. An ontological approach is to be used, however not covered in this paper. An implementation of the specialisation graph context model is detailed.

We introduce an ontology for transportation systems demand forecasting and its implementation into a decision support system (DSS) generator. The term ontology, as we use it here, means a collection of building blocks necessary and sufficient to construct a skeleton of a specific DSS, that is a task ontology. The ontology is specified in constraint logic, which also ensures a good support for modularity.