In recent automated and integrated manufacturing, so-called intelligence skill is becoming more and more important and its efficient transfer to next-generation engineers is one of the urgent issues. In this paper, we propose a new approach without costly OJT (on-the-job training), that is, combinational usage of a domain ontology, a rule ontology and a rule-based system. Intelligence skill can be decomposed into pieces of simple engineering rules. A rule ontology consists of these engineering rules as primitives and the semantic relations among them. A domain ontology consists of technical terms in the engineering rules and the semantic relations among them. A rule ontology helps novices get the total picture of the intelligence skill and a domain ontology helps them understand the exact meanings of the engineering rules. A rule-based system helps domain experts externalize their tacit intelligence skill to ontologies and also helps novices internalize them. As a case study, we applied our proposal to some actual job at a remote control and maintenance office of hydroelectric power stations in Tokyo Electric Power Co., Inc. We also did an evaluation experiment for this case study and the result supports our proposal.

In this paper, we propose an interactive domain ontology development environment called DODDLE-OWL. DODDLE-OWL refers to existing ontologies and supports the semi-automatic construction of taxonomic and other relationships in domain ontologies from documents. Integrating several modules, DODDLE-OWL is a practical and interactive domain ontology development environment. In order to evaluate the efficiency of DODDLE-OWL, we compared DODDLE-OWL with popular manual-building method. In order to evaluate the scalability of DODDLE-OWL, we constructed a large sized ontology over 34,000 concepts in the field of rocket operation using DODDLE-OWL. Through the above evaluation, we confirmed the efficiency and the scalability of DODDLE-OWL. Currently, DODDLE-OWL is open source software in Java and has 100 and more users from 20 and more countries.

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.

We propose a tool to manage several sorts of relationships among RDF (Resource Description Framework) and RDFS (RDF Schema). Our tool consists of three main functions: graphical editing of RDF descriptions, graphical editing of RDFS descriptions, and meta-model management facilities. In this paper, we focus on the meta-model management, a key concept which is defined as the appropriate management of the correspondence between a model and its meta-model: especially, the class and property in the meta-model, and the type of RDF resource and property in the model. The above facilities are implemented based on the plug-in system. We provide basic plug-in modules for checking the consistency of RDFS classes and properties. The prototyping tool, called MR3 (Meta-Model Management based on RDFs Revision Reflection), is implemented by Java language. Through an experiment using MR3, we show how MR3 contributes to the Semantic Web paradigm from the standpoint of RDFs description management.

In order to build up a process-centered software engineering environment using ontologies, we present a methodology to manually construct the following ontologies: an object ontology constructed based on constituent elements to make up objects (products), and a process ontology constructed based on the relationships between inputs and outputs. Afterwards, using the constructed ontologies, the environment generates software process plans good for user queries, with both user interaction and constraints satisfaction by the Generate and Test paradigm. Furthermore, case studies show us that the environment works well in generating software process plans good for a query about the intermediate stage of development, between basic design and detailed design.

In a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system.