• The Journal of the Korea Contents Association
• /
• v.9 no.6
• /
• pp.45-52
• /
• 2009

Recently, we started a project development of the digital expert system for the product design supporting in manufacturing industry. This digital expert system is used to the engineers in manufacturing industry for the process control, production management and system management. In this paper, we develop the ontology based inference engine shell for building of expert system. This expert system shell included a various functions which of Korean language supporting, graphical ontology map modeling interface, fuzzy rule definition function and etc. And, we introduce the knowledge representation method for the ontology map building and ontology based fuzzy inferencing method.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.73-74
• /
• 2006

Design repository is considered an effective method to manage a set of heterogeneous design knowledge. In this paper, methodologies for modeling and managing different types of design knowledge - ontology for mold design task as well as mold components, rule bases, and library containing standard parts, material property, molding condition, etc. - in a design repository are described.

• Journal of Internet Computing and Services
• /
• v.11 no.4
• /
• pp.107-117
• /
• 2010

The feature models representing the common and variable concepts among the software products and the feature configurations generated by selecting the features to be included in the target product are the essential components in the software product lines methodology. Although the researches on the formal semantics and reasoning of the feature models and feature configurations are in progress, the researches on feature model ontologies and feature configuration validation using the semantic web technologies are yet insufficient. This paper defines the formal semantics of the feature models and proposes a feature configuration validation technique based on ontology and semantic web technologies. OWL(Web Ontology Language), a semantic web standard language, is used to represent the knowledge in the feature models and the feature configurations. SWRL(Semantic Web Rule Language), a semantic web rule languages, is used to define the rules to validate the feature configurations. The approach in this paper provides the formal semantic of the feature models, automates the validation of feature configurations, and enables the application of various semantic web technologies, such as SQWRL.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1238-1245
• /
• 2024

The construction industry faces the challenge of providing effective, engaging, and rule-specific safety learning. Traditional methodologies exhibit limited adaptability to technological advancement and struggle to deliver optimal learning experiences. Recently, there has been widespread adoption of information retrieval and ontology-based chatbots, as well as content delivery methods, for safety learning and education. However, existing information and content retrieval methods often struggle with accessing and presenting relevant safety learning materials efficiently. Additionally, the rigid and complex structures of ontology-based approaches pose obstacles in accommodating dynamic content and scaling for large datasets. They require more computational resources for ontology management. To address these limitations, this paper introduces iSafe Chatbot, a novel framework for construction safety learning. Leveraging Natural Language Processing (NLP) and Large Language Model (LLM), iSafe Chatbot aids safety learning by dynamically retrieving and interpreting relevant Occupational Safety and Health Administration (OSHA) rules from the comprehensive safety regulation database. When a user submits a query, iSafe Chatbot identifies relevant regulations and employs LLM techniques to provide clear explanations with practical examples. Furthermore, based on the user's query and context, iSafe Chatbot recommends training video content from video database, enhancing comprehension and engagement. Through advanced NLP, LLM, and video content delivery, iSafe Chatbot promises to revolutionize safety learning in construction, providing an effective, engaging, and rule-specific experience. Preliminary tests have demonstrated the potential of the iSafe Chatbot. This framework addresses challenges in accessing safety materials and aims to enhance knowledge and adherence to safety protocols within the industry.

• Journal of KIISE
• /
• v.43 no.3
• /
• pp.370-379
• /
• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.9
• /
• pp.904-917
• /
• 2010

Much research on ontology has been done during the last decade in order to represent knowledge and connect data semantically in AI and Semantic Web areas. However, ontologies might be represented and defined in different ways depending on knowledge and intention of users. It causes heterogeneity problem that the same concept can be differently expressed. This paper introduces a XOB (XMDR-based Ontology Builder) system based on XMDR to resolve the problem. XOB creates ontologies by reusing classes and relations defined in XMDR. XOB therefore is able to either solve or minimize the heterogeneity problem among ontologies. This paper introduces the conceptual model and overall architecture of the proposed system XOB. This paper defines the process, algorithm, ontology generation rule that is required to create ontologies by using concepts registered in XMDR. Our proposal supports higher standardization than the previous approaches, and it provides many advantages such as consistent concept usage, easy semantic exchange, and so on. Therefore, XOB enables high-quality ontology creation and reduces cost for ontology integration and system development.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.222-229
• /
• 2022

Although stochastic programming and feedback control approaches could efficiently mitigate the overdue risks caused by inherent uncertainties in ground conditions, the lack of formal representations of planners' rationales for resource allocation still prevents planners from applying these approaches due to the inability to consider comprehensive resource allocation policies for hard rock tunnel projects. To overcome the limitations, the authors developed an ontology that represents the project duration estimation rationales, considering the impacts of ground conditions, excavation methods, project states, resources (i.e., given equipment fleet), and resource allocation policies (RAPs). This ontology consists of 5 main classes with 22 subclasses. It enables planners to explicitly and comprehensively represent the necessary information to rapidly and consistently estimate the excavation durations during construction. 10 rule sets (i.e., policies) are considered and categorized into two types: non-progress-related and progress-related policies. In order to provide simplified information about the remaining durations of phases for progress-related policies, the ontology also represents encoding principles. The estimation of excavation schedules is carried out based on a hypothetical example considering two types of policies. The estimation results reveal the feasibility, potential for flexibility, and comprehensiveness of the developed ontology. Further research to improve the duration estimation methodology is warranted.

• Annual Conference of KIPS
• /
• 2004.11a
• /
• pp.509-512
• /
• 2004

본 논문에서는 지능형 e-비즈니스의 효과적인 수행을 지원하기 위하여 서비스 온톨로지 표현 언어 OWL-S와 규칙표현 언어 RuleML 사이의 관계를 살펴봄으로써 서비스 온토롤지 및 규칙 표현 모두가 함께 사용될 수 있는 통합모델의 이론적인 근간을 제공한다. 이를 위하여 OWL-S의 정형 시맨틱스를 기술하고 로직 프로그램과의 관계를 분석한 후 두 마크업 표현의 상호매핑을 보인다.

• Journal of Advanced Navigation Technology
• /
• v.14 no.1
• /
• pp.108-118
• /
• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• Journal of Information Science Theory and Practice
• /
• v.7 no.4
• /
• pp.56-64
• /
• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.