• International conference on construction engineering and project management
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• 2024.07a
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• pp.566-569
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• 2024

Clashes between architectural, structural, and mechanical, electrical, and plumbing (MEP) systems are unavoidable as each discipline typically develops its own BIM models prior to federation. Commercial model checkers identify these clashes but do not classify them with respect to their severity, requiring every clash to be evaluated manually by the parties involved. Moreover, the assessment of their severity can be subjective and open to misinterpretations. To address these inefficiencies, an ontological approach was employed exclusively for clashes between multi-disciplinary BIM models. For a given clash, the ontology linked two elements, and encompassed their relevant geometric data and topology, which were retrieved using Navisworks and Python mesh packages. The clashes, distinguished as hard and soft, used separate approaches to classify their severity. Hard clashes employed machine learning algorithms to infer their severity based on geometric and project type features. Soft clashes used SPARQL-based rules which have predefined conditions for distinguishing clash severity based on semantic, geometric, and topological features. The ontology was implemented using RDF/OWL standards and programmed in Navisworks as an add-in module. Validation performed on an actual BIM model with 18,887 number of clashes showed that the ontology enabled highly accurate clash severity detection for both hard and soft clashes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.4869-4874
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• 2011

In this paper, we defined constructivism and ontology theory and associate it in Location Based Service ubiquitous learning. And this paper aims to provide a clear vision about location based service (LBS) ubiquitous learning. The typical ubiquitous learning involving the Context Aware Intelligent system was presented. Also the Architecture for learning environment including the key idea and technical concept is being presented in this paper. Guided with these principles and with the advancement of information and communication technology the context-awareness based on Artificial intelligence for Location based Service for ubiquitous Learning was conceptualized. U-learning for Location Based Service is presented here and the concept behind this new learning paradigm. The learning environment architecture which comprises the entire component is illustrated here.

• The Journal of Korean Association of Computer Education
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• v.14 no.1
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• pp.159-168
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• 2011

This study analyzed whether the ontology-based information-searching model affected the ability of students to effectively search for meaningful information to carry out their projects. The experiment results illustrated that the amount of relevant information sought by the ontology-based information retrieval (OIR) method was significantly greater than that of the existing information retrieval (EIR) method. In addition, the relevance rate of the bookmarked documents sought by the OIR method was significantly greater than that of the EIR method. Interviews showed that the OIR model was helpful for students to effectively find information and thus, it helped them to complete the project more easily. Furthermore, the OIR model was beneficial for them to understand the subordinate concepts and their relationships for an important learning concept. The results of this study indicate that the OIR model could be used as a supplementary learning tool for project-based learning.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.487-494
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• 2010

One of the major issues in the field of corporate training and formal education is the support of personalized learning. Successful personalized learning needs the availability of the relevant learning contents at just-in-time for learners each. The competency is one of personal characteristics. So competency-based learning is one of the methods that fulfill the above need. Successful competency-based learning needs the method that recommends the relevant contents for the user's deficient competency based on the user's current competency and objectives. We assume that there exists a student information system that provides each user's competences and objectives as fields of a LIP/ePortfolio-compliant student information. This paper proposes an ontology-based system that, given the user's competences and objectives from the above student informaton system, recommends the relevant contents among a large number of educational contents using competency ontology and domain ontology. The advantage of this system can easily handle the change of competency map and terms related with competences in student information and education contents.

• The Journal of Information Systems
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• v.30 no.1
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• pp.65-84
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• 2021

Purpose Based on the development of artificial intelligence and big data technologies, the RegTech has been emerged to reduce regulatory costs and to enable efficient supervision by regulatory bodies. The word RegTech is a combination of regulation and technology, which means using the technological methods to facilitate the implementation of regulations and to make efficient surveillance and supervision of regulations. The purpose of this study is to describe the recent adoption of RegTech and to provide basic examples of applying RegTech to capital market regulations. Design/methodology/approach English-based ontology and deep learning technologies are quite developed in practice, and it will not be difficult to expand it to European or Latin American languages that are grammatically similar to English. However, it is not easy to use it in most Asian languages such as Korean, which have different grammatical rules. In addition, in the early stages of adoption, companies, financial institutions and regulators will not be familiar with this machine-based reporting system. There is a need to establish an ecosystem which facilitates the adoption of RegTech by consulting and supporting the stakeholders. In this paper, we provide a simple example that shows a procedure of applying RegTech to recognize and interpret Korean language-based capital market regulations. Specifically, we present the process of converting sentences in regulations into a meta-language through the morpheme analyses. We next conduct deep learning analyses to determine whether a regulatory sentence exists in each regulatory paragraph. Findings This study illustrates the applicability of RegTech-based ontology and deep learning technologies in Korean-based capital market regulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

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

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.19 no.1
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• pp.19-36
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• 2008

The advantage of ontology leads a new knowledge system through integrating existing knowledge system and descriptive element of the concept. This study based on the advantage of ontology, providing a modeling education ontology that considered educational circumstances and related objects-person, organization, educational resources and so forth. Therefore, this study developed the framework for education ontology that provided link between school library and MLA to practice teaching-learning activity, these characteristics of educational ontology were as follows : the first, utilizing the existing education metadata and ontology, the second, representing a concept of educational ontology, subsequently defining classes and properties of education domain, the third, adding new classes and properties to connect existing classes and properties.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.83-91
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• 2011

Much research has been conducted on the e-learning systems for recommending a learning content to a student based on the difficulty of it. The difficulty is one of the most important factors for selecting a learning content. In the existing learning content recommendation systems, the difficulty of a learning content is determined by the creator. Therefore, it is not easy to apply a standard rule to the difficulty as it is determined by a subjective method. In this paper, we propose an ontology-based method for determining the difficulty of a learning content in order to provide an objective measurement. Previously, ontologies and knowledge maps have been used to recommend a learning content. However, their methods have the same problem because the difficulty is also determined by the creator. In this research, we use an ontology representing the IS-A relationships between words. The difficulty of a learning content is the sum of the weighted path lengths of the words in the learning content. By using this kind of difficulty, we can provide an objective measurement and recommend the proper learning content most suitable for the student's current level.