• Journal of Information Science Theory and Practice
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• v.6 no.3
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• pp.6-15
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• 2018

With the increasing demand for intelligent services, knowledge graph technologies have attracted much attention. Various application-specific knowledge bases have been developed in industry and academia. In particular, open knowledge bases play an important role for constructing a new knowledge base by serving as a reference data source. However, identifying the same entities among heterogeneous knowledge sources is not trivial. This study focuses on extracting and determining exact and precise entities, which is essential for merging and fusing various knowledge sources. To achieve this, several algorithms for extracting the same entities are proposed and then their performance is evaluated using real-world knowledge sources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.505-511
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• 2003

Production rule based knowledge representation method has many advantages, but has the difficulties in maintaining the consistency of knowledge. Since the consistency maintenance of knowledge exercises a marked effect on the reliability of inference results, the system for consistency maintenance of knowledge is indispensable to increase the reliability. In the most popular pairwise checking method among consistency verification methods, the valuable rules can be omitted and it takes much time in checking the consistency when the rules are numerous. So, this paper is to propose and implement the verification system which can remove the structural errors and semantic ones, making up for the defects of pairwise checking method by using the certain property list and eventual property list and improving the steps of verification.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.2
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• pp.65-80
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• 2024

This paper aims to determine the impact of knowledge creation and sharing on use by librarians in federal universities in Nigeria. The study adopted the survey method of the correlation type, where 518 librarians were surveyed through a close-ended questionnaire, descriptive statistics, Pearson Product Moment Correlation coefficient and multiple regression were used to test the hypotheses. Findings indicate that significant relationships exist between knowledge creation and knowledge sharing (r=0.52), knowledge sharing and knowledge use (r=0.63) and knowledge creation and knowledge use (r=0.52), respectively. Knowledge created and shared by the librarians is used for better work performance. The paper adds value to the existing body of knowledge by proposing the need to understand the importance of knowledge creation and sharing as facilitators of knowledge use by librarians. This will, in turn, enhance service delivery to library patrons, thereby improving library patronage. This paper is limited to knowledge creation and sharing and its impact on knowledge use. Types of knowledge created and channels through which knowledge is shared are outside the focus of this article.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.277-301
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• 2003

Knowledge Management is presented as the management method having the survival and competition of enterprise under variously changing management environment. Knowledge Management is introduced recently as successful survival strategy of advanced enterprise. Scientifically, however, the definition. study model and propulsion method of KM leave much to be desired, and now it has become the subjects of active study among scholars. To creation of the competitive power of the company have to be important knowledge based continuous innovation. That is to say, the knowledge management means a lot to knowledge assets included knowledge, knowledge creating, sharing, and acting This study aims to propose models on the research result based a case study of the financial industry in knowledge creating processes(SECI) and deriving knowledge management styles in the flied works of the bank.

• Knowledge Management Research
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• v.16 no.4
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• pp.169-190
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• 2015

The status of small and medium-sized enterprises has been changed into more independent business entities rather than simply subcontractor so that the utilization of specialized knowledge has been much more necessary for the survival in the market. However, small and medium-sized enterprises, it is difficult to sufficient investment in knowledge services due to limited resources relative to large enterprises and demand for knowledge services business of government support is growing. For this reason, it is important to measure accurately the demand for knowledge services of small and medium-sized enterprises in knowledge management for effective utilization of knowledge service. In this study, we analyzed previous studies on small and medium-sized enterprises knowledge services that can be utilized in a comprehensive way. As a result, we developed knowledge service needs assessment model based on five critical success factors for continual growth and 12 types of knowledge service. This model has been modified and supplemented through expert meeting using delphi research method and topic modeling analysis using secondary data. This study is attempted to appropriately measure necessary knowledge services for small and medium-sized enterprises so that generated the evaluation model of knowledge service demands, comprehensively dealing with core knowledge services for many kinds of business entities. It is expected that the developed model will be a useful tool to understand and evaluate knowledge services demands of enterprises.

• Knowledge Management Research
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• v.10 no.4
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• pp.75-91
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• 2009

Many prior researchers have repeatedly emphasized the importance of utilizing external knowledge as a critical factor for the success of organizational innovation. But they seem to have ignored the importance of the practical methods to advance the ability of finding new way of applying external knowledge to innovation activities. This paper suggests the exploitation of firm's knowledge base in innovative way as a practical method to utilize external knowledge for organizational innovation, because it could be possible to find out a common factor in external knowledge with organizational knowledge base by exploiting it. According to the empirical test with data of 1,143 manufacturing firms, all of the hypothesis were strongly supported.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.44-51
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• 2005

Water flow in rivers during flood season can be 10 to 100 fold higher than normal seasons (low precipitation) in Japan and predicting flood runoff is essential for operating reservoirs with discharging gates. Abundant experiences and knowledge are requisites for operators to be able to make efficient decisions at work. This research investigated a method to transfer technical knowledge by acquiring skills and knowledge from actual dam operators and by using the information to construct an educational training system. The purpose of the research was to enable the execution of a secure and rational reservoir operation during flood period. The educational training system for reservoir operation was developed with the focuses on acquiring knowledge on hydraulics and hydrology and learning about decision making related to the reservoir operation as well as the timing of control. The system is capable of conducting education that corresponds to individual levels in each location. Of the educational training methods, a lecture method that uses textbooks is effective for the understanding of basic knowledge and concepts while a training method that uses a simulation device is essential for the practice of advanced and specialized procedures in specific fields. Simulation devices are used in operational training for airplane flight and driving cars and trains. The educational system presented here was designed to provide further assistance to those who have acquired basic knowledge and concepts through textbooks and also to at low them to perform the satisfactory operation of dam equipment. Our research proposes a method which can realize a system to acquire technical skills-the skills which are the foundation of technical knowledge and operation.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.193-202
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• 2017

Development of Intelligent Systems involves effective integration of large-scaled knowledge processing and understanding, human-machine interaction, and intelligent services. Especially, in our project for development of a self-growing knowledge-based system with inference methodologies utilizing the big data technology, we are building a platform called WiseKB as the central knowledge base for storing massive amount of knowledge and enabling question-answering by inferences. WiseKB thus requires an effective methodology to analyze diverse requirements convoluted with the integration of various components of knowledge representation, resource management, knowledge storing, complex hybrid inference, and knowledge learning, In this paper, we propose an integrated requirement analysis method that blends the traditional sequential method and the iterative-incremental method to achieve an efficient requirement analysis for large-scale systems.