• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.6-21
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• 2014

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

• Journal of Korean language education
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• v.28 no.2
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• pp.209-233
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• 2017

The purpose of this study is to analyze the pragmatic functions of '-ketun(yo)' expressed in the discourse associating with the context of communication based on the actual conversations of Korean native speakers. As discourse is closely related to the context, contextual factors surrounding the discourse should be actively considered in order to reveal the function of grammar expressed in the discourse. Also, there is need to consider the grammatical functions in terms of the linguistic user which is the subject of interaction in the discourse. Based on this necessity, in this study, we analyzed the pragmatic functions of '-ketun(yo).' As a result, '-ketun(yo)-' had a great influence on the formation and expansion of the shared context in communication contexts. The shared context is expanded through generative mutual knowledge and priori mutual knowledge. As a result of the conversation analysis, '-ketun(yo)-' was used at a high frequency in the expansion of generative mutual knowledge formation. In addition, '-ketun(yo)-' appeared to have a discourse cohesion function that binds topics with other topics. In the case that '-ketun(yo)-' is formed through priori mutual knowledge, '-ketun(yo)-' could be used as a sign to lead the union of the speaker and the listener. This study has significance in that it examines the pragmatic functions of '-ketun(yo)-' in relation to the context of communication based on actual utterance.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.2
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• pp.67-72
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• 2018

In the EPC(Engineering Procurement and Construction) project of the plant, procurement engineering has a profound effect on the profitability of the project. It is important that the procurement specifications are well written to ensure that procurement engineering works properly. In the meantime, the procurement specifications have been created by the experience of the person in charge because there was no system for helping procurement engineering. To cope with this situation, we are developing a procurement engineering management support system (PeMSS). This paper describes how to implement a knowledge-based service in the procurement engineering management support system. First, we briefly introduce the PeMSS, the knowledge base application field, and how to apply it. The parts that requires knowledge-based service are parsing the requirements in the PDF (Portable Document Format) file and management of the document provided by the supplier of the equipment.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.430-434
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• 2015

Safety education at the tertiary level prepares students to enter construction industry with adequate safety knowledge; then accidents can be prevented proactively. However, safety subject has not been paid adequate attention in universities and most institutional safety programs consider safety matters in isolation. Meanwhile, anatomical theory in the medicine field has been successfully adopted and proved potential advantageous in various scientific disciplines. With this regard, this study proposes a visualization based Building Anatomy Model (BAM) for construction safety education, which utilizes the anatomical theory in order to improve student's safety knowledge and practical skill. This BAM consists of two modules: 1) Knowledge Acquisition Module (KAM) aims to deliver safety knowledge to students through building anatomy models; 2) Practical Experience Module (PEM) where students safely perform construction activities by using the system to improve safety skill. The system trial is validated with virtual scenarios derived from real accidents cases. This study emphasizes the visualization based building anatomy model would be a powerful pedagogical method to provide effectively safety knowledge and practical skill for students, as a result, safety competence of students would be enhanced.

• Journal of East-West Nursing Research
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• v.29 no.2
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• pp.141-149
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• 2023

Purpose: The purpose of this study was to verify the effects of virtual reality (VR) based anatomy education program on nursing students' learning presence, technology acceptance, learning motivation, and knowledge. Methods: A nonequivalent control group pre-test and post-test design was employed. The study participants included 113 nursing students (56 in the experimental group and 57 in the control group) from Ansan City. Data collection was conducted from June 1 through 23. Data were analyzed using χ²-test, Fisher's exact test, and t-test using SPSS 23.0 program. Results: The experimental group had a significant increase in learning presence, technology acceptance, and knowledge before and after the intervention compared to the control group. Conclusion: Virtual reality based anatomy education is an effective learner-centered educational program. From an educational perspective, VR anatomy education programs can improve anatomy knowledge by increasing students' acceptance of VR technology and increase their motivation to learn by increasing their sense of presence.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.237-240
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• 2005

This paper describer a design expert system which determines the design values of shadow mask using Case-Based Reasoning. In Case-Based Reasoning, it is important to both retrieve similar cases and adapt the cases to meet the design specifications exactly. Especially, the difficulty in automating the adaptation process will prevent the designers from using the design expert systems efficiently and easily. This paper explains knowledge-based design support systems for shadow mask through neural network-based case adaptation. Specifically, we developed 1) representing design knowledge and 2) adaptive case-based reasoning method using RBFN (Radial Basis Function Network).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.280-292
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• 2016

This descriptive survey assessed knowledge of intensive care unit (ICU) nurses regarding compliance with infection control for six kinds of multi-drug resistant organisms to assist in development of effective intervention strategies. Participants included 210 nurses working in the ICUs of general hospitals who completed a structured questionnaire. The results showed that the nurses' knowledge level and infection control compliance was 10.54 and 3.39 for MRSA; 11.25 and 3.69 for VRE; and 9.60 and 3.49 for CRGNB, respectively[ED highlight - consider providing additional information to describe what these values indicate.]. Knowledge regarding MRSA infection control differed significantly based on age, clinical experience, and experience as a trainee, while compliance with MRSA infection control differed based on age. Knowledge regarding VRE infection control was significantly different based on academic qualification level, experience as a trainee, and whether guidelines existed, while compliance with VRE infection control differed based on academic qualification level and the presence of an isolation environment. Knowledge regarding CRGNB infection control differed significantly based on academic qualification level and experience as a trainee, while compliance with CRGNB infection control differed based on the presence of an isolation environment. Thus, intervention strategies should include education programs for enhancing ICU nurse' knowledge regarding strategies for creating isolation environments.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Journal of KIISE
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• v.43 no.5
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• pp.579-589
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• 2016

Currently, corporations have introduced the knowledge management system that utilizes knowledge effectively for practical purpose and development of core ability. However, existing knowledge systems have failed to share the knowledge content due to lack of elements that encourage the members to participate in the system. In this study, we designed a novel knowledge management system that employs the structure of social network service (SNS). More precisely, screen layout according to function and several algorithms to improve user friendliness and produce integrated knowledge content are recommended. The proposed SNS-based knowledge management system encourages the enterprise members to participate in the system to produce and share valuable knowledge contents.