• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.286-288
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• 2001

본 논문에서는 기존에 개발된 데이터베이스내의 데이터 소스를 공유하기 위해 통합 라이브러리를 구축하고 이를 통해 데이터베이스내의 소스 데이터에 대한 의미적인 통합을 위한 구조를 제안한다. 데이터의 의미적인 통합을 위해 데이터 스키마가 가지고 있는 실질적인 의미값을 기존의 각 데이터베이스 시스템에서 참조하고 데이터 공유에 대한 동의를 갖도록 메타 데이터를 이용하였다. 따라서 먼저 통합 라이브러리를 구축하여 메타 데이터에 대한 정보와 메타 데이터간에 존재하는 함수적인 관계를 변경 함수로 함께 구현하였으며. 데이터 통합을 위한 참고 자료는 각 시스템마다 온톨로지(Ontology)를 작성하여 활용하였다. 본 논문에서 제안하는 방법은 기존의 방법에 비해 메타데이터의 수정과 주가가 간단하며 새로운 시스템을 통합 라이브러리로 합병하는 비용이 현저히 감소되었다.

• Korean Journal of Computational Design and Engineering
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• v.20 no.2
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• pp.145-158
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• 2015

ISO 15926 is an international standard for the sharing and integration of plant lifecycle information. Plant design data consist of logical configuration, equipment specifications, 2D piping and instrument diagrams (P&IDs), and 3D plant models (shape data). Although 3D computer-aided design (CAD) data is very important data across the plant lifecycle, few studies on the exchange of 3D CAD data using ISO 15926 have been conducted so far. For this, we analyze information requirements regarding plant 3D design in the process industry. Based on the analysis, ISO 15926 templates are defined for the representation of constructive solid geometry (CSG) - based 3D design data. Since system environments for 3D CAD modeling and Semantic Web technologies are different from each other, we present system architecture for processing and visualizing plant 3D design data in the Web Ontology Language (OWL) format. Through the visualization test of ISO 15926-based 3D design data for equipment with a prototype system, feasibility of the proposed method is verified.

• 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 Information Management
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• v.37 no.2
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• pp.1-20
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• 2006

It has lately been recognized that the sharing and exchanging of the research results information is the critical factor to improve the research productivity. So many institutions are planning or developing the information systems which provide the research information services for researcher. But it has very difficulty in integrating the research resources information due to the dispersion and heterogeneity in data sources, and semantic and structural difference in describing data. We propose the semantic web based methodology and conceptual framework for raising the interoperability of metadata about research results information, which will support the integration of the distributed research data for information services in the end. We first introduce the ontology which is developed based on Standard Metadata of Research Results Information published by STISC. Then to show the applicability in real-world environment, we express the metadata of research information in RDF/RDFS according to ontology. Finally we proposed the conceptual architecture of research information service system which shows the main components, the functional requirements, and the principal and design direction at implementing the system.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.213-216
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• 2005

W3C에서는 온톨로지의 표준 언어로 OWL(Web Ontology Language)을 발표하였고 이를 활용한 온톨로지가 다양한 곳에 적용되어 구축이 되고 있다. 하지만, DL(Description Logic)기반인 OWL언어가 표현할 수 있는 규칙의 한계로 인하여 이를 확장하기 위한 연구가 활발히 진행되고 있다. 이러한 연구를 통하여 W3C에서는 OWL과 RuleML(Rule Markup Language)을 통합하여 규칙(Rule)에 대한 표현력이 더욱 향상된 SWRL(Semantic Web Rule Language) 언어를 제안하였다. 현재 이러한 연구는 OWL, SWRL 온톨로지 언어를 활용하고 Racer, Jess와 같은 엔진을 통하여 추론을 하는 형태로 활성화 되어 가고 있다. 하지만 이러한 형태로 온톨로지를 구축하는데 있어서 Racer를 이용한 DL추론, Jess를 이용한 Rule-base추론이 병행되고 있다. 이에 따라 본 논문에서는 온톨로지를 추론하기 위한 엔진으로 Racer와 Jess의 병행이 아닌, Jess를 이용하여 DL기반언어인 OR온톨로지를 추론하는 것 뿐 만 아니라 SWRL언어의 규칙 또한 추론할 수 있도록 한다. 이러한 시스템을 구축하기 위해 OWL을 Jess언어를 이용하여 추론할 수 있도록 개발된 OWLJessKB라는 툴과 SWRL언어를 추론하기 위해 Jess언어로 변환하여 이를 추론하는 SWRL Factory, 그리고 이출 이용하여 통합 추론하기 위한 세가지 통합 추론 플랫폼을 제안한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.237-244
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• 2006

시맨틱 웹 관련연구가 증가함에 따라 하나의 관련분야로 규칙기반 시스템 동의 지능적인 웹 환경에 대한 기대 역시 커지고 있다. 하지만 규칙기반 시스템을 활용하기에는 아직도 규칙습득이 많은 제약이 되고 있다. 규칙습득은 웹으로부터 필요한 규칙을 습득하는 일련의 방법인데, 이러한 규칙을 습득하기 위해서는 규칙구성요소를 먼저 식별해야만 한다. 그러나 이러한 규칙을 식별하는 작업은 대부분 지식관리자의 수작업에 의해 이루어지고 있다. 본 연구의 목적은 웹으로부터 규칙구성요소 식별을 최대한 자동화하고 지식관리자의 수작업을 최소화함으로써 그 부담을 줄여 주는 데 있다. 이러한 방법으로는 온톨로지를 근간으로 하여 웹 페이지와의 문자열 비교, 이러한 비교의 한계를 극복하기 위한 확장등의 방법이 있다. 첫 번째 방법은 온툴로지 기반으로 규칙식별 할 웹 페이지와 비교를 통해 지식관리자의 규칙식별 과정을 최대한 자동화하여 주는 것이다. 여기서 만약 현재 규칙을 식별하고자 하는 웹 사이트와 유사한 시스템의 규칙들을 활용하여 일반화 된 온툴로지가 구축되었다면, 이 온톨로지를 기반으로 규칙을 식별하고자 하는 웹사이트와의 비교를 통해 규칙구성요소를 자동화하여 추출 할 수 있다. 이러한 온툴로지를 기반으로 규칙을 식별하기 위해서는 문자열 비교 기법을 사용하게 된다. 하지만 단순한 문자열 비교 기법만으로는 규칙을 식별하는 데에 자연어 처리에 대한 한계가 있다. 이를 극복하기 위해 다음의 두 번째 방법을 사용하고자 한다. 두 번째 방법은 정형화되지 않은 정보들을 확장하여 사용하는 것이다. 우선 찾고자 하는 단어들의 원형을 찾기 위한 스테밍 알고리즘 기법, WordNet을 이용하여 동의어 유의어등으로 확장을 하는 WordNet Expansion 기법, 의미 유사도를 측정하기 위한 방법인 Semantic Similarity Measure 등을 단계적으로 수행하여 자동화되고 정확한 규칙식별을 하고자 한다. 이러한 방법들의 조합으로 인하여 규칙구성요소 추출이 되지 않을 후보 단어들의 수를 줄여서 보다 더 정확하고, 지능적인 규칙구성요소 추출 방법론을 제시하고 구현하여 지식관리자의 규칙습득에 대한 부담을 줄여 주고자 한다.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.115-126
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• 2012

Mashups have become very popular over the last few years, and their use also varies for IT convergency services. In spite of their popularity, there are several challenging issues when combining Open APIs into mashups, First, since portal sites may have a large number of APIs available for mashups, manually searching and finding compatible APIs can be a tedious and time-consuming task. Second, none of the existing portal sites provides a way to leverage semantic techniques that have been developed to assist users in locating and integrating APIs like those seen in traditional SOAP-based web services. Third, although suitable APIs have been discovered, the integration of these APIs is required for in-depth programming knowledge. To solve these issues, we first show that existing techniques and algorithms used for finding and matching SOAP-based web services can be reused, with only minor changes. Next, we show how the characteristics of APIs can be syntactically defined and semantically described, and how to use the syntactic and semantic descriptions to aid the easy discovery and composition of Open APIs. Finally, we propose a goal-directed interactive approach for the dynamic composition of APIs, where the final mashup is gradually generated by a forward chaining of APIs. At each step, a new API is added to the composition.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.618-632
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• 2005

A framework for semantic annotation oi human motion sequences is proposed in this paper. Motion capture technology is widely used for manuiacturing animation since it produces high qualify character motion similar to the actual motion of the human body. However, motion capture has a significant weakness due to the lack of an industry wide standard for archiving and retrieving motion capture data. It is difficult for animators to retrieve the desired motion sequences from motion capture files as there is no semantic annotation on already captured motion data. Our goal is to improve the reusability of motion capture data. To archive our goal first, we propose a standard format for integrating different motion capture file formals. Our standard format is called MCML (Motion Capture Markup Language). It is a markup language based on XML (extensible Markup Language). The purpose of MCML is not only to facilitate the conversion or integration of different formats, but also to allow for greater reusability of motion capture data, through the construction of a motion database storing the MCML documents Second, we define motion ontologies that are used to annotate and semantically organize human motion sequences. This ontology-based approach provides the means for discovering and exploiting the information and knowledge surrounding motion capture data.