• 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 Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.71-73
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• 2010

웹에서 다양한 웹 지리 지역 정보를 검색할 수 있는 시스템에 대한 요구가 증가하고 있다. 그러나 현재의 웹 검색 시스템은 사용자가 키워드로 지역 웹 문서를 검색하고 해당 웹 문서를 지도와 비교하여 공간정보를 취득하며, 다른 관련 정보를 얻기 위해서는 검색과 비교를 반복해야 하는 어려움이 있다. 따라서 본 논문에서는 비구조화 된 텍스트 웹 자원으로부터 지리정보 온툴로지(geo-ontology)를 확장할 수 있는 통합된 검색시스템을 제안한다. 이를 위해 문서의 정보에서 위치 정보를 추출하고 공간정보 위치 맵핑 기법을 적용하여 텍스트의 공간정보를 추출한다.

• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.174-178
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• 2007

온톨로지 기반 분산 정보시스템 환경에서는 시스템들 간의 정보 공유을 위해 전문가에 의한 명시적 온톨로지 매핑(Explicit Mapping)을 통해 의미적 이질성(Semantic Heterogeneity)을 해결하고 있다. 하지만, 온톨로지 매핑의 고비용성 때문에 모든 정보시스템들 간의 매핑이 이루어지기 힘들다. 따라서, 본 논문에서는 이미 존재하는 온톨로지 매핑 정보들의 재사용을 통해 존재하지 않은 온톨로지 매핑 정보를 묵시적으로 예측하고자 한다. 본 논문에서는 이와 같은 분산 환경에서의 쿼리 전송을 통한 지식 검색에 있어서의 온톨로지 매핑을 기반으로 한 적절한 쿼리 변환 방법론을 소개하고자 한다.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.435-446
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• 2007

B2B registries are information systems to register B2B related business information such as companies' profiles, business documents, business processes, and services and to provide query facilities to find information about potential business partners. Focusing on the design of the registry for B2B business processes, in this paper, a metadata ontology is designed to register B2B business processes. In practice, there are several competitive business process definition languages such as ebXML BPSS (Business Process Specification Schema), WSBPEL (Web Service Business Process Execution Language), BPMN (Business Process Modeling Notation), and so on. In order to register heterogeneous business processes based on different representation frameworks, the proposed metadata ontology consists of three layers, common metadata, language-specific metadata, and interrelationship metadata. To show the usefulness of the proposed metadata ontology, two examples which are represented by ebXML BPSS and WSBPEL respectively are described in order to show how the proposed metadata ontology is used to registry B2B business processes. To implement the proposed metadata ontology using ebXML registry, metadata mapping scheme to ebRIM (ebXML Registry Information Model) is also suggested.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.210-217
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• 2013

The development of collaboration among information systems in accordance with changes in enterprises' business environment brings about the problems of duplication of the existing business services and increase in costs of maintenance. Accordingly, Web service has been suggested as the standard of distributed computing to prevent the duplication of services within the same business domain and to attain the services that are already being utilized. But since the data needed for Web services are not standardized, it is difficult for the users to find services that meet diverse business purposes. In this paper, we construct an ontology-based collaboration system for service interoperability. The ontology can support fusion service by finding services which are existed interdependently under the distributed environment for collaboration processing. The role of the collaborative system includes development, registration and call of services based on ontology. A local systems request collaboration support through the service profile. Collaborative system supports the development of service using the service profiles, represents the semantic association between real data through system ontology, and infers relationship between instances contained in the services. Based on this, we applied the travel booking services for collaboration system. As a result, service can be managed effectively preventing collision in collaborative system, and we verify that the mapping between system is reduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.921-929
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• 2008

Recently, many researches have been conducted to solve data heterogeneity as a way for data integration. The elements of the system that we suggest are an XMDR wrapper and XMDR Repository. XMDR wrapper solves the heterogeneity of the existing system by creating the interface based on the standard information of XMDR, and performing the inter-conversion between global XMDR query and local query using mapping data on standard information and local schema. XMDR Repository are composed of XMDR which manages the mapping data on standard information and local schema, and of Proxy DB which saves the accomplished results. With XMDR wrapper and XMDR Repository, users can use the same interface, and they need not conduct repeated queries since XMDR wrapper not only solves the heterogeneity of the schema using the meta-semantic ontology of XMDR, but also considers the heterogeneity accompanying the meaning of the value through instance semantic ontology. Therefore, in this paper we suggest the grid wrapper for the solution of data heterogeneity and efficient data integration.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.149-162
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• 2014

Course guidance is a mentoring process which is performed before students register for coming classes. The course guidance plays a very important role to students in checking degree audits of students and mentoring classes which will be taken in coming semester. Also, it is intimately involved with a graduation assessment or a completion of ABEEK certification. Currently, course guidance is manually performed by some advisers at most of universities in Korea because they have no electronic systems for the course guidance. By the lack of the systems, the advisers should analyze each degree audit of students and curriculum information of their own departments. This process often causes the human error during the course guidance process due to the complexity of the process. The electronic system thus is essential to avoid the human error for the course guidance. If the relation data model-based system is applied to the mentoring process, then the problems in manual way can be solved. However, the relational data model-based systems have some limitations. Curriculums of a department and certification systems can be changed depending on a new policy of a university or surrounding environments. If the curriculums and the systems are changed, a scheme of the existing system should be changed in accordance with the variations. It is also not sufficient to provide semantic search due to the difficulty of extracting semantic relationships between subjects. In this paper, we model a course mentoring ontology based on the analysis of a curriculum of computer science department, a structure of degree audit, and ABEEK certification. Ontology-based course guidance system is also proposed to overcome the limitation of the existing methods and to provide the effectiveness of course mentoring process for both of advisors and students. In the proposed system, all data of the system consists of ontology instances. To create ontology instances, ontology population module is developed by using JENA framework which is for building semantic web and linked data applications. In the ontology population module, the mapping rules to connect parts of degree audit to certain parts of course mentoring ontology are designed. All ontology instances are generated based on degree audits of students who participate in course mentoring test. The generated instances are saved to JENA TDB as a triple repository after an inference process using JENA inference engine. A user interface for course guidance is implemented by using Java and JENA framework. Once a advisor or a student input student's information such as student name and student number at an information request form in user interface, the proposed system provides mentoring results based on a degree audit of current student and rules to check scores for each part of a curriculum such as special cultural subject, major subject, and MSC subject containing math and basic science. Recall and precision are used to evaluate the performance of the proposed system. The recall is used to check that the proposed system retrieves all relevant subjects. The precision is used to check whether the retrieved subjects are relevant to the mentoring results. An officer of computer science department attends the verification on the results derived from the proposed system. Experimental results using real data of the participating students show that the proposed course guidance system based on course mentoring ontology provides correct course mentoring results to students at all times. Advisors can also reduce their time cost to analyze a degree audit of corresponding student and to calculate each score for the each part. As a result, the proposed system based on ontology techniques solves the difficulty of mentoring methods in manual way and the proposed system derive correct mentoring results as human conduct.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.1
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• pp.93-115
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• 2017

In this study, the RiC-CM (Records in Context-Conceptual Model) draft of the International Council on Archives Expert Group on Archival Description (ICA EGAD) was analyzed, mapped with the descriptive elements of ISAD(G), and the archival description of the record group based on RiC-CM was piloted. This was done to identify trends in the archival descriptive standards and to derive considerations in relation to improving existing descriptive standards. The mapping types of RiC-CM and ISAD(G) include inter-attribute mapping, attribute-entity mapping, and attribute-relation mapping. In addition to the mapping between descriptive elements, a frame for the archival information that can construct a record through the objects, attributes, and relationships of RiC-CM is constructed using the protege, and the example data is inputted for trial. As a result, it was possible to express most of the existing descriptive information of ISAD(G) through RiC-CM. In addition, in RiC-CM, the recording descriptive information is classified in detail, and the characteristic of browsing the relation between individual objects is enhanced.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.223-230
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• 2020

Companies have made an investment of cost and time to optimize processing of a new business model in a cloud environment, applying collaboration technology utilizing business processes in a social network. The collaborative processing method changed from traditional BPM to the cloud and a mobile cloud environment. We proposed a collaborative system for operating processes in social networks using MOXMDR-DAI+ (eXtended Metadata Registry-Data Access & Integration based multimedia ontology). The system operating cloud-based collaborative processes in application of MOXMDR-DAI+, which was suitable for data interoperation. MOXMDR-DAI+ applied to this system was an agent effectively supporting access and integration between multimedia content metadata schema and instance, which were necessary for data interoperation, of individual local system in the cloud environment, operating collaborative processes in the social network. In operating the social network-based collaborative processes, there occurred heterogeneousness such as schema structure and semantic collision due to queries in the processes and unit conversion between instances. It aimed to solve the occurrence of heterogeneousness in the process of metadata mapping using MOXMDR-DAI+ in the system. The system proposed in this study can visualize business processes. And it makes it easier to operate the collaboration process through mobile support. Real-time status monitoring of the operation process is possible through the dashboard, and it is possible to perform a collaborative process through expert search using a community in a social network environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.84-97
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• 2012

M2M (Machine-to-Machine) Technology makes it possible to network all kinds of terminal devices and their corresponding enterprise applications. Therefore, several M2M platforms were developed in China in order to collect information from terminal devices dispersed all over the local places through 3G wireless network. However, when enterprise applications try to integrate with M2M platforms, they should be maintained and refactored to adapt the heterogeneous features and properties of M2M platforms. Moreover, syntactical and semantic unification for information sharing among applications and devices are still unsolved because of raw data transmission and the usage of distinguished business vocabularies. In this paper, we propose and develop an M2M Middleware to support agile application integration with M2M platform. This middleware imports the event engine and XML-based syntax to handle the syntactical unification, makes use of Ontology-based semantic mapping to solve the semantic unification and adopts WebService and ETL techniques to sustain multi-pattern interactive approach, in order to agilely make applications integrated with the M2M platform. Now, the M2M Middleware has been applied in the China Telecom M2M platform. The operation results show that applications will cost less time and workload when being integrated with M2M platform.