• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.718-722
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• 2008

Ontology plays an important role of the Semantic Web to describe meaning and reasoning of resources. Ontology has more rich expressive power through OWL that is a next standard representation language recommended by W3C. As the Semantic Web is widely known, an amount of information resources on the Web is growing rapidly and the related information resources are placed in distributed systems on the Web. So, for providing seamless services without the awareness of far distance, efficient management of the distributed information resources is required. Especially, sear ching fast for local repositories that include data related to user's queries is important to the performance of systems in the distributed environment. In this paper, first, we propose an index structure to search local repositories related to queries in the distributed Semantic Web. Second, we propose a query rewriting strategy to extend given user's query using various expression of OWL. Through the proposed index and query strategy, we can utilize various expressions of OWL and find local repositories related to all query patterns on the Semantic Web.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.101-119
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• 2003

Achieving the beneficiary goal of recent discovery in human genome project still needs a way to retrieve and analyze the exponentially expanding bio-related information. Research on bio-related fields naturally applies knowledge discovered to the current problem and make inferences to extract new information where shared concepts and data containing information need to be defined and used in a coherent way. In such a professional domain, while the need to help users reduce their work and to improve search results has been emerged, methods for systematic retrieval and adequate exchange of relevant information are still in their infancy. The design of our system aims at improving the quality of information retrieval in a professional domain by utilizing both corpus-based and concept-based ontology. Meta-rules of helping users to make an adequate query are formed into an ontology in the domain. The integration of those knowledge permits the system to retrieve relevant information in a more semantic and systematic fashion. This work mainly describes the query models with details of GUI and a secondary query generation of the system.

• The Journal of Korean Association of Computer Education
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• v.12 no.3
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• pp.75-83
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• 2009

Moving picture has become the important media in education with expanded e-learning paradigm, but Korea Educational Metadata has limitation about representing information of lots of events and objects in moving picture. Announcing the MPEG-7 specification the information of lots of events and objects in it can be presented in terms of semantic and structural description of moving pictures. In this paper moving picture searching system model that integrates two metadata specifications, such as KEM and MPEG-7, is proposed. In this model one ontology to combine two metadata specifications is designed, and the other ontology about knowledge of a subject matter is added to search efficiently in searching system. As some moving picture data from Edunet were selected and stored in our server, our prototype of searching system using MPEG-7 and KEM shows the results that we are expected.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.31-41
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• 2009

In the Semantic Web, it is possible to provide intelligent information retrieval and automated web services by defining a concept of information resource and representing a semantic relation between resources with meta data and ontology. It is very important to manage semantic data such as ontology and meta data efficiently for implementing essential functions of the Semantic Web. Thus we propose an index structure to support more accurate search results and efficient query processing by considering semantic and structural features of the semantic data. Especially we use a graph data model to express semantic and structural features of the semantic data and process various type of queries by using graph model based path expressions. In this paper the proposed index aims to distinguish our approach from earlier studies and involve the concept of the Semantic Web in its entirety by querying on primarily extracted structural path information and secondary extracted one through semantic inferences with ontology. In the experiments, we show that our approach is more accurate and efficient than the previous approaches and can be applicable to various applications in the Semantic Web.

• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.61-70
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• 2023

Due to economic or environmental reasons, green remodeling projects for old buildings are being actively carried out. Meanwhile, in the process of performing the green remodeling, the plan of green remodeling including passive and active elements has been decided based on the subjective experience and knowledge of engineers currently. Therefore, in this study, an ontology-based green remodeling decision-making support model, which can analyze the properties of old buildings and suggest appropriate remodeling plans, was established. In the developed model, once the basic properties of a building are entered, an appropriate remodeling plan composed of passive and active elements can be provided. By utilizing the results developed through the research, it is expected that it will be possible to support decision-making on more objective and appropriate remodeling alternatives development through web-based meta data search in accordance with the accumulation in remodeling cases.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.100-104
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• 2010

Recently with advent of Web 2.0, RESTful Web Services are becoming increasing trend to emphasize Web as platform. There are already many services and the number of service increases in very fast pace. So it is difficult to find the service what we want by keyword based search. To solve this problem, we developed the search method using sem antic technologies about RESTful Web Services. For that, first we define the system structure and model the description format based on the integrated search system for OpenAPIs, and then we add Semantic Markup (tagging, semantic annotation) on the HTML description pages. Next we extract RDF document from them and store it in service repository. Based on the keywords that are extended by means of ontology, the developed system provides more purified and extended results than similarity-based keyword searching system.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.231-242
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• 2008

W3C has selected OWL as a standard for Web ontology description and a necessity of research on storage models that can store OWL ontologies effectively has been issued. Until now, relational model-based storage systems such as Jena, Sesame, and DLDB, have been developed, but there still remain several issues. Especially, they lead inefficient query processing performance. The structural problems of their low query processing performance are as follow: Jena has a simple structure which is not normalized and also stores most information in a single table. It exponentially decreases the performance because of comparison with unnecessary information for processing queries requiring join operations as well as simple search. The structures of storages(e.g., Sesame) have been completely normalized. Therefore it executes many join operations for query processing. The storages require many join operations to find simply a specific class. This paper proposes a storage model to resolve the problems that the query processing performance is decreased because of non-normalization or complete normalization of the existing storages. To achieve this goal, we analyze the problems of existing storage models as well as relations of OWL elements and query patterns. The proposed model, defined with the analysis results, provides an optimal normalized structure to minimize join operations or unnecessary information comparison. For the experiment of query processing performance, a LUBM data sets are used and query patterns are defined considering search targets and their hierarchical relations. In addition, this paper conducts experiments on correctness and completeness of query results to verify data loss of the proposed model, and the results are described. With the comparative evaluation results, our proposal showed a better performance than the existing storage models.

• 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.