• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.491-500
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• 2004

This paper presents an ontology construction method using various computational language resources, and an ontology-based word sense disambiguation method. In order to acquire a reasonably practical ontology the Kadokawa thesaurus is extended by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. To apply the ontology to disambiguate word senses, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9% improvement over methods which do not use ontology for Korean translation.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.409-420
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• 2007

Software adaptation may be required to interact between heterogeneous platforms and to react to rapid change of environment in ubiquitous computing. Web service provides a way to use heterogeneous and distributed services or resources to utilize and organize them. But it is not easy to retrieve appropriate services when we search services because web service lacks of semantic information. Semantic web service provides additional information of services, but it does not support a method to match them in various ways. We can adapt and extend web applications by using web service, but a method for management and administration is still needed. Therefore in this paper, we propose a framework for dynamic growing of web service applications based on ESB(Enterprise Service Bus) and agent and provide a prototype to show its usefulness.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.476-485
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• 2006

The Resource Description Framework(RDF), which emerged with the semantic web, is settling down as a standard for representing information about the resources in the World Wide Web Hence, a lot of research on storing and query processing RDF documents has been done and several RDF storage systems, such as Sesame and Jena, have been developed. But the research on updating RDF documents is still insufficient. When a RDF document is changed, data in the RDF triple store also needs to be updated. However, current RDF triple stores don't support incremental update. So updating can be peformed only by deleting the old version and then storing the new document. This updating method is very inefficient because RDF documents are steadily updated. Furthermore, it makes worse when several RDF documents are stored in the same database. In this paper, we propose an incremental update algorithm on RDF, documents in triple stores. We use a text matching technique for two versions of a RDF document and compensate for the text matching result to find the right target triples to be updated. We show that our approach efficiently update RDF documents through experiments with real-life RDF datasets.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Journal of the Korean Society for information Management
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• v.20 no.1
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• pp.417-455
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• 2003

Ontologies are formal theories that are suitable for implementing the semantic web. which is a new technology that attempts to achieve effective retrieval, integration, and reuse of web resources. Ontologies provide a way of sharing and reusing knowledge among people and heterogeneous applications systems. The role of ontologies is that of making explicit specified conceptualizations. In this context, domain and generic ontologies can be shared, reused, and integrated in the analysis and design stage of information and knowledge systems. This study aims to design an ontology for international organizations. and build an Internet web retrieval system based on the proposed ontology. and finally conduct an experiment to compare the system performance of the proposed system with that of internet search engines focusing relevance and searching time. This study found that average relevance of ontology-based searching and Internet search engines are 4.53 and 2.51, and average searching time of ontology-based searching and Internet search engines are 1.96 minutes and 4.74 minutes.

• Journal of Korean Society of Archives and Records Management
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• v.16 no.1
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• pp.175-193
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• 2016

A topic map was designed as an efficient information retrieval method that is optimized for classification, organization, and navigation through the use of a semantic link network above information resources. With this, this study aims to design a topic map-based university archives retrieval system to provide the relevant information retrieval. For this study, electronic records that relate to the academic administration within two years of D university were collected, and topic map editing was carried out with Ontopia Omnigator. Topics were classified according to their functional analysis of academic administration. In the end, the number of topics was finalized as 626, with 6 types in general: academic work, staff, college register, student, university, etc. Association was separated into six types as well, which were formed with consideration to the relationships among topics. In addition, there are seven occurrence types: register class, register number, register date, receiver, title, creator, and identifier. It is expected that the associative nature of the designed topic map-based retrieval system in this study will make navigation of large records easy and allow incidental discovery of knowledge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.292-298
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• 2011

In order to acquire new information and biological meaning of the signal data by defining the relationships between them, new modeling technique, ontology, has been proposed. The data of bio-signal can be represented as a systematic and logical to manage continuously bio-signal data using ontology. Furthermore, knowledge of which resources are utilized to provide improved service quality in medical information, health services in various fields. However, relevant studies have not been performed actively to compare importance of relationships between bio-signals. Therefore semantic representation of biometric information should be by defining the relationship between bio-signals. In this paper, we have developed bio-signal ontology to use as a model for using domain knowledge. We verified the usefulness of the ontology by using scenarios.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.169-180
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• 2009

The conventional paper retrieval is the keyword-based search and as a huge amount of data be published, this search becomes more difficult in retrieving information that user want to find. In order to search for information to the user's intent, we need to introduce semantic Web that represents semantics of Web document resources on the Internet environment as ontology and enables the computer to understand this ontology. Therefore, we describe a paper retrieval system through OWL(Ontology Web Language) ontology-based reason in this paper. We build the paper ontology based on OWL which is new popular ontology language for semantic Web and represent the correlation among diverse paper properties as the DL(description logic) query, and then this system infers the correct results from the paper ontology by using the DL query and makes it possible to retrieve information intelligently. Finally, we compared our experimental result with the conventional retrieval.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.17-29
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• 2024

The Gyeongin Ara Waterway is a regional development project designed to transport cargo by sea and to utilize the surrounding waterfront area to enjoy tourism and leisure. It is being used as a space for demonstration projects for urban air transportation (UAM), which has recently been attracting attention, and various efforts are being made at the local level to strengthen cultural and tourism functions and revitalize local food. This study examined the perception and trends of tourism consumers on the Gyeongin Ara Waterway before and after the outbreak of COVID-19. The research method utilized semantic network analysis based on social network analysis. As a result of the study, first, before the outbreak of COVID-19, key words such as bicycle, Han River, riding, Gimpo, Seoul, hotel, cruise ship, Korea Water Resources Corporation, emotion, West Sea, weekend, and travel showed a high frequency of appearance. After the outbreak of COVID-19, keywords such as cafe, discovery, women, Gimpo, restaurant, bakery, observatory, La Mer, and cruise ship showed a high frequency of appearance. Second, the results of the degree centrality analysis showed that before the outbreak of COVID-19, there was increased interest in accommodations for tourism, such as Marina Bay and hotels. After the outbreak of COVID-19, interest in food such as specific bakeries and cafes such as La Mer was found to be high. Third, due to the CONCOR analysis, five keyword clusters were formed before the outbreak of COVID-19, and the number of keyword clusters increased to eight after the outbreak of COVID-19.