• Journal of Digital Convergence
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• v.10 no.10
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• pp.215-220
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• 2012

This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

• Journal of the Korean Geographical Society
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• v.41 no.5 s.116
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• pp.582-599
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• 2006

Due to the growing popularity of mobile information technology, more people, especially in the general public, have access to computerized geospatial information systems for wayfinding tasks or urban navigation. One of the problems with the current services is that, whether the users are exploring or navigating, whether they are travelers who are totally new to a region or long-term residents who have a fair amount of regional knowledge, the same method is applied and the direction are given in the same way. However, spatial knowledge for a given urban region expands in proportion to residency. Urban navigation is highly dependent on cognitive mental images, which is developed through spatial experience and social communication. Thus, the wayfinding service for a regional community can be highly supported, using well-known regional places. This research is to develop the framework for urban navigation within a regional community. The concept of communal ontology is proposed to aid in urban regional navigation. The experimental work was implemented with case study to collect regional landmarks, develop the ontological model and represent it with formal structure. The final product of this study will provide the geographical information of a region to the other agent and be the fundamental information structure for cognitive urban regional navigation.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.337-344
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• 2006

In this paper, we describe the design and the implementation of word clustering system using a definition of an entry word in the dictionary, called a dictionary definition. Generally word clustering needs various features like words and the performance of a system for the word clustering depends on using some kinds of features. Dictionary definition describes the meaning of an entry in detail, but words in the dictionary definition are implicative or abstractive, and then its length is not long. The word clustering using only features extracted from the dictionary definition results in a lots of small-size clusters. In order to make large-size clusters and improve the performance, we need to transform the features into more general words with keeping the original meaning of the dictionary definition as intact as possible. In this paper, we propose two methods for extending the dictionary definition using ontology. One is to extend the dictionary definition to parent words on the ontology and the other is to extend the dictionary definition to some words in fixed depth from the root of the ontology. Through our experiments, we have observed that the proposed systems outperform that without extending features, and the latter's extending method overtakes the former's extending method in performance. We have also observed that verbs are very useful in extending features in the case of word clustering.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.579-586
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• 2020

There is a large amount of multimedia data on the web page, and a method of extracting semantic information from low level visual information for accurate retrieval is being studied. However, most of these techniques extract one of information from a single image, so it is difficult to extract semantic information when multiple objects are combined in the image. In this paper, each low-level feature is extracted to extract various objects and backgrounds in an image, and these are divided into predefined backgrounds and objects using SVM. The objects and backgrounds divided in this way are constructed with ontology, infer the semantic information of location and association using inference engine. It's possible to extract the semantic information. We propose this method process the complex and high-level semantic information in image.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.335-346
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• 2008

Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.43-50
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• 2007

Delay in the process of Information Extraction, IE, is largely due to inability to correctly recognize the user's information requirement of particular search factors. Especially if the wrapper rules are used in a search engine, the search generally fails to classify internet documents properly and efficiently since the application of the same wrapper rules lacks extensibility throughout various types of existing internet document. In case of buying or selling a ship, if the price range, type. place of delivery, inspection site and other information relevant to the sales would be available through the internet for proper retrieval the sales could more readily succeed by using Ontology relating to sales or purchase information and by selectively searching for the desired information through the content based retrieval system. This system proposes to improve various wrapper systems existing throughout different internet sites and to eliminate unnecessary information tagged on the existing internet documents in order to create a more advanced information retrieval system.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.118-123
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• 2008

온톨로지란 실세계에 존재하는 사물 및 개념, 그리고 용어들 간의 관계들을 컴퓨터가 이해할 수 있는 형태로 표현한 것이다. 온톨로지 구축에 있어서 대용량 코퍼스의 활용은 해당코퍼스에서 등장하는 용어들과 이들 사이에서 나타나는 문자열을 일종의 패턴으로 취급하여 특정 패턴과 함께 나타나는 용어 쌍들을 해당 패턴이 대표하는 의미 관계로 설정하는 방식을 취한다. 그러나 기존의 방법은 주로 두 용어들 사이에서 나타나는 문자열만을 고려하여 패턴을 추출하기 때문에 해당 문장에 포함된 보다 다양한 문장 정보들을 활용할 수 없다. 본 논문은 이러한 한계점을 감안하여, 용어 쌍 사이에서 나타나는 문자열과 주변 동사 정보를 함께 고려함으로써 패턴의 정교성을 향상시키는 방법을 제안한다. 또한 동사들의 동의어를 활용하여 다양한 용어들을 포괄할 수 있는 일반화된 패턴을 구축한다. 본 방법론은 is-a 관계의 경우 64%, part-of 관계의 경우 83%, made-of 관계의 경우 73%, use 관계의 경우 72%의 정확률을 보였으며 모두 기존 방법보다 향상된 결과를 가져왔다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.645-646
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• 2018

Recently, the concept of machine learning has been introduced as a decision making method through data processing. Machine learning uses the results of running based on existing data as a means of decision making. The data generated by the development of technology is vast. This data is called big data. It is important to extract the necessary data from these data. In this paper, we propose a method for extracting related data for constructing an ontology through machine learning. The results of machine learning can be given a relationship from a semantic perspective. it can be added to the ontology to support relationships depending on the needs of the application.