• Journal of Information Processing Systems
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• v.13 no.4
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• pp.834-851
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• 2017

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.

• The Journal of Information Systems
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• v.18 no.3
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• pp.211-229
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• 2009

Recently, trends of information technology development include offerings of service for personalization, intelligence, and convergence. The research suggested a new tour system that tourists can make their tour packages by applying Semantic Web technology. The system includes ontologies and inference rules for offering intelligent and personalized service. Our system called MYT (Make Your Tour-package) was successfully demonstrated by employing realistic scenarios. Current version of the MYT system needs manager's intervention to link and integrate automatically ontology subsystem and Web service. In further study, the MYT will be extended to the system including a component integrating automatically subsystems and a component capturing and processing context data from RFID/USN.

• Language and Information
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• v.17 no.1
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• pp.1-16
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• 2013

The goal of this paper is two-fold: (i) the paper aims to characterize unique semantics of so called "symmetric" locatives like across the street - this will provide a guiding semantics for annotating a variety of paths; and (ii) the paper claims that we need "symmetric" paths to give a unified account of the various semantic effects of symmetric locatives. The paper illustrates several semantic effects induced by symmetric locatives: (i) symmetric underspecification, (ii) path-/event-quantification, (iii) static symmetric relations, and (iv) the symmetric inference by the adverb back. The paper defines the semantic class of symmetric locatives, and accounts for the symmetry effects in terms of properties and relations of Path Structure proposed by Nam (1995).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.314-317
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• 2006

This study proposes an ontology design framework to support the cooperation among devices by using PROLOG, Conceptual Graph (CG), and Resource Description Framework (RDF). Quite a large number of representation languages for representing ontology on the Web have been established over the last decade. Most of these researches are focused on design of independent resources description. In Semantic Web, however, cooperation ontology will be needed. In this study, the CG could make an entire conceptual view of knowledge and RDF can represent that knowledge. Then the PROLOG could support the natural inference based on that knowledge. Therefore, our proposed ontology will be used in the designing of Semantic Web-based cooperation systems.

• The Journal of Society for e-Business Studies
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• v.12 no.1
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• pp.135-149
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• 2007

There are many kinds of applications or software components to manage files in a local computer, but it is very difficult to organize personal documents in a consistent way and to search expected ones in a precise way. In this paper, we present our development of a document management and retrieval tool, which is named Ontalk. Our system provides a semi-automatic metadata generator and an ontology-based search engine for electronic documents. Ontalk can create and import various ontologies in RDFS or OWL for describing the metadata. Our system that is built upon.NET technology is easily communicated with or flexibly plugged into many different programs.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.488-496
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• 2005

현재의 웹이 HTML을 바탕으로 인간 사용자와의 인터페이스에 초점을 맞추고 있는데 비하여, 차세대 웹은 XML 및 XML 기반 각종 표준들을 바탕으로 소프트웨어 에이전트와의 인터페이스에 초점을 맞추어 나가고 있다. 차세대 웹에서 소프트웨어 에이전트의 두뇌 역할을 수행하기 위하여 추론엔진은 차세대 웹의 표준 언어인 시맨틱 웹(Semantic Web)을 충실히 이해할 수 있어야 한다. 이를 위한 기초 작업의 일환으로 OWL(Web Ontology Language)과 RuleML(Rule Markup Language)이 W3C에 제안된 바 있다. 본 연구에서는 SWRL을 규칙 표현 방법으로 사용하고, OWL을 사실 표현 방법으로 사용하는 역방향 추론엔진인 SMART-B(SeMantic web Agent Reasoning Tools - Backward chaining inference engine)을 개발하고자 한다. 이를 위하여 SWRL 기반 역방향 추론을 위한 요구 기능을 분석하고, 기존 역방향 추론 알고리즘에 차세대 시맨틱 웹을 요구 기능을 반영한 역방향 추론 알고리즘을 설계하였다. 또한, 유비쿼터스 환경에서의 각종 플랫폼의 독립성과 이식성을 확보하고 기기 간의 성능 차이를 극복할 수 있도록 사실 베이스 및 규칙 베이스의 관리도구와 역방향 추론 엔진 등을 Java 프로그래밍 언어를 이용하여 단위 컴포넌트의 형태로 개발 중에 있다.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.141-150
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• 2000

There are three significant drawbacks in extant fuzzy rule-based expert system languages. First, they lack the functionality of composite object inference. Second, they do not support fuzzy reasoning semantically easy to understand and conceptually simple to use. Third, knowledge representation and reasoning style of their model have a great semantic gap with those of current database models. Therefore, it is very difficult for the two models to be seamlessly integrated with each other. This paper provides the formal specification of a fuzzy object inference model to solve the three drawbacks. GIS(Geographic Information System) application domain is used to demonstrate that our model naturally models complex GIS information in terms of composite objects and successfully performs fuzzy inference between them.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.125-135
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• 2014

In the medical field, for the purpose of diagnosis and treatment of diseases, building knowledge base has received a lot of attention. The most important thing to build a knowledge base is representing the knowledge accurately. In this paper we suggest a knowledge representation method using Ontology technique with the datasets obtained from the domestic papers on Alzheimer disease that has received a lot of attention recently in the medical field. The suggested Ontology for Alzheimer disease defines all the possible classes: lexical information from journals such as 'author' and 'publisher' research subjects extracted from 'title', 'abstract', 'keywords', and 'results'. It also included various semantic relationships between classes through the Ontology properties. Inference can be supported since our Ontology adopts hierarchical tree structure for the classes and transitional characteristics of the properties. Therefore, semantic representation based query is allowed as well as simple keyword query, which enables inference based knowledge query using an Ontology query language 'SPARQL'.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.677-684
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• 2016

With the increase of multimedia information such as image, researches on extracting high-level semantic information from low-level visual information has been realized, and in order to automatically generate this kind of information. Various technologies have been developed. Generally, image retrieval is widely preceded by comparing colors and shapes among images. In some cases, images with similar color, shape and even meaning are hard to retrieve. In this article, in order to retrieve the object in an image, technical value of middle level is converted into meaning value of middle level. Furthermore, to enhance accuracy of segmentation, K-means algorithm is engaged to compute k values for various images. Thus, object retrieval can be achieved by segmented low-level feature and relationship of meaning is derived from ontology. The method mentioned in this paper is supposed to be an effective approach to retrieve images as required by users.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.