Hyunjang Kong

A new methodology for merging the heterogeneous domain ontologies based on the WordNet

As the study on the semantic Web actively progresses, many domain ontologies are being built. However, most engineers are building the ontologies based on their academic background and research interests. Consequently, ontologies on the same subject are different because the ontology engineers have different view point. Furthermore, there is no standard method for building ontologies, and there are many ontology tools using different ontology languages. Because of these reasons, interoperability between the ontologies is very low. And, current ontology tools mostly concentrate on the functions to create, edit, inference the ontology. Methods for merging heterogeneous domain ontologies are not in most tools. This paper presents a study on a merging method for building a more complete ontology. The method was based on WordNet. The study tried to merge heterogeneous domain ontologies about the same subject throughout specific steps.

Design of the automatic ontology building system about the specific domain knowledge

Nowadays, ontology is being studied on the various parts. Especially, as the study on the semantic Web has actively progressed, the applicable range of the ontology is getting wide. At the result, many ontologies are existed in the web. Based on this theory, several problems happened on the ontology side. Among these problems, the significant issue is how to address the heterogeneity and consistency between the ontologies that were made about the same domain. In this paper, we give the possibility for building the ontology automatically based on the existing knowledge data. And we could expect to address the problem of the ontology reliability. In this paper, we suggest the new methodology for building the ontology automatically based on the WordNet and we design and implement the automatic ontology building system. Thus, we could expect that the users are able to build the ontology more consistently and easily

The Design of the Ontology Retrieval System on the Web

The purpose of this paper is to maximize the reusability of ontology. We try to design the system for searching the ontologies dispersing on the Web. As activated progressing of research about the semantic Web, ontology that is the necessity factor of the semantic Web is getting important. So, both many ontology languages and ontology tools are developed until now. And much ontology is constructed by the researchers, but ontology retrieval on the Web is the hard work and the repository that is storing the ontologies was not existed. For that reason, there is the trouble that the domain ontology is always constructed newly, though the identical domain ontology was presented somewhere on the Web. It is the big problem to decrease the reusability of the ontology that is significant feature of ontology. In this paper, we describe the method for retrieving the domain ontology and design the system

Topic selection of web documents using specific domain ontology

This paper proposes a topic selection method for web documents using ontology hierarchy. The idea of this approach is to utilize the ontology structure in order to determine a topic in a web document. In this paper, we propose an approach for improving the performance of document clustering as we select the topic efficiently based on domain ontology. We preprocess the web documents for keywords extraction using Term Frequency formula and we build domain ontology as we branch off the partial hierarchy from WordNet using an automatic domain ontology building tool in preprocessing step. And we select a topic for the web documents based on domain ontology structure. Finally we realized that our approach contributes the efficient document clustering.

Multimedia information retrieval based on spatiotemporal relationships using description logics for the semantic web

Since the latter half of the 1990s, ontology has been the main area of study in the semantic web. Ontology has been actively studied in several areas for a long time. There are many ontological applications and construction methods. In the study of the semantic web, ontologies are built using OWL capabilities. However, it is not suitable for managing spatial relationships. To represent spatial relationships, we try to create new vocabularies based on these two theories. Modeling topological relationships are accomplished using a neighborhood graph that only considers topological relatedness. In addition, we define the new vocabulary to represent the spatial relationships based on spatial description logic. In this article, we focus on multimedia information retrieval. We use the new vocabularies that were defined using these two theories for representing spatial relationships in image and video retrieval.

The method for the unknown word classification

Natural Language Processing is a hard task. For the real Natural Language Processing, it is a necessary technique to process the unknown words. In this paper, we introduce the method for understanding the unknown words means. Many terms are newly created and we do not find these words in dictionary. Unknown words are generally occurred by reflecting the new phenomenon and technology. Hence, unknown words are dramatically created because of rapid changes in society. However, it is a hard task to define the meaning of all unknown words in dictionary. So, in this paper, we focus on how the machine understands the unknown words means. We propose a method to classify unknown words using the relevancy values between all nouns in the document and their TF values.

The Techniques for the Ontology-Based Information Retrieval

The use of ontologies to address the problems of the existing keyword-based search has been searched. For the efficient ontology-based information retrieval, there are several facts we should consider. In this paper, we describe the techniques demanded for the ontology-based information retrieval.

Adaptive-Tangent space representation for image retrieval based on kansei

From the engineering aspect, the research on Kansei information is a field aimed at processing and understanding how human intelligence processes subjective information or ambiguous sensibility and how such information can be executed by a computer. Our study presents a method of image processing aimed at accurate image retrieval based on human Kansei. We created the Kansei-Vocabulary Scale by associating Kansei of high-level information with shapes among low-level features of an image and constructed the object retrieval system using Kansei-Vocabulary Scale. In the experimental process, we put forward an adaptive method of measuring similarity that is appropriate for Kansei-based image retrieval. We call it “adaptive-Tangent Space Representation (adaptive-TSR)”. The method is based on the improvement of the TSR in 2-dimensional space for Kansei-based retrieval. We then it define an adaptive similarity algorithm and apply to the Kansei-based image retrieval. As a result, we could get more promising results than the existing method in terms of human Kansei.

Concept based image retrieval using the domain ontology

The recent study has been progressed the research about more semantic image indexing and retrieval. In our paper, we represent the improved concept-based image retrieval by using domain ontology. We analyze the many studies that applied the theory of ontology to concept-based image retrieval. Then, we try to solve the problems when we apply the huge ontologies in image retrieval system. There are two big problems. First, the huge ontologies that have many concepts, especially in particular domain, cannot express in existing ontologies. Therefore, in this paper we try to design and implement the domain ontology about the car based on the WordNet, which is one kinds of ontologies. The experimental result shows that the semantic distances between words are quite close when we test domain ontology than the existing WordNet.

Representing the spatial relations in the semantic web ontologies

The core of the study into the semantic web is ontologies. Ontologies have been actively studied in many areas for a long time. There has been a great deal of effort reported in building ontologies. In the study of a semantic web, the ontologies are constructed using the RDF Schemas and OWL Capabilities. The RDF Schemas and OWL Capabilities can deal with the many relationships that occur when ontologies are constructed, but it is not inadequate for managing spatial relations. This paper defines the new axioms for representing the spatial relations based on the Description Logic as a web ontology regarding nations was built. The ontology was then represented using the OWL.