Miyoung Cho

Travel Ontology for Intelligent Recommendation System

Nowadays, travel information is increasing to appeal the tourists on the web. Although there are numerous information provided on the web, the user gets puzzled in finding accurate information. In order to solve these web problems, the concept of semantic web comes into existence to have communication between human and computer.In this paper, we propose intelligent recommendation system based on Jeju travel ontology. The proposed system can recommend the tourist more intelligent information using properties, relationships of travel ontology. Next, the system is responsible for finding personalized attractions and plotting location of traveler on the AlMap.

Travel Ontology for Recommendation System based on Semantic Web

This paper presents the travel ontology to retrieve information from recommendation system based on semantic Web. The metadata is made by preference profile and transaction profile. Information repository is consisting of travel information and ontology. The travel ontology is made by OWL, rule based on description logic. The top class is travel as the domain to build the travel ontology. And accommodation, activity, food and transportation are selected as the upper class. The subclass of the activities consists of leisure sports and tourism culture. At last, the result of Recommendation following scenario using OWL-QL is shown. We propose the travel recommendation system in semantic Web using ontology. As you know, we can easily insert attributes to ontology. Besides we can inference from rules and properties in ontology

A New Method for Ontology Merging based on Concept using WordNet

Ontologies have been realized as the key technology for shaping and exploiting information for the effective management of knowledge and for the evolution of the semantic Web and its applications. However, it seems that there are always more than one ontology even for the same domain. In such a setting where different conceptualizations of the same domain exist, information services must effectively answer queries, bridging the gaps between their formal ontologies and users own conceptualizations. Therefore, coordination (i.e. mapping, alignment, merging) of ontologies is a major challenge for bridging the gaps between agents with different conceptualizations. So we propose a new method for ontology merging using WordNet. In this paper, we introduce ontology merging based on the horizontal approach and vertical approach. These approaches have different characters. The horizontal approach is to analyze mapping between ontologies through integrated similar concept in the same level. The vertical approach is the method which create the rule from similarity measure between concepts in the different level (when it is impossible in horizontal approach)

Domain knowledge ontology building for semantic video event description

A novel method for video event analysis and description based on the domain knowledge ontology has been put forward in this paper. Semantic concepts in the context of the video event are described in one specific domain enriched with qualitative attributes of the semantic objects, multimedia processing approaches and domain independent factors: low level features (pixel color, motion vectors and spatio-temporal relationship). In this work, we consider one shot (episode) in the Billiard Game of video as the domain to explain how the high-level semantic mapped into low level features and the detection of the semantically important event.

Comparing Ontologies Using Entropy

Ontologies have been realized as the key technology for shaping and exploiting information for the effective management of knowledge and for the evolution of the Semantic Web and its applications. As the amount of ontologies is rapidly increasing, comparison or coordination (i.e. mapping, alignment, merging) of ontologies is a major challenge for bridging the gaps between agents with different conceptualizations. Therefore, in this paper, we compare ontologies using entropy which shows structural features of ontology as the average of information content. We use classified domain ontologies in the WordNet that is a kind of the linguistic ontology and analyze characteristics of domain ontologies.

Object retrieval by query with sensibility based on the KANSEI-Vocabulary scale

Recently the demand for image retrieval and recognizable extraction corresponding to KANSEI (sensibility) has been increasing, and the studies focused on establishing those KANSEI-based systems have been progressing more than ever. In addition, the attempt to understand, measure and evaluate, and apply KANSEI to situational design or products will be required more and more in the future. Particularly, study of KANSEI-based image retrieval tools have especially been in the spotlight. So many investigators give a trial of using KANSEI for image retrieval. However, the research in this area is still under its primary stage because it is difficult to process higher-level contents as emotion or KANSEI of human. To solve this problem, we suggest the KANSEI-Vocabulary Scale by associating human sensibilities with shapes among visual information. And we construct the object retrieval system for evaluation of KANSEI-Vocabulary Scale by shape. In our evaluation results, we are able to retrieve object images with the most appropriate shape in term of the querys KANSEI. Furthermore, the method achieves an average rate of 71% users satisfaction.

Matching colors with KANSEI vocabulary using similarity measure based on wordnet

Recently, the image retrieval based on content is capable of understanding the semantics of visual information. However, it is hard to represent emotion or feeling of human. To approach more intelligent content-based retrieval, we focus on KANSEI information. This paper presents a method of matching color, which is part of visual information associated with KANSEI-vocabulary relation. We use WordNet that is a kind of lexical ontology by relations between words. We define relation for matching between color and KANSEI vocabulary using the meaning of color table. We propose the similarity measure between Color-KANSEI vocabulary and query. After experiment we can find the best pertinent color using Lesk algorithm. The significance of our study is finding semantically pertinent color according to various queries based on WordNet. This is the approach as computing vocabulary to show KANSEI of Human.

An Efficient Computational Method for Measuring Similarity between Two Conceptual Entities

Previous definitions of semantic similarity can be classified into two approaches. The node(information content)-based approach uses an entropy measure that is computed on the basis of child node population. The edge-based approach involves the use of the number of edges between two concepts within a hierarchical conceptual structure. The edge-based distance method is more intuitive, while the node-based information content approach is more theoretically sound. We consider a combined model that is derived from the edge-based notion with the addition of the information content. In this paper, we propose a method for computerized conceptual similarity calculation in WordNet space. The proposed method provides a degree of conceptual dissimilarity between two concepts. It gives a higher correlation value with a criterion based on human similarity judgment.

Comparison between motion verbs using similarity measure for the semantic representation of moving object

Most of the researchers have used spatio-temporal relations for retrieval in video. Its just trajectory-based or content-based retrieval. However, we seldom retrieve information referring to semantics. So, in this paper, we propose a novel approach for motion recognition from the aspect of semantic meaning. This issue can be addressed through a hierarchical model that explains how the human language interacts with human motions. And, in the experiment part, we evaluate our new approach using trajectory distance based on spatio-temporal relations to distinguish the conceptual similarity and get the satisfactory results.

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.