• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.516-523
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• 2008

Recently, the Web has been constructed based on collective intel1igence and growing up quickly. User created contents have been made the mainstream in this environments. So it's required to make an efficient technique of searching for the contents. The current searching technique mainly is achieved by key words. Semantic Web based on similarity and relationship of a language and using user tags in web2.0 also have been researched with activity. Generally, the web of the participation architecture has a lot of user created contents, various forms and classification. Therefore, it is necessary to classify and to efficiently search for a lot of user created contents. In this paper, we propose a depth searching technique considering the relationship among the tags that descript user contents. It is expected that the proposed depth searching techniques can reduce the time taken to search for the unwanted contents and the increase the efficiency of the contents searching using a service of suggestion words in tags groups.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.895-901
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• 2004

This paper describes a two-phase intelligent method for filtering spam mail based on textual information and hyperlinks. Scince the body of spam mail has little text information, it provides insufficient hints to distinguish spam mails from legitimate mails. To resolve this problem, we follows hyperlinks contained in the email body, fetches contents of a remote webpage, and extracts hints (i.e., features) from original email body and fetched webpages. We divided hints into two kinds of information: definite information (sender`s information and definite spam keyword lists) and less definite textual information (words or phrases, and particular features of email). In filtering spam mails, definite information is used first, and then less definite textual information is applied. In our experiment, the method of fetching web pages achieved an improvement of F-measure by 9.4% over the method of using on original email header and body only.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.145-155
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• 2010

In this paper, we suggest a fish drug domain ontology to show an associated information and hierarchy together through concept-relation and inference mechanism instead of keyword matching. First, we investigate competency questions from workers of fishery industry and then we derive concepts and terminologies. Next, we present a process of fish drug ontology modelling using Protege-OWL editor, which is an extension of Protege that supports the Web Ontology Language(OWL). Last, we suggest the user interface of semantic web-based fish drug information system to search easily associated informations of fish drug using this ontology. It is to provide an effective search method that fish disease manager, fish farmer, and students majoring in fisheries can confirm details of diseases, fish, and drug evaluations associated with fish drug within one screen without moving to another position.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.449-454
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• 2020

This paper proposes a machine learning-based emotion analysis system that detects a user's depression through their SNS posts. We first made a list of keywords related to depression in Korean, then used these to create a training data by crawling Twitter data - 1,297 positive and 1,032 negative tweets in total. Lastly, to identify the best machine learning model for text-based depression detection purposes, we compared RNN, LSTM, and GRU in terms of performance. Our experiment results verified that the GRU model had the accuracy of 92.2%, which is 2~4% higher than other models. We expect that the finding of this paper can be used to prevent depression by analyzing the users' SNS posts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.772-774
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• 2017

Currently, there are many simple searches for local culture and tourism, but systematic information retrieval using ontology technology is weak. The keyword-based search, which is an existing search method, derives a search result that is different from a user's wanted intention. On the other hand, semantic search using ontology constructs shows the information related to the search term by creating a relation between words and words. Therefore, when tourists search for cultural and tourist attractions in the area, they provide information that includes meaning relevance in the search results. If the ontology provides information on the culture, sightseeing area, transportation, Can be more easily grasped. In this paper, we propose an ontology-based retrieval system based on culture and tourist sites utilizing public institutions database by using mobile application by extending search system which relied only on existing internal database to provide accurate and reliable information to users. This efficient structure of the ontology makes it possible to provide information suitable for the user quickly and accurately.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.32-37
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• 2008

To ensure access to rapidly growing video collection, video indexing is becoming more and more important. In this paper, video ontology system for retrieving a video data based on a scene unit is proposed. The proposed system creates a semantic scene as a basic unit of video retrieval, and limits a domain of retrieval through a subject of that scene. The content of semantic scene is defined using the relationship between object and event included in the key frame of shots. The semantic gap between the low level feature and the high level feature is solved through the scene ontology to ensure the semantic-based retrieval.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.859-864
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• 2006

The openness of the Web allows any user to access almost any type of information easily at any time and anywhere. However, with function of easy access for useful information, internet has dysfunctions of providing users with harmful contents indiscriminately. Some information, such as adult content, is not appropriate for all users, notably children. Additionally for adults, some contents included in abnormal porn sites can do ordinary people's mental health harm. In the meantime, since Internet is a worldwide open network it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse way, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.495-502
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• 2022

Web crawler, which is mainly used to collect text on the web today, is difficult to maintain and expand because researchers must implement different collection logic by collection channel after analyzing tags and styles of HTML documents. To solve this problem, the web crawler should be able to collect text by formalizing HTML documents to the same structure. In this paper, we designed and implemented WCTT(Web Crawling system based on Tag path and Text appearance frequency), a web crawling system that collects text with a single collection logic by formalizing HTML documents based on tag path and text appearance frequency. Because WCTT collects texts with the same logic for all collection channels, it is easy to maintain and expand the collection channel. In addition, it provides the preprocessing function that removes stopwords and extracts only nouns for keyword network analysis and so on.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.595-598
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• 2007

Vast amount of XML documents raise interests in how they will be used and how far their usage can be expanded. This paper has two central goals: 1) easy and fast retrieval of XML documents or relevant elements; and 2) efficient and stable management of large-size XML documents. The keys to develop such a practical system are how to segment a large XML document to smaller fragments and how to store them. In order to achieve these goals, we designed SCOF(Service-centric Object Fragmentation) model, which is a semi-decomposition method based on conversion rules provided by XML database managers. Keyword-based search using SCOF model then retrieves the specific elements or attributes of XML documents, just as typical XML query language does. Even though this approach needs the wisdom of managers in XML document collection, SCOF model makes it efficient both retrieval and management of massive XML documents.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.73-79
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• 2010

Emotion intelligence computing is able to processing of human emotion through it's studying and adaptation. Also, Be able more efficient to interaction of human and computer. As sight and hearing, music & image is constitute of short time and continue for long. Cause to success marketing, understand-translate of humanity emotion. In this paper, Be design of check system that matched music and image by user emotion keyword(irritability, gloom, calmness, joy). Suggested system is definition by 4 stage situations. Then, Using music & image and emotion ontology to retrieval normalized music & image. Also, A sampling of image peculiarity information and similarity measurement is able to get wanted result. At the same time, Matched on one space through pared correspondence analysis and factor analysis for classify image emotion recognition information. Experimentation findings, Suggest system was show 82.4% matching rate about 4 stage emotion condition.