• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.596-605
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• 2005

Conventional XML query methods such as simple keyword match or structural query expansion are not sufficient to catch the underlying information in the documents. Moreover, these methods inefficiently try to query all the documents. This paper proposes document tittering and query expansion methods that are based on ontology. Using ontology, we construct a universal DTD that can filter off unnecessary documents. Then, query expansion method is developed through the analysis of concept hierarchy and association among concepts. The proposed methods are applied on variety of sample XML documents to test the effectiveness.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.25-27
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• 2006

XPath 명세는 XML 원소 내용을 필터링하기 위한 질의어 작성이 어렵다. 본 논문은 이러한 문제점을 해결하기 위해 SQL의 LIKE 연산자에서 사용되던 특별한 매칭 문자 '%' 를 허용한 확장된 XPath 명세와 그것을 표준 질의어로 사용하는 문서-중심적 XML 필터링 기법인 Pfilter를 제안한다. Pfilter는 값-기반 술어(value-based predicate)에서 피연산자의 공통 앞부분 문자를 공유하여 값-기반 술어의 처리 성능을 향상시킨다. 또한 본 논문은 Pfilter와 대표적인 데이터-중심적 XML 필터링 기법인 Yfilter를 값-기반 술어 처리의 확장성과 효율성에 대해 비교하고 Pfilter의 값-기반 술어 삽입, 삭제, 처리 결과를 제공한다.

• MALSORI
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• no.63
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• pp.139-151
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• 2007

As the Internet becomes prevalent in our lives, harmful contents, such as phornographic videos, have been increasing on the Internet, which has become a very serious problem. To prevent such an event, there are many filtering systems mainly based on the keyword-or image-based methods. The main purpose of this paper is to devise a system that classifies pornographic videos based on the audio information. We use the mel-cepstrum modulation energy (MCME) which is a modulation energy calculated on the time trajectory of the mel-frequency cepstral coefficients (MFCC) as well as the MFCC as the feature vector. For the classifier, we use the well-known Gaussian mixture model (GMM). The experimental results showed that the proposed system effectively classified 98.3% of pornographic data and 99.8% of non-pornographic data. We expect the proposed method can be applied to the more accurate classification system which uses both video and audio information.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.581-584
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• 2002

인터넷의 급성장과 함께 인터넷을 통해 제공되는 서비스 중 사용자들에게 제공되는 뉴스서비스는 사용자가 원하지 않은 뉴스들까지 제공됨으로써 원하는 뉴스만을 골라서 제공받을 수 있는 시스템의 필요성이 증가하고 있다. 본 논문에서는 사용자가 입력하는 키워드를 이용하여 각 뉴스서버에서 제공하는 뉴스 중 사용자의 요구에 적합한 뉴스를 필터링하는 에이전트 시스템을 구현하였다.

• ETRI Journal
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• v.44 no.5
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.861-873
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• 2009

There are many reasons that fail to get appropriate information in information retrieval. Allomorph is one of the reasons for search failure due to keyword mismatch. This research proposes a method to construct alternative word candidates automatically in order to minimize search failure due to keyword mismatch. Assuming that two words have similar meaning if they have similar co-occurrence words, the proposed method uses the concept of concentration, association word set, cosine similarity between association word sets and a filtering technique using confidence. Performance of the proposed method is evaluated using a manually extracted alternative list. Evaluation results show that the proposed method outperforms the context window overlapping in precision and recall.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.1-8
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• 2019

Recently, consumer distrust has deepened due to blog posts focusing only on public relations due to 'viral marketing'. In addition, marketing projects such as false writing or exaggerated use of the latter phase are one of the most popular programs in 2016 as they are cheaper and more effective than newspaper and TV ads, and the size of advertising costs is set to be a major means of advertising at '3 trillion 394.1 billion won. From this 'viral marketing,' it has become an Internet environment that needs tools to filter information. The fake review filtering application ADDAVICHI presented in this paper extracts, analyzes, and presents blog keywords, total number of searches, reliability and satisfaction when users search for content such as "event" and "taste restaurant." Reliability shows the number of ad posts on a blog, the total number of posts, and satisfaction shows a clean post with confidence divided into positive and negative posts. Finally, the keyword shows a list of the top three words in the review from a positive post. In this way, it helps users interpret information away from advertising.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.23-33
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• 2004

Because of a rapid growth of internet environment, it is also fast increasing to exchange message using e-mail. But, despite the convenience of e-mail, it is rising a currently bi9 issue to waste their time and cost due to the spam mail in an individual or enterprise. Many kinds of solutions have been studied to solve harmful effects of spam mail. Such typical methods are as follows; pattern matching using the keyword with representative method and method using the probability like Naive Bayesian. In this paper, we propose a classification method of spam mails from normal mails using Support Vector Machine, which has excellent performance in pattern classification problems, to compensate for the problems of existing research. Especially, the proposed method practices efficiently a teaming procedure with a word dictionary including a generated index by the n-Gram. In the conclusion, we verified the proposed method through the accuracy comparison of spm mail separation between an existing research and proposed scheme.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.137-152
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• 2005

In this Paper, we Propose an efficient web-based video learning evaluation system that is tailored to individual student's characteristics through the use of user profile-based information filtering. As a means of giving video-based questions, keyframes are extracted based on the location, size, and color information, and question-making intervals are extracted by means of differences in gray-level histograms as well as time windows. In addition, through a combination of the category-based system and the keyword-based system, questions for examination are given in order to ensure efficient evaluation. Therefore, students can enhance school achievement by making up for weak areas while continuing to identify their areas of interest.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.207-210
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• 2007

As the Internet is prevalent in our life, harmful contents have been increasing on the Internet, which has become a very serious problem. Among them, pornographic video is harmful as poison to our children. To prevent such an event, there are many filtering systems which are based on the keyword based methods or image based methods. The main purpose of this paper is to devise a system that classifies the pornographic videos based on the audio information. We use Mel-Cepstrum Modulation Energy (MCME) which is modulation energy calculated on the time trajectory of the Mel-Frequency cepstral coefficients (MFCC) and MFCC as the feature vector and Gaussian Mixture Model (GMM) as the classifier. With the experiments, the proposed system classified the 97.5% of pornographic data and 99.5% of non-pornographic data. We expect the proposed method can be used as a component of the more accurate classification system which uses video information and audio information simultaneously.