• Journal of Korea Multimedia Society
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• v.7 no.10
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• pp.1354-1369
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• 2004

This paper presents a logical structure analysis method which transforms Web documents into XML ones. The proposed method consists of three phases: visual grouping, element identification, and logical grouping. To produce a logical structure more accurately, the proposed method defines a document model that is able to describe logical structure information of topic-specific document class. Since the proposed method is based on a visual structure from the visual grouping phase as well as a document model that describes logical structure information of a document type, it supports sophisticated structure analysis. Experimental results with HTML documents from the Web show that the method has performed logical structure analysis successfully compared with previous works. Particularly, the method generates XML documents as the result of structure analysis, so that it enhances the reusability of documents.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1131-1134
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• 2001

가상문서 개념에 기반을 둔 디지털도서관에서는 기존의 멀티미디어 문서를 활용하여 새로운 view를 생성하고 지식을 창출할 수 있는 기능을 제공한다. 이 접근 방법의 초점은 링크를 사용하여 기존의 자원을 연결하여 가상문서를 생성하고 필요한때 필요한 부분만 가져다가 통합하여 재현시킬 수 있는 기능과 이렇게 생성된 가상문서를 총체적으로 혹은 부분적으로 검색할 수 있다는 것이다. 반면에 W3C에서는 Topic Maps라는 개념을 표준안으로 하였는데, 이 개념은 근본 취지에 있어 가상문서의 개념과 매우 유사하나 문서를 끌어서 통합하는 기능이나 부분문서를 가져오는 기능 등이 없다. 여기서도 기존 문서에 링크를 걸어 사용자가 원하는 토픽이 연결될 수 있도록 하는 기능을 규정하고 있는데, 지식 표현 및 추론 등 다양한 응용에 관한 연구가 진행되어 오고 있다. 따라서 본 연구에서는 충남대학교에서 개발한 가상문서 기반 디지털도서관 개념에 이 Topic Maps 기능을 연결하여 호환성을 제공하는 것을 목표로 한다. 이러한 호환성은 디지털도서관 분야에서의 핵심 이슈이며, 실용적인 관점에서 가상문서 기반 디지털도서관의 가용성을 향상시켜 궁극적으로 외부 시스템과도 연계될 수 있는 기반을 제공할 것이다.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.730-739
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• 2010

One of core technology in Web 2.0 is tagging, which is applied to multimedia data such as web document of blog, image and video etc widely. But unlike expectation that the tags will be reused in information retrieval and then maximize the retrieval efficiency, unacceptable retrieval results appear owing to toot limitation of tag. In this paper, in the base of preceding research about image retrieval through tag clustering, we design and implement a topic map generation system which is a semantic knowledge system. Finally, tag information in cluster were generated automatically with topics of topic map. The generated topics of topic map are endowed with mean relationship by use of WordNet. Also the topics are endowed with occurrence information suitable for topic pair, and then a topic map with semantic knowledge system can be generated. As the result, the topic map preposed in this paper can be used in not only user's information retrieval demand with semantic navigation but alse convenient and abundant information service.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2607-2613
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• 2012

The results of inherent knowledge based generic summarization are influenced by the composition of sentence in document set. In order to resolve the problem, this papser propses a new generic document summarization which uses clustering of semantic feature of document and coherence of document cluster. The proposed method clusters sentences using semantic feature deriving from NMF(non-negative matrix factorization), which it can classify document topic group because inherent structure of document are well represented by the sentence cluster. In addition, the method can improve the quality of summarization because the importance sentences are extracted by using coherence of sentence cluster and the cluster refinement by re-cluster. The experimental results demonstrate appling the proposed method to generic summarization achieves better performance than generic document summarization methods.

• Journal of KIISE
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• v.42 no.12
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• pp.1522-1534
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• 2015

This work presents a new method that can predict, without the use of training data, whether an input essay is written on a given topic. ConceptNet is a common-sense knowledge base that is generated automatically from sentences that are extracted from a variety of document types. An essay prompt is the topic that an essay should be written about. The method that is proposed in this paper uses ConceptNet and an essay prompt to decide whether or not an input essay is off-topic. We introduce a way to find the shortest path between two nodes on ConceptNet, as well as a way to calculate the semantic similarity between two nodes. Not only an essay prompt but also a student's essay can be represented by concept nodes in ConceptNet. The semantic similarity between the concepts that represent an essay prompt and the other concepts that represent a student's essay can be used for a calculation to rank "on-topicness" ; if a low ranking is derived, an essay is regarded as off-topic. We used eight different essay prompts and a student-essay collection for the performance evaluation, whereby our proposed method shows a performance that is better than those of the previous studies. As ConceptNet enables the conduction of a simple text inference, our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.531-536
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• 2014

With the very large quantity of information available on the Internet, techniques for dealing with the abundance of documents have become increasingly necessary but the problem of processing information in the documents is still technically challenging and remains under study. Automatic document summary techniques have been considered as one of critical solutions for processing documents to retain the important points and to remove duplicated contents of the original documents. In this paper, we propose a document summarization technique that uses a fuzzy theory. Proposed summary technique solves the ambiguous problem of various features determining the importance of the sentence and the experiment result shows that the technique generates better results than other previous techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.392-393
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• 2012

This paper proposes a new document clustering method using clustering and Wikipedia. The proposed method can well represent the concept of cluster topics by means of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of Wikipedia. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.251-260
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• 2016

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patent, and project reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer the further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a RDB-to-Triples transformer is implemented. Lastly, we show an experiment on R&D data integration using the lightweight ontology, triples generation, and visualization and navigation of the knowledge map.

• The Journal of Korean Medicine
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• v.43 no.1
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• pp.73-86
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• 2022

Objectives: In the health care industry, the influence of online reviews is growing. As medical services are provided mainly by providers, those services have been managed by hospitals and clinics. However, direct promotions of medical services by providers are legally forbidden. Due to this reason, consumers, like patients and clients, search a lot of reviews on the Internet to get any information about hospitals, treatments, prices, etc. It can be determined that online reviews indicate the quality of hospitals, and that analysis should be done for sustainable hospital marketing. Method: Using a Python-based crawler, we collected reviews, written by real patients, who had experienced Korean medicine, about more than 14,000 reviews. To extract the most representative words, reviews were divided by positive and negative; after that reviews were pre-processed to get only nouns and adjectives to get TF(Term Frequency), DF(Document Frequency), and TF-IDF(Term Frequency - Inverse Document Frequency). Finally, to get some topics about reviews, aggregations of extracted words were analyzed by using LDA(Latent Dirichlet Allocation) methods. To avoid overlap, the number of topics is set by Davis visualization. Results and Conclusions: 6 and 3 topics extracted in each positive/negative review, analyzed by LDA Topic Model. The main factors, consisting of topics were 1) Response to patients and customers. 2) Customized treatment (consultation) and management. 3) Hospital/Clinic's environments.