• Journal of Digital Convergence
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• v.12 no.6
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• pp.245-252
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• 2014

Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. But, the ordinary users don't know well the complex formal query language and schema of the knowledge base. So, the system should interpret the meaning of user's keywords. In this paper, we describe a user query interpretation system for the semantic retrieval of multimedia contents. Our system is ontological knowledge base-driven in the sense that the interpretation process is integrated into a unified structure around a knowledge base, which is built on domain ontologies.

• Journal of Internet Computing and Services
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• v.4 no.6
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• pp.33-42
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• 2003

A XML Instance repository model based on the Edge-Labeled Graph is suggested for storing the XML instance in Relational Databases, This repository model represents the XML instance as a data graph based on the Edge-Labeled Graph, extracts the defined value based on the structure of data path, element, attribute, and table index table presented as database schema, and stores these values using the Mapper module, In order to support querry, XML repository model offers the module translating XQL which is a query language under XPATH to SQL, and has DBtoXML generator module restoring the stored XML instance. As a result, it is possible to represent the storage relationship between the XML instances and the proposed repository model in terms of Graph-based Path, and it shows the possibility of easy search of specific element and attribute information.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.37-42
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• 2012

The pseudo relevance feedback suggests that frequent words at the top documents are related to initial query. However, the main drawback associated with the term frequency method is the fact that it relies on feature independence, and disregards any dependencies that may exist between words in the text. In this paper, we propose query expansion based on word graph using term proximity. It supplements term frequency method. On TREC WT10g test collection, experimental results in MAP(Mean Average Precision) show that the proposed method achieved 6.4% improvement over language model.

• Korean Journal of Microbiology
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• v.39 no.2
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• pp.67-75
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• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.16-18
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• 2005

웹은 사용자가 원하는 정보를 쉽고 정확하게 검색할 수 있도록 웹 문서를 자료구조화하여 보다 신뢰성 있는 패턴을 추출하고 사용자의 특성과 행동 패턴을 적용하여 개인화 하여야한다. 본 논문에서는 개인화하기 위한 전처리 과정으로서 웹 문서를 구조화 하는 방법을 제안한다. 제안 방법은 기본적으로 웹 문서 태그의 하이퍼링크를 깊이 우선 탐색 알고리즘을 사용하여 방향그래프를 만드는 것이다. 이때 웹 문서 태그 탐색 시 플래시, 스크립트 등의 찾기 힘든 하이퍼링크를 찾는 문제와 '뒤로' 버튼 사용 시 웹 접근로그에 기록되지 않는 문제점을 보완한다. 이를 위해 클릭 스트림을 스택에 저장하여 이미 만들어진 방향그래프와 비교하여 새롭게 찾은 정점과 간선을 추가함으로써 보다 신뢰성높은 방향그래프를 만든다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.247-249
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• 2013

Even though Relational Databases have been widely used in many enterprises, the relations among entities are not managed effectively and efficiently. In order to analyze Big Data, it is absolutely needed to express various relations among entities in a graphical form. In this paper, we define Graph Databases and its structure. And then, we check out their characteristics such as transaction, consistency, availability, retrieval function, and expandability. Also, we appropriate or inappropriate subjects for application of Graph Databases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.621-623
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• 2021

Open science policies are spreading that disclose, share, and utilize research results produced through government public funds. As a policy to revitalize open science, interest in research support services that allow easy search, access, and reuse of results is increasing. To support services to provide researchers with various information, we propose a research resource knowledge graph model to meaningfully express the relationship between the scattered various outcome data. In this paper, it contributes to the improvement of the service of the national research data platform DataON by meaningfully connecting national R&D task information, researcher information, performance information, and research data information.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.189-194
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• 2012

In this paper, we propose a query expansion method based on word graphs using pseudo-relevant and pseudo non-relevant documents to achieve performance improvement in information retrieval. The initially retrieved documents are classified into a core cluster when a document includes core query terms extracted by query term combinations and the degree of query term proximity. Otherwise, documents are classified into a non-core cluster. The documents that belong to a core query cluster can be seen as pseudo-relevant documents, and the documents that belong to a non-core cluster can be seen as pseudo non-relevant documents. Each cluster is represented as a graph which has nodes and edges. Each node represents a term and each edge represents proximity between the term and a query term. The term weight is calculated by subtracting the term weight in the non-core cluster graph from the term weight in the core cluster graph. It means that a term with a high weight in a non-core cluster graph should not be considered as an expanded term. Expansion terms are selected according to the term weights. Experimental results on TREC WT10g test collection show that the proposed method achieves 9.4% improvement over the language model in mean average precision.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.514-516
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• 2005

국내외적으로 흠 네트워크의 보급이 활발히 이루어지고 있으며, 이러한 홈 네트워크는 현재의 데이터 네트워크 및 제어 네트워크 중심에서 향후 다수의 미디어 콘텐츠를 보유하는 A/V 흠 네트워크로 발전될 전망이다. 이런 환경 하에서 사용자들은 현재 P2P 파일 공유 시스템에서 이루어지는 것처럼 자신들의 관심에 기초하여 다른 홈 네트워크에 있는 미디어 콘텐츠들을 공유할 것으로 보인다. 본 논문에서는 이처럼 예상되는 사용자들의 요구를 효율적으로 지원하기 위하여 사용자들의 관심 사항을 효과적으로 반영하며 보장된 응답시간 내에 미디어 콘텐츠 검색을 제공하기 위한 P2P 오버레이 네트워크 구성 방안인 IGN (Interest-based Grouping Network)을 제시한다. Chord와 드브루인 (do Bruijn) 그래프로 구성되는 IGN은 structured P2P의 세밀한 검색 지원에 대한 한계와 unstructured P2P의 보장되지 않은 최대 검색 시간의 문제점을 해결하며, 보장된 최대 검색 시간 내에 사용자들의 관심 사항을 세일하게 반영하는 검색을 지원할 것으로 예상된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.624-632
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• 2022

Relationship networks such as an interpersonal relationship network or navigation route search can be expressed in graph form. However, as the amount of data increase, there is a problem that it is difficult to search for the desired data when it is displayed on one screen. In this paper, we propose a visualization method for searching for people, searching for the shortest path between people, and using graphs to express an interpersonal relationship network with many nodes. Unlike the search for the shortest path in the routing table, the shortest path in the interpersonal relationship network should be changeable according to the intension or importance of the researcher or user who is analyzing it. To this end, the BFS algorithm was modified to apply the characteristics of the interpersonal relationship network. For the verification of the results, the data in the character relationship information of the Korean Classics DB in the Korean Classics Translation Institute was used.