• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.95-107
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• 2014

In general, topographical phenomena are represented graphically by data in the spatial domain, while attributes of the non-spatial domain are expressed by alpha-numeric texts. GIS functions for analysis of attributes in the non-spatial domain remain quite simple, such as search methods and simple statistical analysis. Recently, graph modeling and network analysis of social phenomena are commonly used for understanding various social events and phenomena. In this study, we applied the network analysis functions to the non-spatial domain data of GIS to enhance the overall spatial analysis. For this purpose, a novel design was presented to integrate the spatial database and the graph database, and this design was then implemented into a WebGIS system for better decision makings. The developed WebGIS with underlying synchronized databases, was tested in a simulated application about the selection of water supply households during an epidemic of the foot-and-mouse disease. The results of this test indicate that the developed WebGIS can contribute to improved decisions by taking into account the social proximity factors as well as geospatial factors.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.327-334
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• 2005

The representation of Web Sites with EMFG(Extended Mark Flow Graph) is studied as a new method that represents the complicated Web Sites structure. The Web Sites usually have the number of iteration structures. The representation of these Web Sites with EMFG is too complicated, and so we can not understand the structure of these Web Sites sometimes. Therefore, in this paper, we classify these iteration structures when express Web Sites by EMFG as serial iteration structures and parallel iteration structures and propose the method that can simplify these iteration structures. Then we can reduce number of boxes, arcs, and transitions, and efficiently design and manage Web Sites by using this method.

• Annual Conference on Human and Language Technology
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• 2011.10a
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• pp.49-54
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• 2011

본 논문에서는 회의공지 이메일을 대상으로 하는 개최장소 추출시스템에 대하여 소개한다. 개최장소 추출 시스템은 두 단계로 구성되는데, 첫 번째 단계는 본문에 포함된 개최장소의 추출이고, 두 번째 단계는 추출된 개최장소의 Geocoding이다. 개최장소의 추출을 위하여 문맥 패턴을 분석하여 개최장소가 포함된 문장 주변의 패턴을 반영하는 Local-Grammar Graph를 구축하며, 개최장소의 Geocoding을 위하여는 Addr2Geocode API를 사용한다. 본 논문은 일정공지메일의 개최장소를 추출하기 위한 LGG 방법론 기반의 어휘-통사적 언어 정보를 기술하는 것을 목적으로 한다.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.243-250
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• 2020

Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• Journal of KIISE
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• v.43 no.3
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• pp.380-388
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• 2016

Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.59-66
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• 2005

The organization of the web is progressively more being used to improve search and analysis of information on the web as a large collection of heterogeneous documents. Most people begin at a Web search engine to find information. but the user's pertinent search results are often greatly diluted by irrelevant data or sometimes appear on target but still mislead the user in an unwanted direction. One of the intentional, sometimes vicious manipulations of Web databases is a intentionally biased web page like Google bombing that is based on the PageRank algorithm. one of many Web structuring techniques. In this thesis, we regard the World Wide Web as a directed labeled graph that Web pages represent nodes and link edges. In the Present work, we define the label of an edge as having a link context and a similarity measure between link context and target page. With this similarity, we can modify the transition matrix of the PageRank algorithm. By suggesting a motivating example, it is explained how our proposed algorithm can filter the Web intentionally biased web Pages effective about $60\%% rather than the conventional PageRank.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.05a
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• pp.271-285
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• 2003

In the conduct of this study, a web-enabled healthcare system for the management of hypertension was implemented through a hyperlink-based inference approach. The hyperlink-based inference platform implemented using the hypertext capacity of HTML which ensured accessibility, multimedia facilities, fast response, stability, ease of use and upgrade, and platform independency of expert systems. Many HTML documents, which are hyperlinked to each other based on expert rules, were uploaded beforehand to perform the hyperlink-based inference. The HTML documents were uploaded and maintained automatically by our proprietary tool called the Web-Based inference System (WeBIS) that supports a graphical user interface (GUI) for the input and edit of decision graphs. Nevertheless, the editing task of the decision graph using the GUI tool is a time consuming and tedious chore when the knowledge engineer must perform it manually. Accordingly, this research implemented an automatic generator of the decision graph for the management of hypertension. As a result, this research suggests a methodology for the development of Web-enabled healthcare systems using the hyperlink-based inference approach and, as an example, implements a Web-enabled healthcare system for hypertension, a platform which peformed especially well in the areas of speed and stability.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.569-582
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• 2005

As information and business communication via Internet are growing up, web-based software is wide spread and more important on the viewpoint of software qualify than stand-alone. Research on verification of web content links and web-based Program was tried, but has short on covering various types of web based software and making experiments to be applied in real testing practice. This paper suggests a modeling technique to be applied to dynamic and various types of web-based software. First, it identifies each elements consisting of web-based software and then construct a model of Object Control Flow Graph and Object Relationship Diagram. We can generate test cases covering all test paths of ORD or invoking key points test route. Suggested modeling method and test case selection technique are verified by applying five types of web-based software and compared with other web-based test techniques.