• Kyungpook Mathematical Journal
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• 제58권2호
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• pp.399-426
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• 2018

Cofinite graphs and cofinite groupoids are defined in a unified way extending the notion of cofinite group introduced by Hartley. These objects have in common an underlying structure of a directed graph endowed with a certain type of uniform structure, called a cofinite uniformity. Much of the theory of cofinite directed graphs turns out to be completely analogous to that of cofinite groups. For instance, the completion of a directed graph Γ with respect to a cofinite uniformity is a profinite directed graph and the cofinite structures on Γ determine and distinguish all the profinite directed graphs that contain Γ as a dense sub-directed graph. The completion of the underlying directed graph of a cofinite graph or cofinite groupoid is observed to often admit a natural structure of a profinite graph or profinite groupoid, respectively.

• 정보기술과데이타베이스저널
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• 제7권1호
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• pp.55-65
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• 2000

Information semantics is a well-known issue in areas of information systems researches. It describes what data mean, how they are created, where they can be applied to ; thus, it provides indispensable information for management of data. This article proposes to formalize information semantics by the processes that data are created or transformed. A scheme is proposed to describe an information production structure, which is called an information structure graph. An information structure graph is a directed graph, whose leaves are primary input data objects and whose root and internal nodes are output objects. Information semantics is derived from an information structure graph that has data as its root. For this, rules are proposed to manipulate and compare graphs. The structural relationships among information structure graphs are mapped into semantic relationships among data.

• Journal of Information Technology Applications and Management
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• 제26권1호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4126-4142
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• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• 한국정보시스템학회지:정보시스템연구
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• 제10권1호
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• pp.257-278
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• 2001

An IR(information retrieval) index for dynamic document databases where insertion, deletion, and update of documents happen frequently should be frequently updated. As the conventional structure of IR index is, however, focused on the information retrieval purpose, its structure is inefficient to handle dynamic update of it. In this paper, we propose a new structure for IR Index, we call it Index Graph, which is organized by connecting multiple indexes into a graph structure. By analysis and experiment, we prove the Index Graph is superior to the conventional structure of IR index in the performance of insertion, deletion, and update of documents as well as the performance of information retrieval.

• Annals of Fuzzy Mathematics and Informatics
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• 제16권3호
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• pp.363-371
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• 2018

The main objective of this paper is to connect fuzzy theory, graph theory and fuzzy graph theory with algebraic structure. We introduce the notion of fuzzy graph of semigroup, the notion of fuzzy ideal graph of semigroup as a generalization of fuzzy ideal of semigroup, intuitionistic fuzzy ideal of semigroup, fuzzy graph and graph, the notion of isomorphism of fuzzy graphs of semigroups and regular fuzzy graph of semigroup and we study some of their properties.

• 대한수학회보
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• 제58권2호
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• pp.527-535
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• 2021

The fundamental group of a high dimensional graph manifold canonically has a graph of groups structure. We analyze the group action on the associated Bass-Serre tree and study the algebraic ranks of the fundamental groups of high dimensional graph manifolds.

• 한국멀티미디어학회논문지
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• 제5권1호
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• pp.114-119
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• 2002

철도선로망 제어시스템에서 신속한 철도선로의 배정은 실시간 선로배정의 중요한 요소인데, 이 문제의 해결을 위해서는 먼저 철도 선로망의 위상을 정확하게 표현해야 한다. 그래프는 망 구조를 표현하는데 적절한 자료구조이지만 철도 선로망을 표현하는 데에는 부적절하다. 이 논문에서는 철도 선로망의 위상구조를 정확하게 표현할 수 있는 새로운 자료구조인 선로그래프(railway graph) 개념을 정의한다. 그리고 정의된 선로그래프에서의 경로탐색 알고리즘과 선로그래프를 이용한 하향식 철도 선로망 모델링 방법을 설명한다.

• 한국컴퓨터정보학회논문지
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• 제18권7호
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• pp.111-123
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• 2013

소프트웨어의 취약성을 검증하기 위하여 소프트웨어의 구조를 유추하여 유추된 구조를 활용하여 테스트하는 방법이 주목받고 있다. 이와 같은 방법을 사용하기 위해서 효과적인 소프트웨어의 구조 유추 방법이 요구된다. 많이 사용되는 DFG(Data Flow Graph), CFG(Control Flow Graph) 이나 CFA(Control Flow Automata)와 같은 그래프나 트리 방식은 소프트웨어 모델을 구조적으로 표현하지 못하는 단점을 가진다. 본 논문에서는 이러한 단점을 극복할 수 있는 방법을 제시한다. 제시된 방법은 바이너리 코드에 다양한 입력데이터 들을 부여하여 입력데이터별 CFG를 생성하고, 생성된 CFG들이 구조적으로 표현될 수 있도록 계층적 제어 흐름 그래프(Hierarchical Control Flow Graph, HCFG)를 작성한다. 또한 제안하는 HCFG을 생성하는데 요구되는 그래프의 구성요소와 점진적 그래프 생성 알고리듬도 제시한다. 제안한 방법론을 공개된 SMTP(Simple Mail Transfer Protocol) 서버 프로그램에 적용시켜 소프트웨어의 모델을 작성하는 실험을 수행하고, 생성된 모델과 실제 소프트웨어 구조를 비교 분석한다.