• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.429-437
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• 2022

Recently, the demand for a QA (Question Answering) system for human-machine communication has increased. Among the QA systems, a closed domain QA system that can handle spatial-related questions is called GeoQA. In this study, a new type of graph database, LPG (Labeled Property Graph) was used to overcome the limitations of the RDF (Resource Description Framework) based database, which was mainly used in the GeoQA field. In addition, GraphQL (Graph Query Language), an API-type query language, is introduced to address the fact that the LPG query language is not standardized and the GeoQA system may depend on specific products. In this study, database was built so that answers could be retrieved when spatial-related questions were entered. Each data was obtained from the national spatial information portal and local data open service. The spatial relationships between each spatial objects were calculated in advance and stored in edge form. The user's questions were first converted to GraphQL through FOL (First Order Logic) format and delivered to the database through the GraphQL server. The LPG used in the experiment is Neo4j, the graph database that currently has the highest market share, and some of the built-in functions and QGIS were used for spatial calculations. As a result of building the system, it was confirmed that the user's question could be transformed, processed through the Apollo GraphQL server, and an appropriate answer could be obtained from the database.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.31-39
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• 2011

The facial recognition algorithms using Gabor wavelet based face graph produce very good performance while they have some weakness such as a large amount of computation and an irregular result depend on initial location. We proposed a fully automatic facial recognition algorithm using a Gabor feature based geometric deformable face graph matching. The initial location and size of a face graph can be selected using Adaboost detection results for speed-up. To find the best face graph with the face model graph by updating the size and location of the graph, the geometric transformable parameters are defined. The best parameters for an optimal face graph are derived using an optimization technique. The simulation results show that the proposed algorithm can produce very good performance with recognition rate 96.7% and recognition speed 0.26 sec for FERET database.

• Korean Journal of Cognitive Science
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• v.27 no.1
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• pp.43-60
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• 2016

Recently, Unsupervised Word Sense Disambiguation research has focused on Graph based disambiguation. Graph-based disambiguation has built a semantic graph based on words collocated in context or sentence. However, building such a graph over all ambiguous word lead to unnecessary addition of edges and nodes (and hence increasing the error). In contrast, our work uses Word2Vec to consider the most similar words to an ambiguous word in the context or sentences, to rebuild a graph of the matched words. As a result, we show a higher F1-Measure value than the previous methods by using Word2Vec.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• East Asian mathematical journal
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• v.21 no.2
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• pp.249-260
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• 2005

Experimentation using computer plays an important part in education and research in graph theory. The purpose of this paper is to develop the CAS techniques for the hands-on approach in graph theory specifically on the topic of constructing the spanning tree. This paper discusses the advantages of CAS as the software system for doing graph theory and introduces the software solutions integrating multimedia user interface developed by the author, which extend the functionality of the existing CAS-based graph theory software package.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.242-248
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• 2014

With the rapidly growing amount of information represented in RDF format, efficient querying of RDF graph has become a fundamental challenge. SPARQL is one of the most widely used query languages for retrieving information from RDF dataset. SPARQL is not only simple in its syntax but also powerful in representation of graph pattern queries. However, users need to make a lot of efforts to understand the ontology schema of a dataset in order to compose a relevant SPARQL query. In this paper, we propose a graph query formulation and processing scheme based on ontology schema information which can be obtained by summarizing RDF graph. In the context of the proposed querying scheme, a user can interactively formulate the graph queries on the graphic user interface without making efforts to understand the ontology schema and even without learning SPARQL syntax. The graph query formulated by a user is transformed into a set of class paths, which are stored in a relational database and used as the constraint for search space reduction when the relational database executes the graph search operation. By executing the LUBM query 2, 8, and 9 over LUBM (10,0), it is shown that the proposed querying scheme returns the complete result set.

• Journal of KIISE
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• v.44 no.7
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• pp.710-718
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• 2017

As graphs are becoming increasingly large, the costs for storing and managing data are increasing continuously. Shortest path discovery over a large graph requires long running time due to frequent disk I/Os and high complexity of the graph data. Recently, graph summarization techniques have been studied, which reduce the size of graph data and disk I/Os by representing highly dense subgraphs as a single super-node. Decompressing should be minimized for efficient shortest path discovery over the summarized graph. In this paper, we analyze the decompression performance of a summarized graph and propose an approximate technique that discovers the shortest path quickly with a minimum error ratio. We also propose an exact technique that efficiently discovered the shortest path by exploiting an index built on paths containing super-nodes. In our experiments, we showed that the proposed technique based on the summarized graph can reduce the running time by up to 70% compared with the existing techniques performed on the original graph.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.37-47
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• 2013

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.