• The Journal of Information Systems
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• v.10 no.1
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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.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.438-449
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• 2018

Recently, various graph data have been utilized in various fields such as social networks and citation networks. As the graph changes dynamically over time, it is necessary to manage the graph historical data for tracking changes and retrieving point-in-time graphs. Most historical data changes partially according to time, so unchanged data is stored redundantly when data is stored in units of time. In this paper, we propose a graph history storage management method to minimize the redundant storage of time graphs. The proposed method continuously detects the change of the graph and stores the overlapping subgraph in intersection snapshot. Intersection snapshots are connected by a number of delta snapshots to maintain change data over time. It improves space efficiency by collectively managing overlapping data stored in intersection snapshots. We also linked intersection snapshots and delta snapshots to retrieval the graph at that point in time. Various performance evaluations are performed to show the superiority of the proposed scheme.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.498-508
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• 2005

A shape data is used one oi most important feature for image retrieval as data to reflect meaning of image. Especially, structural feature of shape is widely studied because it represents primitive properties of shape and relation information between basic units well. However, most structural features of shape have the problem that it is not able to guarantee an efficient search time because the features are expressed as graph or tree. In order to solve this problem, we generate curvature-based morphological graph, End design key to cluster shapes from this graph. Proposed this graph have contour features and morphological features of a shape. Shape retrieval is accomplished by stages. We reduce a search space through clustering, and determine total similarity value through pattern matching of external curvature. Various experiments show that our approach reduces computational complexity and retrieval cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2211-2232
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• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.

• Knowledge Management Research
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• v.25 no.3
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• pp.99-119
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• 2024

This research aims to develop high-quality generative AI services by overcoming the limitations of existing Retrieval-Augmented Generation (RAG) models and implementing an enhanced graph-based RAG system to improve knowledge-based question answering (QA) systems. While traditional RAG models demonstrate high accuracy and fluency by utilizing retrieved information, their accuracy can be compromised due to the use of pre-loaded knowledge without rework. Additionally, the inability to incorporate real-time data after the RAG configuration leads to a lack of contextual understanding and potential biased information. To address these limitations, this study implements an enhanced RAG system utilizing graph technology. This system is designed to efficiently search and utilize information. In particular, LangGraph is employed to evaluate the reliability of retrieved information and to generate more accurate and improved answers by integrating various information. Furthermore, the specific operation method, key implementation steps, and case studies are presented with implementation code and verification results to enhance understanding of Advanced RAG technology. This research provides practical guidelines for actively implementing enterprise services utilizing Advanced RAG, making it significant.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.533-538
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• 2001

In this paper, we extend FARGs single mode attribute to multiple attributes for real image application and present a new CBIR using FMARG(Fuzzy Multiple Attribute Relational Graph), which can handle queries involving multiple attributes, not only object label, but also color, texture and spatial relation. In the experiment using the synthetic image database of 1,024 images and the natural image database of 1.026 images built from NETRA database and Corel Draw, the proposed approach shows 6~30% recall increase in the synthetic image database and a good performance, at the displacements and the retrieved number of similar images in the natural image database, compared with the single attribute approach.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.422-427
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• 2009

We present a real-time graph drawing tool for mathematics education. We developed a sketch-based graph drawing interface that recognizes the schematic sketch of a graph. Our system generates figures displaying useful supplementary information such as auxiliary lines, abscissas, and ordinates. The resulting graphs are very similar to the graphs commonly found in textbooks. We also developed a graph retrieval system that makes rapid graph drawing feasible.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.551-562
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• 2000

Content-based image retrieval system retrieves an image from a database using visual features. Among approaches to express visual aspects in queries, 'query by sketch' is most convenient and expressive. However, every 'query by sketch' system has the query imperfectness problem. GContent-based image retrieval system retrieves an image from a database using visual features. Among approaches to express visual aspects in queries, 'query by sketch' is most convenient and expressive. However, every 'query by sketch' system has the query imperfectness problem. Generally, the query image produced by a user is different from the intended target image. To overcome this problem, many image retrieval systems use the spatial relationships of the objects, instead of pixel coordinates of the objects. In this paper, a query-converting algorithm for an image retrieval system, which uses the spatial relationship of every two objects as an image feature, is proposed. The proposed algorithm converts the query image into a graph that has the minimum number of edges, by eliminating every transitive edge. Since each edge in the graph represents the spatial relationship of two objects, the elimination of unnecessary edges makes the retrieval process more efficient. Experimental results show that the proposed algorithm leads the smaller number of comparison in searching process as compared with other algorithms that do not guarantee the minimum number of edges.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.53-61
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• 2016

This research proposes the index optimization as a classification task and application of the graph based KNN. We need the index optimization as an important task for maximizing the information retrieval performance. And we try to solve the problems in encoding words into numerical vectors, such as huge dimensionality and sparse distribution, by encoding them into graphs as the alternative representations to numerical vectors. In this research, the index optimization is viewed as a classification task, the similarity measure between graphs is defined, and the KNN is modified into the graph based version based on the similarity measure, and it is applied to the index optimization task. As the benefits from this research, by modifying the KNN so, we expect the improvement of classification performance, more graphical representations of words which is inherent in graphs, the ability to trace more easily results from classifying words. In this research, we will validate empirically the proposed version in optimizing index on the two text collections: NewsPage.com and 20NewsGroups.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3219-3228
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• 1999

Currently, car information service systems are lack of providing retrieval service, because most of them aim for buying and selling. Thus, in this paper, we design and implement CISS(Car Information Service System on Internet) to provide efficient retrieval for car information. In CISS, we collect information of cars on Internet and construct the lattice structure for efficient retrieval. And we provide retrieval convenience by implementing of Car Browser that permits gradual refinement of the index term by browsing through lattice graph and that permits keyword-based retrieval.