• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.505-517
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• 2008

This paper proposes a rewriting algorithm of OWL-DL ontology query in SPARQL. Currently, to obtain inference results of given SPARQL queries, Web ontology repositories construct inference ontology models and match the SPARQL queries with the models. However, an inference model requires much larger space than its original base model, and reusability of the model is not available for other inferrable SPARQL queries. Therefore, the aforementioned approach is not suitable for large scale SPARQL query processing. To resolve tills issue, this paper proposes a novel SPARQL query rewriting algorithm that can obtain results by rewriting SPARQL queries and accomplishing query operations against the base ontology model. To achieve this goal, we first define OWL-DL inference rules and apply them on rewriting graph pattern in queries. The paper categorizes the inference rules and discusses on how these rules affect the query rewriting. To show the advantages of our proposal, a prototype system based on lena is implemented. For comparative evaluation, we conduct an experiment with a set of test queries and compare of our proposal with the previous approach. The evaluation result showed the proposed algorithm supports an improved performance in efficiency of the inferrable SPARQL query processing without loss of completeness and soundness.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.282-291
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• 2004

There has been many researches in TopicMap and RDF which are approach to handle data efficiently with metadata. However, No researches has been performed to service and implement except for presentation and description. In this paper, We suggest the caching mechanism to support an efficient access of knowledgemap and practical knowledgemap service with implementation of TopicMap system. First, We propose a method to navigate Knowledgemap efficiently that includes advantage of former methods. Then, To transmit TopicMap efficiently, We suggest caching mechanism for knowledgemap. This method is that user will be able to navigate knowledgemap efficiently in the viewpoint of human, not application. Therefor the mechanism doesn't cash topics by logical or physical locality but clustering by information and characteristic value of TopicMap. Lastly, we suggest replace mechanism by using graph structure of TopicMap for efficiency of transmission.

• KIEAE Journal
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• v.10 no.3
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• pp.57-62
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• 2010

To effectively use Building Information Modelling (BIM) dealing with semantic information including the entities of building components, the information about building components should be standardized. Like standardized modern buildings. in the past, Korean traditional houses were built according to strict procedures and formats. Therefore, if the Korean traditional house are modelled by using BIM,. not only the Korean traditional house of good quality will be built quickly and cheaply, but also spaces in the existing Korean traditional house will be easily analyzed. However, when analyzing spaces of the Korean traditional house using Space Syntax, some problems are caused in dividing outdoor space such as yard with unclear boundaries, unlike indoor space with clear boundaries surrounded by walls. These comes from the fact that researchers have subjectively divided a space in the house into convex spaces as units for Space Syntax analysis. Therefore, this study aims to develop an objective and rational spatial subdivision technique for Space Syntax analysis of a Korean traditional house modelled by using BIM. We could objectively and reasonably divide a Korean traditional house space into convex spaces by recognizing the building components in the house modelled in the form of Industry Foundation Classes(IFC). Depending on the connection of convex spaces allocated in the spatial subdivision technique, j-graph in Space Syntax could be drawn and the measurements of spatial configurations could be determinded. Through the developed technique, the social properties including the cultural and philosophical aspects of Korean people was identified by measuring the spatial configurations of Korean traditional house. The developed technique will serve as useful means to help architects to find an appropriate purpose of each space for sustainable architecture on the basis of the spatial and social relationships in buildings or urban systems.

• Smart Media Journal
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• v.6 no.1
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• pp.22-29
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• 2017

Due to the rapid advancement and distribution of smart devices of late, document data on the Internet is on the sharp increase. The increment of information on the Web including a massive amount of documents makes it increasingly difficult for users to understand corresponding data. In order to efficiently summarize documents in the field of automated summary programs, various researches are under way. This study uses TextRank algorithm to efficiently summarize documents. TextRank algorithm expresses sentences or keywords in the form of a graph and understands the importance of sentences by using its vertices and edges to understand semantic relations between vocabulary and sentence. It extracts high-ranking keywords and based on keywords, it extracts important sentences. To extract important sentences, the algorithm first groups vocabulary. Grouping vocabulary is done using a scale of specific weight. The program sorts out sentences with higher scores on the weight scale, and based on selected sentences, it extracts important sentences to summarize the document. This study proved that this process confirmed an improved performance than summary methods shown in previous researches and that the algorithm can more efficiently summarize documents.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.549-554
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• 2016

ConceptNet is a common sense knowledge base which is formed in a semantic graph whose nodes represent concepts and edges show relationships between concepts. As it is difficult to make knowledge base integrity, a knowledge base often suffers from incompleteness problem. Therefore the quality of reasoning performed over such knowledge bases is sometimes unreliable. This work presents neural tensor networks which can alleviate the problem of knowledge bases incompleteness by reasoning new assertions and adding them into ConceptNet. The neural tensor networks are trained with a collection of assertions extracted from ConceptNet. The input of the networks is two concepts, and the output is the confidence score, telling how possible the connection between two concepts is under a specified relationship. The neural tensor networks can expand the usefulness of ConceptNet by increasing the degree of nodes. The accuracy of the neural tensor networks is 87.7% on testing data set. Also the neural tensor networks can predict a new assertion which does not exist in ConceptNet with an accuracy 85.01%.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.261-274
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• 1999

Recently, automated business processing systems, so called workflow systems, have been studied. have been studied. even though each task has noticible duration inherently, most of previous models regard it as a simple finite automation where state transitions occur instantly. In this paper, we propose a modeling method for workflow each of whose component task has minimum processing time and due-date. Firstly we provide a priority function to choose the proper alternatives when tasks fail before their due-date. The priority function considers the time-relevant factor and the semantic compatibility level to decide the best alternative. secondly, we identify six different types of execution requirements for tasks with noticible durations. They are serial, parallel, simultaneous start simultaneous termination, interleaved and nested dependency. Note that previous models deal with only serial execution and parallel execution. Lastly, we also propose graph-based methods to detect logical conflicts in time constraint-based workflow specifications.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.95-115
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• 2023

This study designed a data model linked to Wikidata and examined its applicability to increase the utilization of the digital archive records of the National Debt Redemption Movement, registered as World Memory Heritage, and implications were derived by analyzing the existing metadata, thesaurus, and semantic network graph. Through analysis of the original text of the National Debt Redemption Movement records, key data model classes for linking with Wikidata, such as record item, agent, time, place, and event, were derived. In addition, by identifying core properties for linking between classes and applying the designed data model to actual records, the possibility of acquiring abundant related information was confirmed through movement between classes centered on properties. Thus, this study's result showed that Wikidata's strengths could be utilized to increase data usage in local archives where the scale and management of data are relatively small. Therefore, it can be considered for application in a small-scale archive similar to the National Debt Redemption Movement digital archive.