• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1455-1473
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• 2004

This paper suggests a technique, TELOR(Technique of Object Extraction Based on Legacy System Interface for Improvement of Object Reusability) for reuse and reengineering by analyzing the Legacy System interface to distill the meaningful information from them and disassemble them into object units which are to be integrated into the next generation systems. The TELOR method consists of a 4 steps procedure: 1) the interface use case analysis step, 2) the interface object dividing step, 3) the object structure modeling step, and 4) the object model integration step. In step 1, the interface structure and information about the interaction between the user and the Legacy System are obtained. In step 2, the interface information is divided into semantic fields. In step 3, studies and models the structural and collaborative relationship among interface objects. Finally, in step 4, object model integration step, integrates the models and improves the integrated model at a higher level. The objects integration model created through TELOR provides a more efficient understanding of the Legacy System and how to apply it to next generation systems.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.73-85
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• 2011

Lots of valuable textual information is used to extract relations between named entities from literature. Composite kernel approach is proposed in this paper. The composite kernel approach calculates similarities based on the following information:(1) Phrase structure in convolution parse tree kernel that has shown encouraging results. (2) Predicate-argument structure patterns. In other words, the approach deals with syntactic structure as well as semantic structure using a reciprocal method. The proposed approach was evaluated using various types of test collections and it showed the better performance compared with those of previous approach using only information from syntactic structures. In addition, it showed the better performance than those of the state of the art approach.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.83-90
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• 2010

In vocabulary recognition system has reduce recognition rate unrecognized error cause of similar phoneme recognition and due to provided inaccurate vocabulary. Input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Also can't feature extraction properly when phoneme recognition is similar phoneme recognition. In this paper propose vocabulary recognition post-process error correction system using phoneme likelihood based on phoneme feature. Phoneme likelihood is monophone training phoneme data by find out using MFCC and LPC feature extraction method. Similar phoneme is induced able to recognition of accurate phoneme due to inaccurate vocabulary provided unrecognized reduced error rate. Find out error correction using phoneme likelihood and confidence when vocabulary recognition perform error correction for error proved vocabulary. System performance comparison as a result of recognition improve represent MFCC 7.5%, LPC 5.3% by system using error pattern and system using semantic.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.295-302
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• 2007

It is very iterative and complicated work to enter the personal information every time one fills the form-based resume or one joins the new membership page on the internet. Although there are some systems that have the personal information on the computer and fill the membership page automatically, their accuracies are not often satisfactory in that the fields and their values do not match exactly. The research proposes and implements a system that has user's information on the computer and reasons and fills the information automatically that a membership web page(target page) requests using the personal information ontology. During the reasoning process, the target page is analyzed to extract the requested fields. Then the requested field names are converted to the standard field names using synonym ontology. The converted requested fields find the appropriate level in the personal information ontology using ontology match making to generate the final field value. The system not only finds the similar fields but also generates the exact field values by reasoning on the information ontology hierarchy. By experimenting with several membership pages on the web, the system showed higher accuracy over the existing systems. The system can be easily applicable to the cases where one iteratively fills the same information such as resume form.

• Journal of KIISE
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• v.43 no.9
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• pp.983-997
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• 2016

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.479-486
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• 2005

The ontology was proposed to construct the logical basis of semantic web. Ontology represents domain knowledge in the formal form and it enables that machine understand domain knowledge and provide appropriate intelligent service for user request. However, the construction and the maintenance of ontology requires large amount of cost and human efforts. This paper proposes an automatic ontology construction method for defining relation between concepts in the documents. The Proposed method works as following steps. First we find concept pairs which compose association rule based on the concepts in domain specific documents. Next, we find pattern that describes the relation between concepts by clustering the context between two concepts composing association rule. Last, find generalized pattern name by clustering the clustered patterns. To verify the proposed method, we extract relation between concepts and evaluate the result using documents set provide by TREC(Text Retrieval Conference). The result shows that proposed method cant provide useful information that describes relation between concepts.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.255-262
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• 2014

Our previous research[1] suggested object extraction and modeling method based on Fillmore's case grammar. This approach had not considered of use case extraction and method. To solve this problem, we adopt Fillmore's semantic method as linguistic approach into requirement engineering, which refine fillmore's case grammar for extracting and modeling use cases from customer requirements. This Refined mechanism includes the definition of a structured procedure and the representation of visual notations for 'case' modeling. This paper also proposes the use case decision matrix to identify use case size from extracted use cases based on goal oriented requirement engineering(GoRE), which related with the complexity of use case, and also prioritizes the use cases with this matrix. It demonstrates our proposal with the bank ATM system.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.988-992
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• 2009

In this paper, we attempted to extract binary relations between terminologies using composite kernels consisting of convolution parse tree kernels and WordNet verb synset vector kernels which explain the semantic relationships between two entities in a sentence. In order to evaluate the performance of our system, we used three domain specific test collections. The experimental results demonstrate the superiority of our system in all the targeted collection. Especially, the increase in the effectiveness on KREC 2008, 8% in F1, shows that the core contexts around the entities play an important role in boosting the entire performance of relation extraction.