• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Language and Information
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• v.14 no.1
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• pp.165-196
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• 2010

The primary goal of this paper is to propose methods of enriching two heterogeneous ontologies: Sejong Semantic Classes (SJSC) and KorLexNoun 1.5 (KLN). In order to achieve this goal, this study introduces the pros and cons of two ontologies, and analyzes the error patterns found during the fine-grained manual mapping processes between them. Error patterns can be classified into four types: (1) structural defectives involved in node branching, (2) errors in assigning the semantic classes, (3) deficiency in providing linguistic information, and (4) lack of the lexical units representing specific concepts. According to these error patterns, we propose different solutions in order to correct the node branching defectives and the semantic class assignment, to complement the deficiency of linguistic information, and to increase the number of lexical units suitably allotted to their corresponding concepts. Using the results of this study, we can obtain more enriched ontologies by correcting the defects and errors in each ontology, which will lead to the enhancement of practicality for syntactic and semantic analysis.

• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.243-258
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• 2004

Building a domain-suited ontology, as a means to implement the Semantic Web, is widely believed to offer users the benefit of exploiting the semantic knowledge constrained in the application. Electronic Catalog, shortly e-Catalog, manages the information about the goods or conditions play an important role in e-commerce domain. Consequently, semantically enriched yet precise information by the ontology may elaborate the business transactions. In this paper, we analyze the semantic relationships embodied within the catalog domain, as the first step towards the ontological modeling of e-catalog. Exploring ontology should leverage not only the representation of semantic knowledge but also provide the inferencing capability for the model. We employ the EER(extended Entity Relationships) for the basic model. Each modeling construct can be directly translated by DL(Description Logics). Semantic constraints that can be hardly represented in EER are directly modeled in DL.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.2-27
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• 2002

As pact of a long-ranged project that aims at establishing database-theoretic semantics as a model of computational semantics, this presentation focuses on the development of a syntactic component for processing strings of words or sentences to construct semantic data structures. For design arid modeling purposes, the present treatment will be restricted to the analysis of some problematic constructions of Korean involving semi-free word order, conjunction arid temporal anchoring, and adnominal modification and antecedent binding. The present work heavily relies on Hausser's (1999, 2000) SLIM theory for language that is based on surface compositionality, time-linearity arid two other conditions on natural language processing. Time-linear syntax for natural language has been shown to be conceptually simple and computationally efficient. The associated semantics is complex, however, because it must deal with situated language involving interactive multi-agents. Nevertheless, by processing input word strings in a time-linear mode, the syntax cart incrementally construct the necessary semantic structures for relevant queries and valid inferences. The fragment of Korean syntax will be implemented in Malaga, a C-type implementation language that was enriched for both programming and debugging purposes arid that was particluarly made suitable for implementing in Left-Associative Grammar. This presentation will show how the system of syntactic rules with constraining subrules processes Korean sentences in a step-by-step time-linear manner to incrementally construct semantic data structures that mainly specify relations with their argument, temporal, and binding structures.

• Language and Information
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• v.17 no.2
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• pp.51-66
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• 2013

Korean is a wh-in-situ language where the wh-expression stays in situ with an obligatory Q-particle marking its interrogative scope. This paper briefly reviews some basic properties of the wh-question construction in Korean and shows how a typed feature structure grammar, HPSG (Pollard and Sag 1994, Sag et al. 2003), together with the notions of 'type hierarchy' and 'constructions', can provide a robust basis for parsing the wh-construction in the language. We show that this system induces robust syntactic structures as well as enriched semantic representations for real-time applications such as machine translation, which require deep processing of the phenomena concerned.

• Language and Information
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• v.15 no.2
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• pp.1-21
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• 2011

Of the coordination structures in Korean, the symmetric and asymmetric properties of verbal coordination have challenged both theoretical and computational approaches. This paper shows how a typed feature structure grammar, HPSG, together with the notions of 'type hierarchy' and 'constructions', can provide a robust basis for parsing (un)tensed verbal coordination as well as pseudo-coordination found in the language. We show that the analysis sketched here and computationally implemented in the existing resource grammar for Korean, Korean Resource Grammar (KRG), can yield proper syntactic structures as well as enriched semantic representations for real-time applications such as machine translation.

• Language and Information
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• v.19 no.1
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• pp.19-36
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• 2015

Honorific agreement is one of the main properties in languages like Korean, playing a pivotal role in appropriate communication. This makes the deep processing of honorific information crucial in various computational applications such as spoken language translation and generation. This paper shows that departing from previous literature, an adequate analysis of Korean honorification needs to involve a system that has access not only to morpho-syntax but to semantics and pragmatics as well. Along these lines, this paper offers a constraint-based HPSG analysis of Korean honorification in which the enriched lexical information tightly interacts with syntactic, semantic, and pragmatic levels for the proper honorific system.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.207-218
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• 2012

In recent years, the skyline query paradigm has been established as a reliable method for database query personalization. While early efficiency problems have been solved by sophisticated algorithms and advanced indexing, new challenges in skyline retrieval effectiveness continuously arise. In particular, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. We addressed the special challenges presented by linked open data in previous work; and now further extend this work, with a heuristic workflow to boost efficiency. This is necessary; because the new view on linked open data dominance has serious implications for the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as: we present an intuitive skyline query paradigm to deal with linked open data; we provide an effective dominance definition, and establish its theoretical properties; we develop innovative skyline algorithms to deal with the resulting challenges; and we design efficient heuristics for the case of predicate equivalences that may often happen in linked open data. We extensively evaluate our new algorithms with respect to performance, and the enriched skyline semantics.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.97-106
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• 2007

A core technology that may realize the Semantic Web is Ontology. The OWL (Web Ontology Language) has been positioned as a standard language. It requires technical expertise to directly represent the domain knowledge in OWL. Based on our experience of analyzing the fundamental relationships of concepts in e-catalog domain, we have developed a visual product information modeler called PROMOD. The modeling editor makes it possible to automatically generate the OWL codes for the given product information. We employ an Extended Entity-Relationship for conceptual modeling, enriched with modeling elements specialized for the product domain. In this paper, we present our translation schemes from EER model to OWL codes, and how to design and implement the modeling editor. We also provide a scenario to demonstrate the usage of the editor in practice.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.