• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.553-555
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• 2004

최근 인터넷의 급속한 성장과 더불어 전자메일(I-Mail)은 의사교환의 필수적인 매체로 사용 되어지고 있다. 그러나 편리하고 비용이 들지 앉는 장정을 이용해 엄청난 양의 스맴 메일이 매일같이 솎아져 오고, 이를 해결하기 위한 다양한 연구들이 제시되어져 왔다. 특히. 문서 분류에 널리 쓰이는 베이지안 분류자(Bayesian classifier)가 가장 널리 이용되어지고 있는데, 정확도와 재현율에서 비교적 우수한 성능을 보이고 있다. 그러나 몇 가지 문제점을 갖고 있는데, 첫째, 사전에 사용자에 의해 스팸. 논스팸 메일에 대한 충분한 학습이 선행되어야 하는 정, 둘째, 필터링을 위한 연산시간이 소요되는 점, 셋째, 필터링의 대상이 되는 메일 본문의 내용이 적을 경우 정확한 필터링이 어렵다는 정 등의 문제점이 있다. 본 논문에서는 마지막 문제점으로 지적된 메일 본문의 내용이 적을 경우 즉, 연산을 위한 특징적인 단어들의 부족으로 정확한 분류가 불가능한 경우의 해결방안으로 온틀로지와 Semantic Enrichment 기법을 이용한 스팸 메일 필터링 시스템을 제안한다. 실험 결과, 제안하는 시스템이 베이지안 분류자를 이용한 분류 시스템보다 정확도에서 4.1%, 재현율에서 10.5%. 그리고 F-measure에서 7.64%의 성능향상을 보였다.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• English Language & Literature Teaching
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• v.9 no.spc
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• pp.149-165
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• 2003

This paper is aimed at the investigation of pragmatic contributions to the identification of explicatures. An explicature is the result of fleshing out the semantic representation of an utterance. The basic assumption of the paper is that the process of the developing the semantic representation into an explicature depends heavily on contextual information. Therefore, we are concerned with the way in which hearers use contextual information to flesh rut or develop the semantic representation of an utterance. The identification of explicatures includes both the recovery of the proposition expressed and the recovery of what we called higher-level explicatures. There are three subtasks involved in the recovery of the proposition expressed: reference assignment disambiguation and enrichment On the other hand, there are two subtasks involved in the recovery of higher-level explicatures: attitudes and speech acts.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.199-203
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• 2023

In recent decades, the growth of communication technology has resulted in an explosion of data-related information. Ontology perception is being used as a growing requirement to integrate data and unique functionalities. Ontologies are not only critical for transforming the traditional web into the semantic web but also for the development of intelligent applications that use semantic enrichment and machine learning to transform data into smart data. To address these unclear facts, several researchers have been focused on expanding ontologies and semantic web technologies. Due to the lack of clear-cut limitations, ontologies would not suffice to deliver uncertain information among domain ideas, conceptual formalism supplied by traditional. To deal with this ambiguity, it is suggested that fuzzy ontologies should be used. It employs Ontology to introduce fuzzy logical policies for ambiguous area concepts such as darkness, heat, thickness, creaminess, and so on in a device-readable and compatible format. This survey efforts to provide a brief and conveniently understandable study of the research directions taken in the domain of ontology to deal with fuzzy information; reconcile various definitions observed in scientific literature, and identify some of the domain's future research-challenging scenarios. This work is hoping that this evaluation can be treasured by fuzzy ontology scholars. This paper concludes by the way of reviewing present research and stating research gaps for buddy researchers.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.752-759
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• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• Journal of KIBIM
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• v.12 no.2
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• pp.12-25
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• 2022

To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.3
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• pp.61-78
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• 2021

Records on Japanese "Comfort Women" have been individually managed by private sectors or institutions, and some are provided as digital archives on the Internet. However, records of digital archives differ in the composition and representation of metadata by individual institutions. Meanwhile, there is a lack of a consistent structure to describe the relationships between and among these records, leading to their fragmentation and disconnectedness. This paper proposes a knowledge model for interlinking the digital archival resources and builds a knowledge graph by integrating the records from distributed digital archives. It derives common elements by analyzing metadata from the diverse digital archives and expresses them in standard vocabularies to semantically describe multiple entities and relationships of the digital archival resources. In particular, the study includes the refinement of collected data to search and thread dispersed records and the enrichment of external data to provide significant contextual information of records. An evaluation of the knowledge graph is performed via a query measuring the (dis)connectivity between the distributed records. As a result, the knowledge graph is capable of interlinking and retrieving fragmented records, providing substantial contextual information on the records with external data enrichment, and searching accurately to match the user's intentions through semantic-based queries.

• Journal of Ginseng Research
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• v.46 no.1
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• pp.39-53
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• 2022

Ginsenoside Rb₁ (Rb₁), one of the most important ingredients in Panax ginseng Meyer, has been confirmed to have favorable activities, including reducing antioxidative stress, inhibiting inflammation, regulating cell autophagy and apoptosis, affecting sugar and lipid metabolism, and regulating various cytokines. This study reviewed the recent progress on the pharmacological effects and mechanisms of Rb₁ against cardiovascular and nervous system diseases, diabetes, and their complications, especially those related to neurodegenerative diseases, myocardial ischemia, hypoxia injury, and traumatic brain injury. This review retrieved articles from PubMed and Web of Science that were published from 2015 to 2020. The molecular targets or pathways of the effects of Rb₁ on these diseases are referring to HMGB1, GLUT4, 11β-HSD1, ERK, Akt, Notch, NF-κB, MAPK, PPAR-γ, TGF-β1/Smad pathway, PI3K/mTOR pathway, Nrf2/HO-1 pathway, Nrf2/ARE pathway, and MAPK/NF-κB pathway. The potential effects of Rb₁ and its possible mechanisms against diseases were further predicted via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and disease ontology semantic and enrichment (DOSE) analyses with the reported targets. This study provides insights into the therapeutic effects of Rb₁ and its mechanisms against diseases, which is expected to help in promoting the drug development of Rb₁ and its clinical applications.