• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.101-114
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• 2023

This research aimed to recognize the determinant characteristics affecting consumers' clothing fit evaluation by employing text mining and social network analysis. For this aim, we first extracted text data linked to clothing fit from 2,000 consumer reviews collected from social network services and conducted semantic network examination and CONCOR analysis. As a result, we reported that "pants" and "skirts" were the most commonly associated clothing items with consumers' clothing fit evaluation. And the length of clothing was most commonly investigated. Then, the "waist" and "hip" were the most critical body parts affecting consumers' perception of clothing fit. Further, the four keywords including "wide," "large," "short," and "long" were the most employed ones in consumer reviews when evaluating clothing fit. This study is meaningful in that it specifically recognized the structural relationship and semantic meanings of keywords relevant to consumers' evaluation of clothing fit, which could bring empirical reference information for advanced clothing fit.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.152-152
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• 2017

• Journal of the Korean Geographical Society
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• v.42 no.5
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• pp.788-807
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• 2007

To better publish geographical information on the Web, it is important to capture how Web technologies are changing. For a recent decade, Semantic Web has been developed by incorporating ontologies into the current Web, with an aim to make computers understand rather than simply display. Ontology, an explicit specification of a conceptualization, and the Semantic Web grounded on the ontology, have the potential for effective sharing and appropriate retrieval of geographical information. This paper describes a Semantic Web Service over the mobile Web that can offer pertinent tourism information according to user contexts. To do this, a tourism ontology was formalized in the PARA(Place-Attraction-Resource-Activity) ontology model by organizing tourist places, tourist attractions, tourism resources, and activities. Locational relationships between tourist places were also included in the PARA ontology model to take into account the movements of tourists on a railway network. The XML(Extensible Markup Language) Web Service in the middle tier manages the client-side request for information retrieval and the corresponding server-side response from the data provider. The PARA ontology was integrated into the XML Web Service for the concept-based discovery of tourism information. The applicability of the proposed system was tested through a simulation experiment for Tokyo tourism.

• Journal of Information Technology Services
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• v.16 no.2
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• pp.111-127
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• 2017

Computing Performance, sensor, storage, memory, and network costs have been steadily declining, and IoT services have recently become more active. The Internet of Things is linked with Big Data to create new business, and public institutions and corporations are hurry to import Internet of things. As the importance of the Internet of things has increased, the number of devices supporting the IoT has rapidly increased. With the development of the Internet of Things, various types of Internet services are being developed. For this reason, there is an increasing demand for IoT service designers and developers for IoT service case automatic search technology. IoT service designers can avoid duplication with existing services through service case retrieval and developers can save cost and time by combining existing reusable service equipment. This paper proposes IoT-Lite ontology for IoT and Semantic Web service to solve the above-mentioned problems. The existing ontologies for IoT, despite its many advantages, are not widely used by developers because it has not overcome the relatively slow drawbacks of increasing complexity and searching for development. To complement this, this study uses the IoT-Lite ontology introduced by W3C as a model and a semantic web service for automatic system retrieval. 3D camera, GPS, and 9-axis sensor, and IoT-Lite designed by IoT-Lite technique are integrated with the semantic technique and implemented directly.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.663-667
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• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.123-133
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• 2005

In recent years, service discovery is one of the major technologies of home networks which head for a pervasive computing environment. However, existing service discovery techniques are difficult to understand semantics, and they only provide syntactic level service matching. To solve these problems, we have designed and developed ontology for semantic service discovery. Our ontology could enrich the amount of devices and services representations with semantics, and the relation of devices and service could be efficiently described through primitive service. For representing context information of devices, we describe attributes of device including location information, device status and etc. To determine whether the developed ontology can be applied to service discovery systems, we have implemented a semantic service discovery system by extension of the existing Jini lookup service. Also, we have evaluated our ontology with associated software environment according to some experiment scenarios, and have proved the usefulness of our ontology-based semantic service discovery system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.87-88
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• 2008

As an information service system based on semantic web technology, $OntoFrame^{(R)}$ takes aim at a framework for providing analysis and fusion services of academic information. It currently consists of three parts: ontologies representing knowledge schema derived from academic information, $OntoURI^{(R)}$ which makes academic information into knowledge, and $OntoReasoner^{(R)}$ which performs inference and search on the knowledge. Unlike existing search engines which provides simple search services, our system provides, based on semantic web technology, several semantic and analytic services such as year-based topic trends in academic information, related topics, topic-based researchers and institutes, researcher network, statistics and regional distribution of academic information.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.725-730
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• 2006

Recently Web services have become a key technology which is indispensable for e-business. Due to its ability to provide the desired information or service regardless of time and place, integrating current application systems within a single business or between multiple businesses with standardized technologies are realized using the open network and Internet. However, the current Web Services Retrieval Systems, based on text oriented search are incapable of providing reliable search results by perceiving the similarity or interrelation between the various terms. Currently there are no web services retrieval models containing such semantic web functions. This research work is purported for solving such problems by designing and implementing an extended Semantic Web Services Retrieval Model that is capable of searching for general web documents, UDDI and semantic web documents. Execution result is proposed in this paper and its efficiency and accuracy are verified through it.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.