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

• Journal of Information Technology Services
• /
• v.13 no.2
• /
• pp.133-149
• /
• 2014

It is unavoidable to provide products that meet customers' needs and wants so that firms may survive under the competition in this globalized market. This paper focuses on how to provide levels for attributes that compse product so that firms may give the best products to customers. In particular, its main issue is how to determine common attributes and the others with their appropriate levels to maximize firms' profits, and how to construct a decision support system to ease decision makers' decisons about optimal common attribute selection using the Semantic Web and SWCL technologies. Parameter data in problems and the relationships in the data are expressed in an ontology data model and a set of constraints by using the Semantic Web and SWCL technologies. They generate a quantitative decision making model through the automatic process in the proposed system, which is fed into the solver using the Logic-based Benders Decomposition method to obtain an optimal solution. The system finally provides the generated solution to the decision makers. This presentation suggests the opportunity of the integration of the proposed system with the broader structured data network and other decision making tools because of the easy data shareness, the standardized data structure and the ease of machine processing in the Semantic Web technology.

• Annual Conference on Human and Language Technology
• /
• 2019.10a
• /
• pp.185-189
• /
• 2019

관계추출기의 학습을 위해서는 많은 학습 데이터가 필요한데, 사람이 모으게 되면 많은 비용이 필요하여 원격 지도 학습을 이용한 데이터 수집이 많은 연구에서 사용되고 있다. 원격 지도 학습은 지식베이스를 기반으로 학습 데이터를 자동으로 만들어 내는 방식이기에 비용이 거의 들지 않지만, 지식베이스의 질과 양에 영향을 받는다. 본 연구는 원격 지도 학습을 기본으로 관계추출기의 성능을 향상 시키고, 지식베이스를 확장하는 방안으로 반복학습을 제안한다. 실험을 적은 비용으로 빠르게 진행하기 위해 반복학습을 자동화 하는 시스템을 설계하여 실험을 하였고, 이 시스템으로 관계추출기의 성능이 향상 될 수 있는 가능성을 보였으며, 반복학습을 통한 지식베이스의 확장 방안을 제시한다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.1-9
• /
• 2005

Today, according to tremendous development of B2B e-commerce, B2B e-marketplaces which accomplish various types of transactions through a number of buyers and sellers on online are embossed importantly. However, buyers are unable to search correct products because of inconsistency of product information between buyers and sellers. This paper solved this problem as semantic web technology. Semantic Web is an extension of current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The Semantic Web aims at machine-processable information. Its underlying technologies are RDF, RDF Schema, and ontology as the shared formal conceptualization of particular domains. In this paper, we present an implementation of Semantic Web enabled search system for B2B E-Marketplace domains. The system exploits OWL as the standard ontology language proposed by W3C and the Jena which is a Semantic Web toolkit, namely a Java framework writing Semantic Web applications. Finally, we summarize our experiences and discuss future research topics.

• Journal of Digital Contents Society
• /
• v.6 no.4
• /
• pp.201-208
• /
• 2005

In high-technology digital age that change everyday fast, Web became most familiar and essential tool to public. Since creation of web, web began from the first step web that offer simple information and contents. and now, it have developed to step offer dynamic contents that various multimedia components are combined. In future, it will develop to the next generation, intelligence style Semantic Web that Meta Communication of meaning-base is possible. In this paper, we examine about concept and characteristic of Semantic web that is a point technology in next generation. Also, we studied about example and practical use in personalized contents side that is based on Semantic Meta-Communication system. Semantic web is value industry field that has infinite possibility and has necessary of a lot of studies and development yet. Therefore in digital age, we expect this paper will help for space construction of more mature communication.

• Korean Journal of Cognitive Science
• /
• v.22 no.3
• /
• pp.271-298
• /
• 2011

The lexical semantic system should be built to grasp lexical semantic information more clearly. In this paper, we studied a semantic clustering of predicates that is one of the steps in building the lexical semantic system. Unlike previous studies that used argument of subcategorization(subject and object), selectional restrictions and interaction information of adverb, we used sense tagged definition in dictionary for the semantic clustering of predicate, and also attempted hierarchical clustering of predicate using the relationship between the generic concept and the specific concept. Most of the predicates in the dictionary were used for clustering. Total of 106,501 predicates(85,754 verbs, 20,747 adjectives) were used for the test. We got results of clustering which is 2,748 clusters of predicate and 130 recursive definition clusters and 261 sub-clusters. The maximum depth of cluster was 16 depth. We compared results of clustering with the Sejong semantic classes for evaluation. The results showed 70.14% of the cohesion.

• The Journal of Society for e-Business Studies
• /
• v.21 no.4
• /
• pp.1-14
• /
• 2016

Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users' search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order 'document-by-concept' tensor (i.e., matrix), and each concept as the 2nd-order 'document-by-term' tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method.

• Journal of Internet Computing and Services
• /
• v.11 no.4
• /
• pp.107-117
• /
• 2010

The feature models representing the common and variable concepts among the software products and the feature configurations generated by selecting the features to be included in the target product are the essential components in the software product lines methodology. Although the researches on the formal semantics and reasoning of the feature models and feature configurations are in progress, the researches on feature model ontologies and feature configuration validation using the semantic web technologies are yet insufficient. This paper defines the formal semantics of the feature models and proposes a feature configuration validation technique based on ontology and semantic web technologies. OWL(Web Ontology Language), a semantic web standard language, is used to represent the knowledge in the feature models and the feature configurations. SWRL(Semantic Web Rule Language), a semantic web rule languages, is used to define the rules to validate the feature configurations. The approach in this paper provides the formal semantic of the feature models, automates the validation of feature configurations, and enables the application of various semantic web technologies, such as SQWRL.

• Journal of Information Technology Services
• /
• v.10 no.3
• /
• pp.189-201
• /
• 2011

Feature models are widely used to model commonalities and variabilities among products during software product line development. Feature configurations are generated by selecting the features to be included in individual products. Automated tools to identify errors or inconsistencies in the feature models and configurations are essential to successful software product line engineering. This paper proposes a verification technique and tool based on semantic web technologies such as OWL, SWRL and Protege API. This approach checks the feature model and configuration based on predefined rules and provides information on existence of errors as well as the kinds of those errors. This approach is extensible due to ease of rule modification and may be easily applied to other environments because semantic web technologies can be easily integrated with other programming environments. This paper demonstrates how various semantic web-related technologies can support automatic verification of one kind of software development artifact, the feature model.

• MALSORI
• /
• no.57
• /
• pp.59-72
• /
• 2006

The purpose of this study was to see whether Korean-English bilinguals' semantic systems of Korean and English are shared or separated between the two languages. In a series of picture-word interference tasks, participants were required to name the pictures in Korean or in English with distractor words printed either in Korean or English. The distractor words were any of identical, semantically related, or neutral to the picture. The response time of naming was facilitated when distractor words were semantically identical for both same- and different-language pairs. But this facilitation effect was stronger when naming was produced in their native language, which in this case was Korean. Also, inhibitory effect was found when the picture and its distractor word were semantically related in both same- and different-language paired conditions. From these results it can be concluded that semantic representations of Korean and English may not be entirely but partly overlapping in bilinguals.