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

• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.61-70
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• 2023

Due to economic or environmental reasons, green remodeling projects for old buildings are being actively carried out. Meanwhile, in the process of performing the green remodeling, the plan of green remodeling including passive and active elements has been decided based on the subjective experience and knowledge of engineers currently. Therefore, in this study, an ontology-based green remodeling decision-making support model, which can analyze the properties of old buildings and suggest appropriate remodeling plans, was established. In the developed model, once the basic properties of a building are entered, an appropriate remodeling plan composed of passive and active elements can be provided. By utilizing the results developed through the research, it is expected that it will be possible to support decision-making on more objective and appropriate remodeling alternatives development through web-based meta data search in accordance with the accumulation in remodeling cases.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.139-142
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• 2014

시맨틱웹의 구현 도구로써 온톨로지가 있다. 온톨로지는 지식개념의 의미적 연결을 하는데 사용된다. 영어 위키피디아를 토대로 한 영어 DBpedia 온톨로지는 스키마(owl파일 형태)와 인스턴스 모두 잘 구축이 되어있다. 그리고 영어 DBpedia의 각 Class에 한글은 레이블의 형태로 달려있다. 하지만 한글 레이블을 가지고 있지 않은 영어 DBpedia의 Class들이 절반이 넘기 때문에 한글 Class들만으로 된 스키마 구축은 의미가 있다. 한글 Class들로 만들어진 스키마가 있다면 두 한글 온톨로지 사이의 클래스 매칭 알고리즘을 위한 실험이나 한글 온톨로지 자동 증강 알고리즘의 연구 등에 유용하게 쓰일 수 있을 것이다. 본 논문에서 구축한 한글 DBpedia 온톨로지 스키마는 영문 DBpedia 온톨로지의 계층구조와 한글 클래스와 영문 클래스 사이의 매핑정보를 바탕으로 구축되었다. 그리고 기존에 제공되는 한글 DBpedia 온톨로지 클래스의 영어매핑 정보가 있는 한글 프로퍼티와 영어매핑 정보가 없는 한글 프로퍼티를 모두 한글 클래스의 프로퍼티로 입력해주었다.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.345-371
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• 2017

Traditionally, the construction industry field is one of the typical fields using facet analysis method. Internationally the facet analysis method is applied to the classification scheme "Uniclass" or thesaurus "Construction Industry Thesaurus(CIT)". In the case of Korea, the facet analysis method is being used in classification scheme "Construction Industry Classification System" but it is difficult to find studies on facet analysis method in thesaurus or building case study. This study aims to establish facet types by introducing building of thesaurus for information retrieval in construction field using facet analysis method and to suggest building plan of thesaurus in construction field using facet analysis method. In this paper, We establish the 11 top facets(agent/patient, artifacts, abstract, material, parts/component, works, attribute, media, process, space, time) and the 13 subfacet as rudimental facets and suggest building plan according to thesaurus building procedure which is suggested by International Standard(ISO 25964-1). The result of this study is expected to apply to facet based thesaurus. And it is also expected to reuse in Taxonomy or Ontology etc. and to use in interoperability with the classification scheme of Construction filed.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.209-217
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• 2019

In this paper, the purpose of this paper is to lay the foundation for the search system by using and building an online search engine suitable for the search domain and enabling search, conversion, integration and sharing of information. It is to use the ontology to infer hierarchical relationships, deduce objects based on that layer, and extract attributes to search areas that are relevant to the data that the user wants. In order to search for information in this way, the information search system was implemented by entering key words related to 'qualifications'. The implemented system arranged the meaning and relationship of each attribute online so that the general public can search information quickly, easily, and accurately. In addition, the implementation results were compared with two different search engines. Comparable search engines are Naver and Daum, the two major search engines. The search engine of this study, which was built using an ontology suitable for the search domain to perform searches using the semantic web, was evaluated to have excellent results. However, it is thought that a more formalized online location is necessary to increase the accuracy and reliability of search engines and to include more comprehensive categories of search terms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.501-510
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• 2023

The importance of using construction data is increasing in accordance with the recent trend in the smart construction. However, construction project and maintenance information is distributed on the web, and the existing BIM(Building Information Modeling) information exchange and linking method using IFC(Industry Foundation Classes) cannot support connection with BIM data and web resources. This study aims to establish the BIM-based port facility data integration system using linked data(LD) technology in order to integrate BIM and heterogeneous data in the port maintenance domain. To this end, the port BIM-based ifcOWL and port facility maintenance ontology were designed, and LD was built for the BIM and maintenance information of Busan New Port 2-1 Pier3, a BIM pilot project. In addition, service prototypes such as search, statistics and SPARQL(SPARQL Protocol and RDF Query Language) endpoint functions were implemented using the issued LD. The LD-based information utilization system is expected to improve the reusability of information by converting the existing closed information system into an open system and BIM and maintenance data as a web resource in a standard format.

• Journal of KIBIM
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• v.3 no.1
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• pp.21-27
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• 2013

The purpose of the present study is to suggest the strategy for interoperability between BIM and GIS. For this, we analyzed the interoperability issue including the neutral information model such as CityGML, LandXML, IFC and identified the structure differences as the viewpoint of use-case, object, geometry, property by using UML(Unified Modeling Langauge) and reverse engineering. To solve the interoperability problem between BIM and GIS model which is the neutral GIS format, We proposed the consideration including converter, information mapping filter based on ontology dictionary, automation by using API.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.345-348
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• 2006

웹 정보의 의미적 검색을 위한 시맨틱 웹의 연구가 활발히 진행되고 있다. 시맨틱 웹에서 핵심이 되는 것은 온톨로지이다. 2004년 2월 W3C는 온톨로지 구축을 위해 RDF(S)와 OWL을 온톨로지 구축 언어의 표준으로 제정하고 총 13개의 기술문서를 공표하여, 온톨로지 언어의 정의와 구축 사례 및 활용의 내용을 제공하고 있다. 이로 인해, 많은 온톨로지들이 여러 목적에 의해 구축되고 있으며, 그 활용도는 점차 증가하고 있다. 하지만 지리정보의 검색을 위한 온톨로지는 구축이 어려우며 이에 대한 연구도 미흡하다. 이에 본 논문에서는 지역과 지역의 위치 정보, 지역 내의 건물 및 도로에 대한 위치정보를 온톨로지로 구축할 수 있는 방법과 이를 지리정보 검색에 활용할 수 있는 방안을 연구하였다. 그 결과, 우리는 위치에 대한 온톨로지 구축을 위한 3가지 어휘를 설계하였고, 이를 이용하여 효율적이고 의미적인 지리정보 검색이 가능하다는 것을 확인할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.487-489
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• 2003

시맨틱 웹에서 가장 중요한 부분인 웹 온톨로지 구축을 위한 많은 연구는 많은 발전과 표준화를 거쳐서 현재 웹 온톨로지 구축 언어인 OWL을 가장 널리 이용하여 웹 온톨로지를 구축하고 있다. 온톨로지의 구축에서는 각 개념간의 관계의 정의가 매우 중요하며, 이를 표현하고 정의하는 많은 일련의 과정이 진행되고 있다. 그러나 온톨로지 구축 시에 중요하게 여기어지고 있는 개념간의 관계표현은 아직도 많은 부분 미흡하다. 특히 시간관계 표현에 관한 내용은 일반적으로 중요하게 생각되어지고 있지만, 그 표현 방법이 제시되지 않고 있다. 이에 본 논문에서는 온톨로지 구축 시 생겨나는 많은 관계들 중, 특히 시간적 관계를 표현하고, 적용하여 그 실용성을 제시하고자 한다.