• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.96-97
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• 2021

In the 4th Industrial Revolution and changes in the construction industry market, the importance of information management is becoming more emphasized. For efficient use of information, construction data must be collected, processed and analyzed more quickly and accurately, depending on the purpose of the project. Thus, data could be utilized as integrated knowledge. However, there is a limit to searching or managing construction information produced in various formats over the entire life cycle by index words. Therefore, this study proposes a construction information integrated management plan that reflects the characteristics of the construction industry by applying the OWL concept. For this, as an initial study, an architectural object of small size and clear features was needed. Therefore, a construction information management study using OWL was conducted for Korean traditional architectural style houses (Hanok) which has these characteristics.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.986-998
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• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.295-304
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• 2011

To effectively manage research information in the field of national defense, metadata about the information should be managed systematically, and an integrated system to help convergence and management of the information should be implemented based on the metadata. In addition, the system should provide the users with effective integrated search services in a mobile environment, because searching via the use of mobile devices is increasing. The objective of this paper is to suggest a MSISS (Mobile Semantic Integrated Search System), which satisfies the requirements for effective management of the national defense research information. Specifically, we defined national defense research ontologies and national defense research rules after analyzing the Dublin Core metadata and database information of the major military research institutions. We implemented a prototype system for MSISS to demonstrate the use of the ontologies and rules for semantic integrated searching of the military research information. We also presented a triple-based search service to support semantic integrated search in a mobile environment and suggested future mobile semantic integrated search services.

• Journal of Korean Library and Information Science Society
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• v.36 no.4
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• pp.77-98
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• 2005

The purpose of this research is to propose a powerful alternative in designing knowledge portals using Topic Maps(TM). To demonstrate the power of TM In constructing knowledge portals. we designed a TM-based korean folk music(pansori) site, tested It with an existing pansoree site (pansoree.com ) employing diverse query patterns : simple, advanced, associative, and cross referential Queries. The results show that the TM-based site outperforms the pansoree.com in searching time and steps. The TM-based site also provide novice users who do not know pansori domain with easy access to Information that they need.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.119-133
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• 2011

With the advent of Web 2.0, the use of RESTful web services is expected to overtake that of the traditional SOAP-based web services. Recently, the growing number of RESTful web services available on the web raises the challenging issue of how to locate the desired web services. However, the existing keyword searching method is insufficient for the bad recall and the bad precision. In this paper, we propose a novel building semantic ontology method which employs both the clustering technique based on association rules and the semantic analysis technique based on patterns. From this method, we can generate ontologies automatically, reduce the burden of semantic annotations, and support more efficient web services search. We ran our experiments on the subset of 168 RESTful web services downloaded from the PregrammableWeb site. The experimental results show that our method achieves up to 35% improvement for recall performance, and up to 18% for precision performance compared to the existing keyword searching method.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.93-105
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• 2013

Much Interest has focused on social network services such as Facebook and Twitter. Previous research conducted on social network often emphasized the architecture of the social network that is the existence of path between any objects on network and the centrality of the object in the network. However, studies on the semantic association in the network are rare. Studies on searching semantic associations between entities are necessary for future business enhancements. In this research, the ontology based social network analysis is performed. A new method to search and rank relation sequences that consist of several relations between entities is proposed. In addition, several heuristics to measure the strength of the relation sequences are proposed. To evaluate the proposed method, an experiment was performed. A group of social relationships among the university and organizations are constructed. Some social connections are searched using the proposed ranking method. The proposed method is expected to be used to search the association among entities in ontology based knowledge base.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.23-26
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• 2006

최근 대부분의 이러닝(E-Learning)을 교육하는 사이트는 학습 콘텐츠를 검색하는 방법이 단순한 리스트의 나열과 택스트 매칭(Text matching)방법을 사용하는 단점이 있다. 이를 보완하기 위해 좀 더 컴퓨터가 정보 데이터의 의미를 분석하여 검색이 가능하도록 개념 네트워크인 시맨틱웹(Semantic Web)이 등장하였다. 본 논문에서는 이러한 시맨틱웹의 온톨로지(Ontology) 언어 중에 토픽맵(Topic Maps)을 사용하여 많은 양의 학습 정보 데이터를 쉽고도 정확하게 연결 지어 학습 콘텐츠에 대한 정보를 표현하고, 구조화할 수 있는 방법을 모색해 보고자 한다. 학습자의 관심분야 정보, 학습객체의 학습 권장자의 정보와 함께 학습 경험과 검색 빈도수를 분석한 협력 필터링과 학습 에이전트의 개인화 기법을 동시에 사용하여 선호도를 분석한다. 이 선호도를 가지고 학습자의 메타데이터를 생성하고, 로그 데이터를 따로 데이터베이스에 저장한다. 이러한 학습자의 정보와 학습 콘텐츠간의 정보를 상호 연결하여, 그 토픽맵을 사용하여 연관관계를 정의해 줌으로써 학업성취도를 높이고, 학습자 개개인의 성향에 가장 알맞은 학습 콘텐츠를 탐색해가는 네비게이터(Navigator)를 설계하였다.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.732-748
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• 2009

The technology of Semantic Web realizes the base technology for context-awareness that creates new services by dynamically and flexibly combining various resources (people, concepts, etc). According to the realization of ubiquitous computing technology, many researchers are currently working for the embodiment of web service. However, most studies of them bring about the only predefined results those are limited to the initial description by service designer. In this paper, we propose a new service supporting model to provide an automatic method for plan related tasks which achieve goal state from initial state. The inputs on an planner are intial and goal descriptions which are mapped to the current situation and to the user request respectively. The idea of the method is to infer context from world model by DL-based ontology reasoning using OWL domain ontology. The context guide services to be loaded into planner. Then, the planner searches and plans at least one service to satisfy the goal state from initial state. This is STRIPS-style backward planner, and combine OWL-S services based on AI planning theory that enabling reduced search scope of huge web-service space. Also, when feasible service do not find using pattern matching, we give user alternative services through DL-based semantic searching. The experimental result demonstrates a new possibility for realizing dynamic service modeler, compared to OWLS-XPlan, which has been known as an effective application for service composition.