• Asia pacific journal of information systems
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• 제17권4호
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• 환경영향평가
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• 제6권2호
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• pp.113-121
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• 1997

LHR(Landfill Hazard Ranking Model) was developed for assessing the relative hazard of landfills by using the method of value-structured approach. LHR consists of combining a multiattribute decision-making method with a qualitative risk assessment approach. A pairwise comparision method was applied to determine weights of landfill factors related. To prove the validity of weights allocation of landfill hazard evaluation factors, sensitivity analysis was applied. Firstly, the impact on landfill hazard score according to variations of weights of landfill hazard factors was analyzed. Secondly, the impact on landfill hazard score according to conditions change of landfill hazard factors was analyzed. As a result of sensitivity analysis, LHR composite scores are largely influenced by some factors following sequential order such as waste volume, proximity to sensitive environments, containment facilities, distance from drinking water supplies, and waste toxicity. The relative order of landfill hazard evaluated by LHR is not influenced by the weights change of individual factors. Therefore, LHR seems to be a credible model to determine priorities of landfill remediation based on the vulnerability of water resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Smart Structures and Systems
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• 제21권1호
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• pp.123-137
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• 2018

Structural monitoring is the most important part of the construction and operation of the embankment dams. Appropriate instruments selection for dams is vital, as inappropriate selection causes irreparable loss in critical condition. Due to the lack of a systematic approach to determine adequate instruments, a framework based on three comparable Multi-Attribute Decision Making (MADM) methods, which are VIKOR, technique of order preference by similarity to ideal solution (TOPSIS) and Preference ranking organization method for enrichment evaluation (PROMETHEE), has been developed. MADM techniques have been widely used for optimizing priorities and determination of the most suitable alternatives. However, the results of the different methods of MADM have indicated inconsistency in ranking alternatives due to closeness of judgements from decision makers. In this study, 9 criteria and 42 geotechnical instruments have been applied. A new method has been developed to determine the decision makers' importance weights and an aggregation method has been introduced to optimally select the most suitable instruments. Consequently, the outcomes of the aggregation ranking correlate about 94% with TOPSIS and VIKOR, and 83% with PROMETHEE methods' results providing remarkably appropriate prioritisation of instruments for embankment dams.

• 한국전자거래학회지
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• 제17권2호
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• pp.73-85
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• 2012

최근 들어 트위터나 페이스북과 같은 SNS가 대중화되면서 이에 관련한 연구도 활발히 진행되고 있다. 하지만 SNS가 비교적 최근에 시작된 만큼 관련 연구도 아직 초보적인 수준이다. 특히 포털 사이트와 같은 검색 엔진에서는 트위터에 대한 검색 결과를 최근에 등록된 순으로 보여주는 수준에 머물러 있다. 트위터에서의 검색은 기존의 TF-IDF로 대표되는 웹 검색 방식과는 달라야한다. 본 논문에서는 트위터 환경에서 사용자가 원하는 게시글을 효율적으로 검색하는 방법을 제안한다. 제안된 방법에서는 사용자들의 재전송 빈도를 검색결과의 주요한 평가요소로 활용한다. 재전송 정보는 사용자가 직접 게시글의 가치를 판단하는 중요한 평가 척도가 될 수 있다. 또한 실험을 통하여 제안된 방법이 트위터 검색에 효율적으로 적용될 수 있음을 보여준다.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.724-729
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• 2011

정보 검색은 다수 자료에서 사용자가 원하는 부분을 찾는 과정을 의미한다. 일반적으로 대규모 자료 집합의 관리를 위해서는 데이터베이스가 사용되는데 인터넷과 같은 복잡한 문서구조들이 공존하는 환경에서는 한 번에 사용자가 원하는 문서를 정확히 찾아내는 것이 어렵기 때문에, 문서에 순위를 부여하여 사용자에게 제시하는 방법이 일반적으로 많이 사용된다. 본 논문에서는 자료에 포함되어 있는 단어들을 단순히 검색하는 것 뿐만 아니라 단어들 간의 순서 및 인접성을 고려한 검색방법을 용어빈도-역문헌빈도 및 n-gram 기법을 응용하여 구현하였다. 그 결과 19,000개 이상의 다수 문서 집합에서 73%의 정확율로 보다 정확한 검색이 가능하게 되었다.

• 한국전자거래학회지
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• 제14권2호
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• pp.95-116
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• 2009

지식 재사용 측면에서 기존의 온톨로지를 재사용할 수 있다면 많은 자원을 절약할 수 있을 것이다. 그러나 기존의 온톨로지를 활용하기 위해서는 보다 발전된 온톨로지 검색 기능이 요구된다. 현재까지 이루어진 관련 연구들에서는 주로 렉시컬 매칭기법을 사용하여 온톨로지를 검색하였다. 그러나 의미적 측면에서 문제점이 있으므로 본 연구에서는 관계의 의미적 유사성에 기반한 온톨로지 선택 랭킹 모델을 제안한다. 본 연구는 개념간 계층 구조와 관계를 온톨로지 검색에 이용함으로써 온톨로지의 선택 랭킹을 효과적이며 실질적으로 개선하였다. 또한 실험을 통해 연구 모델의 결과와 선행 연구의 결과, 온톨로지 전문가의 랭킹 결과를 비교 분석하고 연구 모델의 타당성을 검증하였다. 본 연구 결과는 온톨로지 검색 연구를 이론적으로 발전시켰을 뿐 아니라 실무적인 측면에서 실무자들이 온톨로지를 쉽게 찾아 재사용할 수 있도록 한다.

• 응용통계연구
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• 제32권5호
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• pp.763-782
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• 2019

텍스트 문서 집합에 대한 정보검색에서는 주어진 질의에 부합하는 각 문서의 적합도 확률을 계산하고 이 확률이 높은 것부터 낮은 순으로 문서 순위를 정하여 사용자에게 제공한다, 각 문서의 적합도 확률 계산에 많이 사용되는 모형은 단어들이 확률적으로 독립이라는 가정 하에 확률을 추정한다. 이 모형은 단어들의 결합 확률을 계산하는 것이 현실적으로 어렵다는 점에서 많이 이용되고 있지만 질의에 사용되는 단어들이 대개 서로 관련성을 가지고 있다는 사실을 고려하고 있지 않다. 본 논문에서는 단어 자질들의 의존 구조를 고려하여 문서의 적합도 확률을 계산하기 위하여 단어들의 결합 패턴의 확률을 다항분포 모형으로 가정하고, 최대 엔트로피 방법으로 확률을 추정하여 문서 순위를 매기는 정보검색 모형을 제안한다. 여러 가지 다항분포 상황에서 시뮬레이션 실험을 한 결과 변수들의 독립을 가정한 모형보다 더 우수한 추정 결과를 보여 준다. 실제 LETOR OHSUMED 데이터 이용한 문서 순위 매기기 실험의 결과도 더 나은 검색 결과를 보여 준다.

• Nuclear Engineering and Technology
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• 제49권1호
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• pp.148-164
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• 2017

The focus on the issues surrounding spent nuclear fuel and lifetime extension of old nuclear power plants continues to grow nowadays. A transparent decision-making process to identify the best suitable nuclear fuel cycle (NFC) is considered to be the key task in the current situation. Through this study, an attempt is made to develop an equilibrium model for the NFC to calculate the material flows based on 1 TWh of electricity production, and to perform integrated multicriteria decision-making method analyses via the analytic hierarchy process technique for order of preference by similarity to ideal solution, preference ranking organization method for enrichment evaluation, and multiattribute utility theory methods. This comparative study is aimed at screening and ranking the three selected NFC options against five aspects: sustainability, environmental friendliness, economics, proliferation resistance, and technical feasibility. The selected fuel cycle options include pressurized water reactor (PWR) once-through cycle, PWR mixed oxide cycle, or pyroprocessing sodium-cooled fast reactor cycle. A sensitivity analysis was performed to prove the robustness of the results and explore the influence of criteria on the obtained ranking. As a result of the comparative analysis, the pyroprocessing sodium-cooled fast reactor cycle is determined to be the most competitive option among the NFC scenarios.

• 상하수도학회지
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• 제35권6호
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• pp.517-531
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• 2021

The water supply facilities of Korea have achieved a rapid growth, along with the other social infrastructures consisting a city, due to the phenomenon of urbanization according to economic development. Meanwhile, the level of water supply service demanded by consumer is also steadily getting higher in keeping with economic growth. However, as an adverse effect of rapid growth, the quantity of aged water supply pipes are increasing rapidly, Bursts caused by pipe aging brought about an enormous economic loss of about 6,161 billion won as of 2019. These problems are not only worsening water supply management, also increasing the regional gap in water supply services. The purpose of this study is to classify hazard evaluation indicators and to rank the water distribution network hazard by cluster using the TOPSIS method. In conclusion, in this study, the entropy-based multi-criteria decision-making methods was applied to rank the hazard management of the water distribution network, and the hazard management ranking for each cluster according to the water supply conditions of the county-level municipalities was determined according to the evaluation indicators of water outage, water leakage, and pipe aging. As such, the hazard ranking method proposed in this study can consider various factors that can impede the tap water supply service in the water distribution network from a macroscopic point of view, and it can be reflected in evaluating the degree of hazard management of the water distribution network from a preventive point of view. Also, it can be utilized in the implementation of the maintenance plan and water distribution network management project considering the equity of water supply service and the stability of service supply.