• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.39-47
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• 2024

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman's rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.

• Journal of KIISE
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• v.43 no.8
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• pp.927-932
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• 2016

With the explosive increase in the number of online medical documents, the demand for question-answering systems is increasing. Recently, question-answering models based on machine learning have shown high performances in various domains. However, many question-answering models within the medical domain are still based on information retrieval techniques because of sparseness of training data. Based on various information retrieval techniques, we propose an answer snippet retrieval model for question-answering systems of medical documents. The proposed model first searches candidate answer sentences from medical documents using a cluster-based retrieval technique. Then, it generates reliable answer snippets using a re-ranking model of the candidate answer sentences based on various sentence retrieval techniques. In the experiments with BioASQ 4b, the proposed model showed better performances (MAP of 0.0604) than the previous models.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.471-474
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• 2010

질의와 질의 주변에 나오는 어휘는 의미적으로 연관되어있다는 가정하에 질의뿐만 아니라 질의 주변에 나오는 문맥 어휘들도 가중치를 높여준다면 검색에 효율을 높일 수 있을 것이다. 본 논문에서는 질의와 질의 주변에 나오는 문맥 어휘들에게 가중치를 주어 질의 어휘의 위치 가중치를 반영한 문서를 표현하고, 위치 가중치가 반영된 문서 벡터들 사이의 유사도를 계산하여 클러스터 기반 재순위화를 하여 성능을 향상시키는 방법을 제안한다. 뉴스 집합인 TREC AP 문서를 이용하여 언어모델, 위치 가중치를 이용한 언어모델, 클러스터 기반 재순위화 모델의 비교실험을 통해 유효성을 검증한다.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.357-365
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• 2010

Personalized web search engine provides personalized results to users by query expansion, re-ranking or other methods representing user's intention. The personalized result page includes URL, page title and small text fragment of each web document. which is known as snippet. The snippet is the summary of the document which includes the keywords issued by either user or search engine itself. Users can verify the relevancy of the whole document using only the snippet, easily. The document summary (snippet) is an important information which makes users determine whether or not to click the link to the whole document. Hence, if a search engine generates personalized document summaries, it can provide a more satisfactory search results to users. In this paper, we propose a personalized document summary system for personalized web search engines. The proposed system provides increased degree of satisfaction to users with marginal overhead.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.399-404
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• 2010

In this paper, we propose a method for precision improvement based on core clusters and term proximity. The method is composed by three steps. The initial retrieval documents are clustered based on query term combination, which occurred in the document. Core clusters are selected by using proximity between query terms. Then, the documents in core clusters are reranked based on context information of query. On TREC AP test collection, experimental results in precision at the top documents(P@100) show that the proposed method improved 11.2% over the language model.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.623-628
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• 2007

본 논문은 Web 2.0시대의 새로운 컨텐츠 매체로 각광받고 있는 블로그와 관련하여 태그 기반의 검색 알고리즘을 제안하고자 한다. 최근 블로그 검색과 관련하여 태그 기반의 블로그 검색 서비스가 등장하기 시작했지만, 현재 제공되는 태그 기반의 검색 서비스는 태그의 유무와 컨텐트의 최신성을 주요 기준으로 삼고, 태그와 컨텐트 간의 관련성을 제대로 고려하지 않아 검색 결과가 만존스럽지 못하는 경우가 많다. 따라서 본 논문에서는 태그와 컨텐트와의 관련성을 실수화하고 이를 주요 기준으로 검색 결과의 순위를 결정하는 PTRank 알고리즘을 제안하였다. PTRank 알고리즘에서는 1) 태그가 피드의 제목에 포함되었는지 여부, 2) 태그가 피드의 설명에 나타나는 회수, 3) 태그가 아이템의 제목에 포함되었는지 여부, 4) 태그가 아이템의 설명에 나타나는 횟수, 5) 피드 내에서 태그의 IDF값, 6) 사용자의 검색 행위를 이용해 태그와 컨텐트간의 관련성을 실수화하였다. 실험 결과, PTRank 모델 및 학습 알고리즘이 태그 기반의 피드 검색에서 잘 작동하며 검색에 효과적으로 활용될 수 있다는 것을 알 수 있었다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.5-24
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• 2013

OPAC is a very important interface for user to use and search library collection and electronic resources. The purpose of this study is to analyze empirically the impact of OPAC usability on user satisfaction and loyalty in an university library setting. The OPAC functions investigated are keyword searching, integrated searching, faceted navigation, relevance ranking, spelling check, recommendations, user participation, and Really Simple Syndication(RSS), etc. This study investigated what usability effects on user satisfaction and loyalty and suggested the way to improve user satisfaction and loyalty.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.4
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• pp.5-20
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• 2016

Discovery service has as its object to cope with the user to take advantage of the collection of the library as possible to index and search, one step further, the interface by more efficiently to the user's information needs. Discovery service has features such as providing a ranking and navigation services to subdivide the search results by facet results along the suitability and visually rich display, suggestions, recommendations associated resources. In this study introduces the status of discovery services such as discovery service products, usage status, and features, and compares and analyzes the use agencies, content status, main functions, and features of the three discovery services used in Korea library.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.163-180
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• 2009

In this paper, we describe a developmental system for establishing personal information tendency based on user queries. For each query, the system classified it based on the category information using a kNN classifier. As category information, we used DDC field which is already assigned to each record in the database. The system accumulates category information for all user queries and the user's personalized feature for the target database. We then developed a personalized retrieval system reflecting the personalized feature to produce search result. Our system re-ranks the result documents by adding more weights to the documents for which categories match with the user's personalized feature. By using user's tendency information, the ambiguity problem of the word could be solved. In this paper, we conducted experiments for personalized search and word sense disambiguation (WSD) on a collection of Korean journal articles of science and technology arena. Our experimental result and user's evaluation show that the performance of the personalized search system and WSD is proved to be useful for actual field services.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.25-34
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• 2009

As the trend of the Web is changing toward 'Web 2.0', OpenAPIs, Web 2.0's core technology, are used in many web sites. In the past, services in websites are used in its own, but recently it is possible to use services in other websites by using OpenAPI. In u-GIS many vendors also can provide combined service by using OpenAPI. There are already lots of OpenAPIs and the numer of OpenAPI increases very fast. So it is difficult to find a service that we want to use, and also difficult to find services for mashup. In this paper, we developed retrieval methods for OpenAPIs and mashup capable services based on similarity. First we define the integrated service information model to cover various protocols of OpenAPI, then developed a retrieval methods based on it. By implementing system according these methods by using relational database and JSP, we prove that the system can provide an ranked result sets based on similarity, OpenAPI's integration retrieval results and mashup capable service retrieval results.