• International Journal of Knowledge Content Development & Technology
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• v.5 no.1
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• pp.49-68
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• 2015

Given a query (a health question), retrieval of relevant frequently asked questions (FAQs) is essential as the FAQs provide both reliable and readable information to healthcare consumers. The retrieval requires the estimation of the semantic similarity between the query and each FAQ. The similarity estimation is challenging as semantic structures of Chinese healthcare FAQs are quite different from those of the FAQs in other domains. In this paper, we propose a conceptual model for Chinese healthcare FAQs, and based on the conceptual model, present a technique ECA that estimates conceptual similarities between FAQs. Empirical evaluation shows that ECA can help various kinds of retrievers to rank relevant FAQs significantly higher. We also make ECA online to provide services for FAQ retrievers.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.83-91
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• 2016

Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2031-2041
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• 1999

In this paper, we present the HVIS (Hybrid Video Information System) which bolsters up meaning retrieval of all the various users by integrating feature-based retrieval and annotation-based retrieval of unformatted formed and massive video data. HVIS divides a set of video into video document, sequence, scene and object to model the metadata and suggests the Two layered Hybrid Object-oriented Metadata Model(THOMM) which is composed of raw-data layer for physical video stream, metadata layer to support annotation-based retrieval, content-based retrieval, and similarity retrieval. Grounded on this model, we presents the video query language which make the annotation-based query, content-based query and similar query possible and Video Query Processor to process the query and query processing algorithm. Specially, We present the similarity expression to appear degree of similarity which considers interesting of user. The proposed system is implemented with Visual C++, ActiveX and ORACLE.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3762-3774
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• 2000

The advances of computer hardware and information processing technologies raise the needs of multimedia information retrieval systems. Up to date. multimedia information systems have been developed for text information and image information. Nowadays. the multimedia information systems for video and audio information. especially for musical information have been grown up more and more. In recent music information retrieval systems. not only the information retrieval based on meta-information such like composer and title but also the content-based information retrieval is supported. The content-based information retrieval in music information retrieval systems utilize the similarity value between the user query and the music information stored in music database. In tbis paper. hence. we developed a similarity computation algorithm in which the pitches and lengths of each corresponding pair of notes are used as the fundamental factors for similarity computation between musical information. We also make an experiment of the proposed algorithm to validate its appropriateness. From the experimental results. the proposed similarity computation algorithm is shown to be able to correctly check whether two music files are analogous to each other or not based on melodies.

• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.498-506
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• 2014

With the development of smart transportation, people are likely to find their paths by using navigation and map application. However, the existing retrieval system cannot output the correct retrieval result due to the inaccurate query. In order to remedy this problem, set-based POI search algorithm was proposed. Subsequently, additionally a method for measuring POI name similarity and POI search algorithm supporting classifying duplicate characters were proposed. These algorithms tried to compensate the insufficient part of the compensate set-based POI search algorithm. In this paper, accuracy improvement methods for measuring string similarity in POI data retrieval system are proposed. By formulization, similarity measurement scheme is systematized and generalized with the development of transportation. As a result, it improves the accuracy of the retrieval result. From the experimental results, we can observe that our accuracy improvement methods show better performance than the previous algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.663-667
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• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.39-55
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• 2013

It is a difficult problem to use keyword retrieval for low-quality Korean document images because these include adjacent characters that are connected. In addition, images that are created from various fonts are likely to be distorted during acquisition. In this paper, we propose and test a keyword retrieval system, using a support vector machine (SVM) for the retrieval of low-quality Korean document images. We propose a keyword retrieval method using an SVM to discriminate the similarity between two word images. We demonstrated that the proposed keyword retrieval method is more effective than the accumulated Optical Character Recognition (OCR)-based searching method. Moreover, using the SVM is better than Bayesian decision or artificial neural network for determining the similarity of two images.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.103-120
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• 2007

Node is the semantic unit and a part of structured document. Information retrieval from structured documents offers an opportunity to go subdivided below the document level in search of relevant information, making any element in an structured document a retrievable unit. The node-based document retrieval constitutes several similarity calculating methods and the extended node retrieval method using structure information. Retrieval performance is hardly influenced by the methods for determining document similarity The extended node method outperformed the others as a whole.