• Title/Summary/Keyword: Similarity Query

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A Trajectory Substitution Privacy Protection Scheme in location-based services

  • Song, Cheng;Zhang, Yadong;Gu, Xinan;Wang, Lei;Liu, Zhizhong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.9
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    • pp.4771-4787
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    • 2019
  • Aimed at the disclosure risk of mobile terminal user's location privacy in location-based services, a location-privacy protection scheme based on similar trajectory substitution is proposed. On the basis of the anonymized identities of users and candidates who request LBS, this scheme adopts trajectory similarity function to select the candidate whose trajectory is the most similar to user's at certain time intervals, then the selected candidate substitutes user to send LBS request, so as to protect user's privacy like identity, query and trajectory. Security analyses prove that this scheme is able to guarantee such security features as anonymity, non-forgeability, resistance to continuous query tracing attack and wiretapping attack. And the results of simulation experiment demonstrate that this scheme remarkably improve the optimal candidate' trajectory similarity and selection efficiency.

Conceptual Retrieval of Chinese Frequently Asked Healthcare Questions

  • Liu, Rey-Long;Lin, Shu-Ling
    • 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.

Performance Improvement of Web Information Retrieval Using Sentence-Query Similarity (문장-질의 유사성을 이용한 웹 정보 검색의 성능 향상)

  • Park Eui-Kyu;Ra Dong-Yul;Jang Myung-Gil
    • Journal of KIISE:Software and Applications
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    • v.32 no.5
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    • pp.406-415
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    • 2005
  • Prosperity of Internet led to the web containing huge number of documents. Thus increasing importance is given to the web information retrieval technology that can provide users with documents that contain the right information they want. This paper proposes several techniques that are effective for the improvement of web information retrieval. Similarity between a document and the query is a major source of information exploited by conventional systems. However, we suggest a technique to make use of similarity between a sentence and the query. We introduce a technique to compute the approximate score of the sentence-query similarity even without a mature technology of natural language processing. It was shown that the amount of computation for this task is linear to the number of documents in the total collection, which implies that practical systems can make use of this technique. The next important technique proposed in this paper is to use stratification of documents in re-ranking the documents to output. It was shown that it can lead to significant improvement in performance. We furthermore showed that using hyper links, anchor texts, and titles can result in enhancement of performance. To justify the proposed techniques we developed a large scale web information retrieval system and used it for experiments.

Word Embeddings-Based Pseudo Relevance Feedback Using Deep Averaging Networks for Arabic Document Retrieval

  • Farhan, Yasir Hadi;Noah, Shahrul Azman Mohd;Mohd, Masnizah;Atwan, Jaffar
    • Journal of Information Science Theory and Practice
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    • v.9 no.2
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    • pp.1-17
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    • 2021
  • Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

Document Summarization using Topic Phrase Extraction and Query-based Summarization (주제어구 추출과 질의어 기반 요약을 이용한 문서 요약)

  • 한광록;오삼권;임기욱
    • Journal of KIISE:Software and Applications
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    • v.31 no.4
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    • pp.488-497
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    • 2004
  • This paper describes the hybrid document summarization using the indicative summarization and the query-based summarization. The learning models are built from teaming documents in order to extract topic phrases. We use Naive Bayesian, Decision Tree and Supported Vector Machine as the machine learning algorithm. The system extracts topic phrases automatically from new document based on these models and outputs the summary of the document using query-based summarization which considers the extracted topic phrases as queries and calculates the locality-based similarity of each topic phrase. We examine how the topic phrases affect the summarization and how many phrases are proper to summarization. Then, we evaluate the extracted summary by comparing with manual summary, and we also compare our summarization system with summarization mettled from MS-Word.

Content Based Image Retrieval System using Histogram Intersection and Autocorrelogram (히스토그램 인터섹션과 오토코릴로그램을 이용한 내용기반 영상검색 시스템)

  • 송석진;김효성;이희봉;남기곤
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.1-7
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    • 2002
  • In this paper, when users choose a query image, we implemented a content-based image retrieval system that users can simply choose and extract a object region of query wanted with not only a whole image but various objects in it. Histogram is obtained by improved HSV transformations from query image and then candidate images are retrieved rapidly by a 1st similarity measure with histogram intersection using representative colors of query image. And finally retrieved images are extracted since 2nd similarity measure with banded autocorrelogram is performed so that recall and precision are improved by combining two retrieval methods that can make up for respective weak points. Moreover images in the database are indexed automatically within feature library that makes possible to retrieve images rapidly.

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Query Space Exploration Model Using Genetic Algorithm

  • Lee, Jae-Hoon;Lee, Sung-Joo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.2
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    • pp.222-226
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    • 2003
  • Information retrieval must be able to search the most suitable document that user need from document set. If foretell document adaptedness by similarity degree about QL(Query Language) of document, documents that search person does not require are searched. In this paper, showed that can search the most suitable document on user's request searching document of the whole space using genetic algorithm and used knowledge-base operator to solve various model's problem.

The Method of Searching Unified Medical Language System Using Automatic Modified a Query (자동 질의수정을 통한 통합의학언어 시스템 검색)

  • 김종광;하원식;이정현
    • Proceedings of the IEEK Conference
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    • 2003.11b
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    • pp.129-132
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    • 2003
  • The metathesaurus(UMLS, 2003AA edition) supports multi language and includes 875, 233 concepts, 2, 146, 897 concept names. It is impossible for PubMed or NLM serve searching of the metatheaurus to retrieval using a query that is not to be text, a fault sentence structure or a part of concept name. That means the user notice correctly suitable medical words in order to get correct answer, otherwise she or he can't find information that they want to find I propose that the method of searching unified medical language system using automatic modified a query for problem that I mentioned. This method use dictionary that is standard for automation of modified query gauge similarity between query and dictionary using string comparison algorithm. And then, the tested term converse the form of metathesaurus for optimized result. For the evaluation of method, I select some query and I contrast NLM method that renewed Aug. 2003.

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The Effectiveness of Hierarchic Clustering on Query Results in OPAC (OPAC에서 탐색결과의 클러스터링에 관한 연구)

  • Ro, Jung-Soon
    • Journal of the Korean Society for Library and Information Science
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    • v.38 no.1
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    • pp.35-50
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    • 2004
  • This study evaluated the applicability of the static hierarchic clustering model to clustering query results in OPAC. Two clustering methods(Between Average Linkage(BAL) and Complete Linkage(CL)) and two similarity coefficients(Dice and Jaccard) were tested on the query results retrieved from 16 title-based keyword searchings. The precision of optimal dusters was improved more than 100% compared with title-word searching. There was no difference between similarity coefficients but clustering methods in optimal cluster effectiveness. CL method is better in precision ratio but BAL is better in recall ratio at the optimal top-level and bottom-level clusters. However the differences are not significant except higher recall ratio of BAL at the top-level duster. Small number of clusters and long chain of hierarchy for optimal cluster resulted from BAL could not be desirable and efficient.

A Similarity Computation Algorithm for Music Retrieval System Based on Query By Humming (허밍 질의 기반 음악 검색 시스템의 유사도 계산 알고리즘)

  • Oh Dong-Yeol;Oh Hae-Seok
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.4 s.42
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    • pp.137-145
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    • 2006
  • A user remembers a melody as not the combination of pitch and duration which is written in score but the contour which is composed of the relative pitch and duration. Because of the way of remembering a melody the previous Music Information Retrieval Systems which uses keyboard Playing or score as the main input melody are not easily acceptable in Query By Humming Systems. In this paper, we mention about the considerable checkpoints for Query By Humming System and previous researches. And we propose the feature extraction which is similar with the way of remembering a melody and similarity computation algorithms between melody in humming and melody in music. The proposed similarity computation algorithms solves the problem which can be happened when only uses the relative pitches by using relative durations.

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