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研究生:林宣丞
研究生(外文):Hsuan-Chen Lin
論文名稱:整合多種字詞相似度的創新方法
論文名稱(外文):A Novel Approach to Aggregating Various Word Similarity Measures
指導教授:李允中李允中引用關係
指導教授(外文):Jonathan Lee
口試委員:孫雅麗鄭有進劉立頌
口試日期:2016-07-25
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:24
中文關鍵詞:WSDLOWL-TCOWLS-TC服務匹配
外文關鍵詞:WSDLOWL-TCOWLS-TCService Matching
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近年來,網路服務的數量不斷上升.對於如何組成複雜服務的研究也日新月異.在組成服務的流程中,匹配服務扮演不可或缺的角色.尋找最佳匹配的服務的重要性不可言喻.要找出最適合的服務,在服務文件裡的重要資訊必須被完整地取出.將這些資料放置在良好的架構裡,並將兩個結構的差異量化.運用這些數值可以幫助網路服務匹配提供搜尋結果.接著,文字語意必須納入考量.許多研究著重在不同語意測量方法下對某種任務的表現.整合多種語意的研究並不多見.在此篇論文中,我們提出了一個整合不同語意系統 的框架來計算提出的資訊.此框架是設計用來辨認出服務文件的特色已幫助服務匹配.

In the recent years, the number of web services has risen up swiftly. Numerous works have been done on how to compose services. In the process of composing services, service matching plays an indispensable role. The importance of searching the most suitable service among composition can not be overemphasized. In order to find the best match for a service, the essential information in the service document should be extracted impact. Then the data should be put in a structure that describes the service perfectly. Then the difference between two structures from two service documents should be quantized. By using these values, a proper web service match discovery could offer a search result to the user. To measure the difference, word semantic must be considered. Many works focus on the performance of various measures for different tasks. Rarely do the researchers study on aggregating different measures. In this thesis, we propose a framework which aggregates different semantic measure for data extracted from WSDL. This framework is designed to identify the features in the service document, and use several measures for precisely interpret the difference between both semantic and structure information of two services.

誌謝 iii
Acknowledgements v
摘要 vii
Abstract ix
1 Introduction 1
2 Related Work 3
3 Semantic Systems 5
3.1 Taxonomical 5
3.2 Statistical 5
3.2.1 LatentSemanticAnalysis(LSA) 6
3.2.2 Probabilistic Latent Semantic Analysis (PLSA) 6
3.2.3 RelationbetweeninLSAandPLSA 7
3.2.4 PMI-IR 7
4 Graph Matching Web Service Discovery 9
4.1 WSDLIntroduction 9
4.2 TestDataset 10
4.3 SimilarityComputation 11
5 Aggregation Framework 13
5.1 CorpusandModels 13
5.2 BasicAssumptions 13
5.3 StepsinAggregation 14
5.4 An Example 17
6 Evaluation 19
7 Conclusion 21
Bibliography 23

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[2] Owls-tc. http://projects.semwebcentral.org/projects/owlstc/. Accessed: 2016- 07-20.
[3] Xml wsdl. http://www.daml.org/services/owl-s/1.0/owl-s-wsdl.html. Ac- cessed: 2016-07-20.
[4] Xml wsdl. http://www.w3schools.com/xml/xml_wsdl.asp. Accessed: 2016-07- 20.
[5] D. M. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77–84, 2012.
[6] G. Bouma. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL, pages 31–40, 2009.
[7] K. W. Church and P. Hanks. Word association norms, mutual information, and lex- icography. Computational linguistics, 16(1):22–29, 1990.
[8] T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Ma- chine learning, 42(1-2):177–196, 2001.
[9] I. Kaur and A. J. Hornof. A comparison of lsa, wordnet and pmi-ir for predicting user click behavior. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 51–60. ACM, 2005.
[10] T. K. Landauer, P. W. Foltz, and D. Laham. An introduction to latent semantic anal- ysis. Discourse processes, 25(2-3):259–284, 1998.
[11] G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39–41, 1995.
[12] F. Šarić, G. Glavaš, M. Karan, J. Šnajder, and B. D. Bašić. Takelab: Systems for measuring semantic text similarity. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main con- ference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pages 441–448. Association for Computational Linguistics, 2012.
[13] P. Turney. Mining the web for synonyms: Pmi-ir versus lsa on toefl. 2001.
[14] U. Zernik. Lexical acquisition: exploiting on-line resources to build a lexicon. Psy- chology Press, 1991.

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