[Objective] As the number of available cloud services increases exponentially, the problem of cloud service discovery and selection arises. [Methods] Semantic retrieval technology in use of information retrieval, semantic analysis and information fusion can improve retrieval efficiency. Combined with Ontology technology can ensure search processes accuracy and consistency, and realize cloud service discovery and selection. [Results] This paper can semantically represent and semantically annotate cloud services. According to extracting semantically annotate terms, it applies vector value to create semantic indexing. Using semantic search engine calculate vector space value between query sentence and index data, and obtain documents similarity. [Limitations] Relevant algorithms involved in some semantic retrieval system are still in development. This paper researches semantic retrieval system as a whole, every module just applies these basic algorithms, algorithm improvement is not involved. [Conclusions] Empirical research proves Ontology technology applied in semantic retrieval system achieves good effects. Especially it is suitable for retrieval of unstructured information, when changes between Ontology and semantic need to keep consistency.
唐守利, 徐宝祥. 基于本体的云服务语义检索系统研究[J]. 现代图书情报技术, 2014, 30(12): 27-35.
Tang Shouli, Xu Baoxiang. Research on Ontology-based Cloud Services Semantic Retrieval System. New Technology of Library and Information Service, 2014, 30(12): 27-35.
[1] Arpirezs J, Perez A G, Lozano A, et al. (Onto)2 Agent:An Ontology-based www broker to Select Ontologies [C]. In: Proceedings of the Workshop on Application of OntoLogies and Problem-Solving Methods. 1998: 16-24.
[2] OntoBroker [EB/OL]. [2014-08-27]. http://www.ontoprise.de/ en/products/ontobroker/.
[3] Scalable Knowledge Composition(SKC) [EB/OL]. [2014-08-27]. http://infolab.stanford.edu/SKC/.
[4] W3C Semantic Web Activity [EB/OL]. [2014-08-27]. http:// www.w3.org/.
[5] Cost R S, Finin T, Joshi A, et al. ITTALKS:A Case Study in the Semantic Web [J]. IEEE Intelligent Systems, 2002, 17(1): 40-47.
[6] Peim M, Franconi E, Paton N W, et al. Query Processing with Description Logic Ontologies over Object-Wrapped Databases [C]. In: Proceedings of the 14th International Conference on Scientific and Statistical Database Management (SSDBM). IEEE Computer Society, 2002: 27-36.
[7] Guo M, Li S, Dong J, et al. Ontology-based Product Data Integration [C]. In: Proceedings of the 17th International Conference on Advanced Information Networking and Applications (AINA), Xi'an, China. IEEE Computer Society, 2003: 530-533.
[8] 陈刚, 陆汝钤, 金芝. 基于领域知识重用的虚拟领域本体构造[J]. 软件学报, 2003, 14(3): 350-355. (Chen Gang, Lu Ruqian, Jin Zhi. Constructing Virtual Domain Ontologies Based on Domain Knowledge Reuse [J]. Journal of Software, 2003, 14(3): 350-355.)
[9] 武成岗, 焦文品, 田启家, 等. 基于本体论和多主体的信息检索服务器[J]. 计算机研究与发展, 2001, 38(6): 641-647. (Wu Chenggang, Jiao Wenpin, Tian Qijia, et al. An Information Retrieval Server Based on Ontology and Multi-Agent [J]. Journal of Computer Research and Development, 2001, 38(6): 641-647.)
[10] Armbrust M, Fox A, Griffith R, et al. A View of Cloud Computing [J]. Communications of the ACM, 2010, 53(4): 50-58.
[11] Masseroli M, Ghisalberti G. Bio-SeCo: Integration and Global Ranking of Biomedical Search Results [A].//Ceri S, Brambilla M. Search Computing: Trends and Developments [M]. Springer, 2011: 203-214.
[12] Hitzler P, Krotszsch M, Rudolph S, 等. 语义Web技术基础[M]. 北京: 清华大学出版社, 2012: 13-18. (Hitzler P, Krotszsch M, Rudolph S, et al. Foundations of Semantic Web Technologies [M]. Beijing: Tsinghua University Press, 2012: 13-18.)
[13] Gruber T R. A Translation Approach to Portable Ontology Specifications [J]. Knowledge Acquisition, 1993, 5(2): 199-220.
[14] Fensel D. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce [M]. Springer, 2003: 90-96.
[15] Garside R, Smith N. A Hybrid Grammatical Tagger: CLAWS4 [A].//Garside R, Leech Gand, McEnery A. Corpus Annotation: Linguistic Information from Computer Text Corpora [M]. London: Longman, 1997: 102-121.
[16] Devedzic V, Gasevic D. Web 2.0 and Semantic Web, Annals of Information Systems [M]. Springer, 2009: 25-54.
[17] 刘豹, 张桂平, 蔡东风. 基于统计和规则相结合的科技术语自动抽取研究[J]. 计算机工程与应用, 2008, 44(23): 147-150. (Liu Bao, Zhang Guiping, Cai Dongfeng. Technical Term Automatic Extraction Research Based on Statistics and Rules [J]. Computer Engineering and Applications, 2008, 44(23): 147-150.
[18] Kageura K,Umino B. Methods of Automatic Term Recognition:A Review [J]. Terminology, 1996, 3(2): 259-289.
[19] Frantzi K, Ananiadou S, Mima H. Automatic Recognition of Multi-word Terms: The C-value/NC-value Method [J]. International Journal on Digital Libraries, 2000, 3(2): 115-130.
[20] 朱靖波, 陈文亮. 基于领域知识的文本分类[J]. 东北大学学报: 自然科学版, 2005, 26 (8): 733-735. (Zhu Jingbo, Chen Wenliang. An Approach Based on Domain Knowledge to Text Categorization [J]. Journal of Northeastern University: Natural Science, 2005, 26(8): 733-735.)
[21] Barrón-Cedeño A, Sierra G, Drouin P, et al. An Improved Automatic Term Recognition Method for Spanish [A].//Computational Linguistics and Intelligent Text Processing [M]. Lecture Notes in Computer Science, 2009, 5449: 126-136.
[22] Kawai Y, Fujita Y, Kumamoto T, et al. Using a Sentiment Map for Visualizing Credibility of News Sites on the Web[C]. In: Proceedings of the 2nd ACM Workshop on Information Credibility on the Web (WICOW 2008). New York, NY, USA: ACM, 2008: 53-58.
[23] Castells P, Fernández M, Vallet D.An Adaptation of the Vector-space Model for Ontology-based Information Retrieval [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(2): 261-272.
[24] 台德艺, 王俊. 文本分类特征权重改进算法[J]. 计算机工程, 2010, 36(9): 197-199. (Tai Deyi, Wang Jun. Improved Feature Weighting Algorithm for Text Categorization [J]. Computer Engineering, 2010, 36(9): 197-199.)