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现代图书情报技术  2012, Vol. Issue (10): 1-7     https://doi.org/10.11925/infotech.1003-3513.2012.10.01
  数字图书馆 本期目录 | 过刊浏览 | 高级检索 |
个性化服务中关联数据驱动的用户语义建模框架
牛亚真1,2, 祝忠明1
1. 中国科学院国家科学图书馆兰州分馆 兰州 730000;
2. 中国科学院大学 北京 100049
A Linked Data-driven Semantic User Modeling Framework for Personalization Service
Niu Yazhen1,2, Zhu Zhongming1
1. The Lanzhou Branch of National Science Library, Chinese Academy of Sciences, Lanzhou 730000, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 基于关联数据的应用框架,提出一种构建用户语义模型的基本框架,设计用户语义模型,并对所提出的框架进行详细的分析。该框架主要包括两个方面:从LOD云中发现并获取与用户相关的关联数据的方法和策略;利用获取的关联数据构建用户模型的基本过程,包括概念映射、同一性解析、数据融合和模型生成几个步骤。
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牛亚真
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关键词 关联数据LOD用户模型用户建模语义网概念映射数据融合    
Abstract:Based on the framework of Linked Data application, this paper prensents a semantic user modeling framework, and designs a semantic user model in the context of semantic Web, then analyzes the framework in detail. This framework mainly consists of two parts: firstly, it discusses the methods and strategies for discovering and acquiring users data from the LOD cloud; secondly, it introduces the processes for constructing a user model using these data, which includes such steps as concept mapping, identity resolution, data fusion and model generation.
Key wordsLinked data    LOD    User model    User modeling    Semantic Web    Concept mapping    Data fusion
收稿日期: 2012-09-11      出版日期: 2013-01-24
: 

G250.76

 
基金资助:

本文系中国科学院西部之光联合学者项目“机构知识库的语义增强方法与技术研究”的研究成果之一。

通讯作者: 牛亚真     E-mail: niuyazhen10@163.com
引用本文:   
牛亚真, 祝忠明. 个性化服务中关联数据驱动的用户语义建模框架[J]. 现代图书情报技术, 2012, (10): 1-7.
Niu Yazhen, Zhu Zhongming. A Linked Data-driven Semantic User Modeling Framework for Personalization Service. New Technology of Library and Information Service, 2012, (10): 1-7.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2012.10.01      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2012/V/I10/1
[1] Berners-Lee T. Design Issues: Linked Data[EB/OL]. [2012-08-05]. http://www.w3.org/DesignIssues/LinkedData.html.
[2] Sosnovsky S, Dicheva D. Ontological Technologies for User Modelling[J]. International Journal of Metadata, Semantics and Ontologies, 2010, 5(1):32-71.
[3] Razmerita L, Angehrn A, Maedche A. Ontology-based User Modeling for Knowledge Management Systems[C]. In: Proceedings of the 9th International Conference on User Modeling (UM’03). Berlin,Heidelberg: Springer-Verlag, 2003: 213-217.
[4] Niederée C, Stewart A, Mehta B, et al. A Multi-Dimensional, Unified User Model for Cross-System Personalization[EB/OL]. [2012-08-05]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9261&rep=rep1&type=pdf.
[5] Heckmann D, Schwartz T, Brandherm B et al. Gumo-the General User Model Ontology[C]. In: Proceedings of the 10th International Conference on User Modeling 2005,Edinburgh, Scotland, UK.Berlin: Springer-Verlag,2005:428-432.
[6] Dolog P, Schäfer M. A Framework for Browsing, Manipulating and Maintaining Interoperable Learner Profiles[C].In: Proceedings of the 10th International Conference on User Modelling 2005, Edinburgh, Scotland, UK. Berlin, Heidelberg: Sping-Verlag, 2005:397-401.
[7] Cyganiak R, Jentzsch A. The Linking Open Data Cloud Diagram[EB/OL]. [2012-08-05]. http://richard.cyganiak.de/2007/10/lod/.
[8] Latif A, Afzal M T, Helic D, et al. Discovery and Construction of Authors’ Profile from Linked Data (A Case Study for Open Digital Journal)[C]. In: Proceedings of LDOW2010, Releigh, USA. 2010.
[9] 刘媛媛, 李春旺, 黄永文. 基于 LOD 的关联参考服务构建研究[J]. 现代图书情报技术, 2011 (6): 66-71. (Liu Yuanyuan, Li Chunwang, Huang Yongwen. Study on Building the Service of Relevance Reference Based on LOD[J]. New Technology of Library and Information Service, 2011(6): 66-71.)
[10] 沈志宏, 张晓林. 关联数据及其应用现状综述[J]. 现代图书情报技术, 2010(11):1-9. (Shen Zhihong, Zhang Xiaolin. Linked Data and Its Applications: An Overview[J]. New Technology of Library and Information Service, 2010(11):1-9.)
[11] Middleton S E, De Roure D C, Shadbolt N R. Capturing Knowledge of User Preferences: Ontologies in Recommender Systems[C]. In: Proceedings of the 1st International Conference on Knowledge Capture (K-CAP’01). New York: ACM, 2001: 100-107.
[12] Middleton S E, Shadbolt N R, De Roure D C. Capturing Interest Through Inference and Visualization: Ontological User Profiling in Rrecommender Systems[C]. In: Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP’03). New York: ACM, 2003: 62-69.
[13] U-Sem in ImREAL[EB/OL]. [2012-08-05]. http://wis.ewi.tudelft.nl/imreal/u-sem/.
[14] Heath T, Bizer C. Linked Data: Evolving the Web into a Global Data Space[M]. Morgan & Claypool,2011.
[15] DBpedia[EB/OL]. [2012-08-06]. http://wiki.dbpedia.org/About.
[16] The Tabulator[EB/OL]. [2012-08-06]. http://www.w3.org/2005/ajar/tab.
[17] Disco - Hyperdata Browser[EB/OL]. [2012-08-06]. http://www4.wiwiss.fu-berlin.de/bizer/ng4j/disco/.
[18] Swoogle[EB/OL]. [2012-08-08]. http://swoogle.umbc.edu/.
[19] SWSE[EB/OL]. [2012-08-08]. http://swse.deri.org/.
[20] Sindice[EB/OL]. [2012-08-09]. http://sindice.com/.
[21] IDSpider[EB/OL]. [2012-08-09]. http://code.google.com/p/ldspider/.
[22] Schultz A, Matteini A, Isele R, et al. LDIF-A Framework for Large-Scale Linked Data Integration[C]. In: Proceedings of the 21st International World Web Conference (WWW 2012). 2012.
[23] Schultz A, Matteini A, Isele R, et al. LDIF-Linked Data Integration Framework[C]. In: Proceedings of World Wide Web Conference (WWW 2012), Lyon, France. 2012.
[24] Berners-Lee T, Kagal L. The Fractal Nature of the Semantic Web[J]. AI Magazine, 2008, 29(3):29-34.
[25] Polleres A, Scharffe F, Schindlauer R. SPARQL+ + for Mapping Between RDF Vocabularies[C]. In: Proceedings of the 2007 OTM Confederated International Conference on the Move to Meaningful Internet Systems: CoopIS, DOA, ODBASE, GADA, and IS (OTM’07). Berlin,Heidelberg: Springer-Verlag, 2007: 878-896.
[26] Euzenat J, Scharffe F, Zimmermann A. D2. 2.10: Expressive Alignment Language and Implementation[OL]. [2012-08-05]. ftp://ftp.inrialpes.fr/pub/exmo/reports/kweb-2210.pdf.
[27] Haslhofer B. A Web-based Mapping Technique for Establishing Metadata Interoperability[D]. Vienna: University of Vienna, 2008.
[28] Bizer C, Schultz A. The R2R Framework: Publishing and Discovering Mappings on the Web[C]. In: Proceedings of the 1st International Workshop on Consuming Linked Data (COLD 2010), Shanghai,China. 2010.
[29] Isele R, Jentzsch A, Bizer C. Silk Server-Adding Missing Links While Consuming Linked Data[C]. In: Proceedings of the 1st International Workshop on Consuming Linked Data (COLD 2010), Shanghai,China. 2010.
[30] Dong X L, Naumann F. Data Fusion: Resolving Data Conflicts for Integration[J]. Proceedings of the VLDB Endowment, 2009, 2(2):1654-1655.
[31] Le-Phuoc D, Polleres A, Hauswirth M, et al. Rapid Prototyping of Semantic Mash-ups Through Semantic Web Pipes[C]. In: Proceedings of the 18th International Conference on World Wide Web (WWW’09). New York: ACM, 2009: 581-590.
[32] Nikolov A, Uren V, Motta E, et al. Integration of Semantically Annotated Data by the KnoFuss Architecture[C]. In: Proceedings of the 16th International Conference on Knowledge Engineering: Practice and Patterns (EKAW’08). Berlin,Heidelberg: Springer-Verlag, 2008:265-274.
[33] Mendes P N, Mühleisen H, Bizer C. Sieve: Linked Data Quality Assessment and Fusion[C]. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops (EDBT-ICDT’12). New York: ACM, 2012: 116-123.
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