[1]吴鸿汉,瞿裕忠.理解语义网实体:基于概念空间的摘要方法[J].东南大学学报(自然科学版),2009,39(4):723-727.[doi:10.3969/j.issn.1001-0505.2009.04.014]
 Wu Honghan,Qu Yuzhong.Understanding semantic web entity: concept space based summarization method[J].Journal of Southeast University (Natural Science Edition),2009,39(4):723-727.[doi:10.3969/j.issn.1001-0505.2009.04.014]
点击复制

理解语义网实体:基于概念空间的摘要方法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
39
期数:
2009年第4期
页码:
723-727
栏目:
计算机科学与工程
出版日期:
2009-07-20

文章信息/Info

Title:
Understanding semantic web entity: concept space based summarization method
作者:
吴鸿汉 瞿裕忠
东南大学计算机科学与工程学院, 南京 211189
Author(s):
Wu Honghan Qu Yuzhong
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
关键词:
语义网 基于概念空间的摘要方法 RDF数据组织 RDF数据浏览 RDF数据摘要
Keywords:
semantic web concept space based summarization method RDF data organization RDF data browse RDF data summarization
分类号:
TP399
DOI:
10.3969/j.issn.1001-0505.2009.04.014
摘要:
为了快速准确地理解语义网实体,提出了基于概念空间的摘要方法.针对RDF数据的无序性问题,首先将一个实体的不同侧面的RDF数据划分到不同的概念空间中去.其次在同一个概念空间中的数据依照谓语聚类的方法进行组织.对于实体重用带来的RDF数据的可信度问题,根据数据的来源,在数据的权威性维度上对实体数据进行划分.针对实体数据的大规模特性,提出实体数据摘要的方法,综合基于结构的重要性、用户偏好以及来源文档的重要性对数据的重要性进行计算.实验结果表明:基于概念空间的摘要方法能够有效地帮助人们快速理解语义网实体; 该方法相对于其他RDF浏览器有4%~17%的效率提升; 在用户比较熟悉RDF的情况下,使用该方法能够提高20%左右的效率.
Abstract:
To achieve fast and accurate understanding of semantic web entities, a concept space based summarization method is proposed.To organize the information, the resource description framework(RDF)data about an entity are partitioned into different concept spaces.In each concept space, the data are clustered by predicates.On the confidence of information, the authoritative dimension of RDF data is proposed.The value of this dimension is set according to the sources of the data.To address the scalability problem, an RDF data summarization method is proposed.The importance of data is asserted by its centrality in the graph structure, user preferences and the popularity of documents containing it.The results of experiments show that the proposed method is efficient in supporting the understanding of semantic web entities.Generally, the method is 4% to 17% faster than the state of art RDF browser.When the user is familiar with the RDF data model, the improvement can be 20%.

参考文献/References:

[1] Davidson M J,Dove L,Weltz J.Mental models and usability,cognative psychology 404[R].Chicago:Depaul University,1999.
[2] Ranganathan S R. Elements of library classification[M].New York:Asia Publishing House,1962.
[3] Schraefel M C,Shadbolt Nigel R,Gibbins Nicholas,et al.CS AKTive space:representing computer science in the semantic web[C] //Proc of the 13th International Conference on World Wide Web.Paris,France,2004:384-392.
[4] Oren E,Delbru R,Decker S.Extending faceted navigation for RDF data[C] //Proc of 5th International Semantic Web Conference.Athens,Georgia,USA,2006:559-572.
[5] Berners-Lee T,Chen Yuhsin,Chilton Lydia,et al.Tabulator:exploring and analyzing linked data on the semantic web[C] //Proc of the 3rd International Semantic Web User Interaction Workshop.Athens,Georgia,USA,2006.
[6] Rutledge L,Hardman L.Making RDF presentable:integrated global and local semantic Web browsing[C] //Proc of the 14th International Conference on World Wide Web.Chiba,Japan,2005:199-206.
[7] Tummarello G,Delbru R,Oren E.Sindice.com:weaving the open linked data[C] //Proc of the International Semantic Web Conference.Busan,Korea,2007:552-565.
[8] Zhang X,Cheng G,Qu Y.Ontology summarization based on RDF sentence graph[C] //Proc of 16th World Wide Web Conference.Banff,Canada,2007:707-715.
[9] Maybury M T.Generating summaries from event data [J]. Information Processing and Management,1995,31(5):735-751.
[10] Yee Ka-Ping,Swearingen Kirsten,Li Kevin,et al.Faceted metadata for image search and browsing[C] //Proc of the SIGCHI Conference on Human Factors in Computing Systems.New York,USA,2003:401-408.

备注/Memo

备注/Memo:
作者简介: 吴鸿汉(1976—),男,博士生; 瞿裕忠(联系人),男,博士,教授,博士生导师,yzqu@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60773106).
引文格式: 吴鸿汉,瞿裕忠.理解语义网实体:基于概念空间的摘要方法[J].东南大学学报:自然科学版,2009,39(4):723-727.[doi:10.3969/j.issn.1001-0505.2009.04.014]
更新日期/Last Update: 2009-07-20