]> BibSonomy publications for /author/Zhou/2007 http://www.bibsonomy.org/burst/author/Zhou/2007 BibSonomy BuRST Feed for /author/Zhou/2007 2008-07-21T00:11:14+02:00 An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations http://www.bibsonomy.org/bibtex/2355fcbb32255f3ba5f41819c00c520ba/iswc2007 iswc2007 2007-11-07T19:13:58+01:00 2007 annotation data_management information_extraction iswc model ontology_(computer_science) research_15 semantic_web semantics social social_network web_annotation
An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations
Mianwei Zhou and Shenghua Bao and Xian Wu and Yong Yu Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea 673--686 (2007)
to 2007 annotation data_management information_extraction iswc model ontology_(computer_science) research_15 semantic_web semantics social social_network web_annotation by iswc2007 on 2007-11-07 19:13:58 ]]> Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember673--686LNCSAn Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations482520072007 annotation data_management information_extraction iswc model ontology_(computer_science) research_15 semantic_web semantics social social_network web_annotation 2007-11-07 19:13:58.0This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency. SPARK: Adapting Keyword Query to Semantic Search http://www.bibsonomy.org/bibtex/2ce56748004a2f073a3b016b4e7a4e54a/iswc2007 iswc2007 2007-11-07T19:13:58+01:00 2007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web
SPARK: Adapting Keyword Query to Semantic Search
Qi Zhou and Chong Wang and Miao Xiong and Haofen Wang and Yong Yu Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea 687--700 (2007)
to 2007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web by iswc2007 on 2007-11-07 19:13:58 ]]> Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember687--700LNCSSPARK: Adapting Keyword Query to Semantic Search482520072007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web 2007-11-07 19:13:58.0Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK’ has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result. EIAW: Towards a Business-friendly Data Warehouse Using Semantic Web Technologies http://www.bibsonomy.org/bibtex/246782dd021c8daeb53589155d858981a/iswc2007 iswc2007 2007-11-07T19:13:58+01:00 2007 application_software datum finance in_use_1 iswc ontology_(computer_science) semantic semantic_web technology tool using warehouse web
EIAW: Towards a Business-friendly Data Warehouse Using Semantic Web Technologies
Guotong Xie and Yang Yang and Shengping Liu and Zhaoming Qiu and Yue Pan and Xiongzhi Zhou Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea 851--904 (2007)
to 2007 application_software datum finance in_use_1 iswc ontology_(computer_science) semantic semantic_web technology tool using warehouse web by iswc2007 on 2007-11-07 19:13:58 ]]> Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember851--904LNCSEIAW: Towards a Business-friendly Data Warehouse Using Semantic Web Technologies482520072007 application_software datum finance in_use_1 iswc ontology_(computer_science) semantic semantic_web technology tool using warehouse web 2007-11-07 19:13:58.0Data warehouse is now widely used in business analysis and decision making processes. To adapt the rapidly changing business environment, we develop a tool to make data warehouses more business-friendly by using Semantic Web technologies. The main idea is to make business semantics explicit by uniformly representing the business metadata (i.e. conceptual enterprise data model and multidimensional model) with an extended OWL language. Then a mapping from the business metadata to the schema of the data warehouse is built. When an analysis request is raised, a customized data mart with data populated from the data warehouse can be automatically generated with the help of this built-in knowledge. This tool, called Enterprise Information Asset Workbench (EIAW), is deployed at the Taikang Life Insurance Company, one of the top five insurance companies of China. User feedback shows that OWL provides an excellent basis for the representation of business semantics in data warehouse, but many necessary extensions are also needed in the real application. The user also deemed this tool very helpful because of its flexibility and speeding up data mart deployment in face of business changes.