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- 673 creator mianwei-zhou.
- 673 creator shenghua-bao.
- 673 creator xian-wu.
- 673 creator yong-yu.
- 673 type InProceedings.
- 673 label "An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations".
- 673 sameAs 673.
- 673 abstract "This 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.".
- 673 hasAuthorList authorList.
- 673 hasTopic Data_management.
- 673 hasTopic Information_extraction.
- 673 hasTopic Ontology_%28computer_science%29.
- 673 hasTopic Semantic_Web.
- 673 hasTopic Social_network.
- 673 hasTopic Web_annotation.
- 673 isPartOf proceedings.
- 673 title "An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations".
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- 687 creator chong-wang.
- 687 creator haofen-wang.
- 687 creator miao-xiong.
- 687 creator qi-zhou.
- 687 creator yong-yu.
- 687 type InProceedings.
- 687 label "SPARK: Adapting Keyword Query to Semantic Search".
- 687 sameAs 687.
- 687 abstract "Semantic 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.".
- 687 hasAuthorList authorList.
- 687 hasTopic Application_software.
- 687 hasTopic Ontology_%28computer_science%29.
- 687 hasTopic Semantic_Web.
- 687 isPartOf proceedings.
- 687 title "SPARK: Adapting Keyword Query to Semantic Search".
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- 71 creator fernando-bobillo.
- 71 creator juan-gomez-romero.
- 71 creator miguel-delgado.