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Titlebook: Web Information Systems Engineering – WISE 2015; 16th International C Jianyong Wang,Wojciech Cellary,Yanchun Zhang Conference proceedings 2

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21#
發(fā)表于 2025-3-25 04:23:40 | 只看該作者
22#
發(fā)表于 2025-3-25 07:48:07 | 只看該作者
GraSS: An Efficient Method for RDF Subgraph Matching,m encoding a star subgraph into a bit string. A SPARQL query graph is decomposed into several star query subgraphs which can be efficiently processed benefiting from succinct FFD-index data structure. Extensive evaluation shows that our approach outperforms RDF-3X and gStore on solving subgraph matching.
23#
發(fā)表于 2025-3-25 15:12:49 | 只看該作者
A Dynamically Extensible Open Cross-Document Link Service,ns via gateways. The presented concepts and architecture for dynamic extensibility improve the document life cycle in so-called cross-media information spaces and enable future-proof cross-document linking.
24#
發(fā)表于 2025-3-25 16:25:28 | 只看該作者
Time-Sensitive Topic Derivation in Twitter, clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.
25#
發(fā)表于 2025-3-25 22:53:17 | 只看該作者
26#
發(fā)表于 2025-3-26 01:19:44 | 只看該作者
GraSS: An Efficient Method for RDF Subgraph Matching,m encoding a star subgraph into a bit string. A SPARQL query graph is decomposed into several star query subgraphs which can be efficiently processed benefiting from succinct FFD-index data structure. Extensive evaluation shows that our approach outperforms RDF-3X and gStore on solving subgraph matching.
27#
發(fā)表于 2025-3-26 07:54:31 | 只看該作者
28#
發(fā)表于 2025-3-26 09:04:12 | 只看該作者
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發(fā)表于 2025-3-26 13:03:22 | 只看該作者
30#
發(fā)表于 2025-3-26 20:12:23 | 只看該作者
Grouping Product Aspects from Short Texts Using Multiple Classifiers,iew websites. Because of the distinct vocabulary used by consumers to describe the same aspects of a product, it is necessary to group pros and cons to support consumers’ decision making. For this purpose we propose a supervised classification model, consisting of an ensemble classifier that combine
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