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Titlebook: Weaving Services and People on the World Wide Web; Irwin King,Ricardo Baeza-Yates Book 2009 Springer-Verlag Berlin Heidelberg 2009 Access.

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樓主: Grant
51#
發(fā)表于 2025-3-30 08:49:19 | 只看該作者
Acquisition of Vernacular Place Names from Web Sourcesat various web sources containing user created geographic information and business data can be used to represent neighbourhoods in Cardiff, UK. The resulting representations can differ in their spatial extent from administrative definitions. The chapter closes with an outlook on future research questions.
52#
發(fā)表于 2025-3-30 14:30:45 | 只看該作者
Acquisition of Vernacular Place Names from Web Sourcesat various web sources containing user created geographic information and business data can be used to represent neighbourhoods in Cardiff, UK. The resulting representations can differ in their spatial extent from administrative definitions. The chapter closes with an outlook on future research questions.
53#
發(fā)表于 2025-3-30 18:40:24 | 只看該作者
On the Effect of Group Structures on Ranking Strategies in Folksonomieshe grouping of resources (one-tailed t-Test, level of significance α=0.05). Furthermore, tag recommendations profit from the group context, and it is possible to make very good recommendations even for untagged resources– which currently known tag recommendation algorithms cannot fulfill.
54#
發(fā)表于 2025-3-30 21:55:10 | 只看該作者
55#
發(fā)表于 2025-3-31 01:40:15 | 只看該作者
56#
發(fā)表于 2025-3-31 06:12:31 | 只看該作者
Resolving Person Names in Web People Search empirically evaluated in this context. On the SemEval 2007 Web People Search it is shown that the person cluster hypothesis holds reasonably well and that the Single Pass Clustering and Agglomerative Clustering methods provide the best performance.
57#
發(fā)表于 2025-3-31 12:48:51 | 只看該作者
58#
發(fā)表于 2025-3-31 13:47:56 | 只看該作者
59#
發(fā)表于 2025-3-31 20:20:32 | 只看該作者
Semantic Services for Wikipediactive characteristics, like entity-based link graph, abundant categorization and semi-structured layout, and can serve as an ideal data source to extract high quality and well-structured data. In this chapter, we first propose several solutions to extract knowledge from Wikipedia. We do not only con
60#
發(fā)表于 2025-4-1 00:49:18 | 只看該作者
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