Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation
Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation
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DOI：
10.1016/j.websem.2011.10.001
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发表时间：
2011-12-01
影响因子：
2.5
通讯作者：
Lavrenko, Victor
中科院分区：
文献类型：
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作者：
Halpin, Harry;Lavrenko, Victor
我们调查的可能性，使用语义Web数据，以提高超文本Web搜索。特别是，我们使用相关性反馈创建一个“良性循环”之间的数据收集的语义Web的关联数据和网页收集的超文本Web。以前的方法通常认为语义Web和超文本Web上的搜索是完全不同的，索引和搜索是在不同的域上进行的。虽然相关反馈传统上提高了信息检索性能，相关反馈通常用于提高在一个单一的数据集的排名。我们的新方法是使用超文本Web结果的相关性反馈，以提高语义Web搜索，并从语义Web的结果，以提高超文本Web数据的检索。在这两种情况下，评估都是基于从现实生活中的查询日志中选择并由人类法官检查的某些类型的信息查询（抽象概念，人和地点）来执行的。我们评估我们的工作在广泛的算法和选项，并显示它提高了基线性能，这些查询部署的系统，以及，如语义Web搜索引擎的搜索引擎的搜索结果-S和雅虎！网络搜索。我们进一步表明，使用语义Web推理似乎会损害性能，而伪相关性反馈在这两种情况下提高性能，虽然不像实际的相关性反馈。最后，我们的评估是第一个严格的“克兰菲尔德”的语义Web搜索的评估。(C)2011爱思唯尔有限公司版权所有。
We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a 'virtuous cycle' between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous 'Cranfield' evaluation of Semantic Web search. (C) 2011 Elsevier B.V. All rights reserved.