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SPARK:将关键字查询与语义搜索相结合

SPARK: Adapting Keyword Query to Semantic Search
课程网址: http://videolectures.net/iswc07_zhou_akqss/  
主讲教师: Qi Zhou
开课单位: 顶点实验室
开课时间: 2008-07-01
课程语种: 英语
中文简介:
语义搜索有望提供比当前关键字搜索更准确的结果。然而,由于其查询语言的复杂性,语义搜索的进展被推迟。在本文中,我们探索了一种使用关键字来查询语义Web的新方法:该方法自动将关键字查询转换为形式逻辑查询,以便最终用户可以使用熟悉的关键字来执行语义搜索。已经根据这种方法实现了名为“SPARK”的原型系统。给定关键字查询,SPARK输出SPARQL查询的排序列表作为翻译结果。 SPARK中的翻译包括三个主要步骤:术语映射,查询图构建和查询排名。具体地,提出概率查询排序模型以选择最可能的SPARQL查询。在实验中,SPARK取得了令人鼓舞的翻译结果。
课程简介: 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.
关 键 词: 语义搜索; 形式逻辑查询; 排序模型
课程来源: 视频讲座网
最后编审: 2020-07-31:yumf
阅读次数: 93