面向水数据目录服务的分面搜索方法研究

Jun Feng, Shengqiu Kong, B. Du, Jiamin Lu
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引用次数: 1

摘要

传统的数据检索是通过对元数据进行关键字搜索来实现的,但在水务行业,普通用户往往难以表达专业、精确的查询需求。针对这一问题,本文介绍了一种探索性检索方法,即面搜索,通过逐步向用户推荐相关的面。首先,提出了一种统一建模算法,对异构水元数据在XML中构建统一元数据模型;基于该模型,可以提取和过滤候选facet项,从而统一检索各种水元数据。最后,提出了一种facet推荐算法,在排除“无关facet”和“冗余facet”后,随着搜索的深入,通过提示更少但更准确的facet来帮助用户锐化查询。实验结果表明,我们的面推荐算法可以显著提高水数据查询的检索精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Faceted Search Method for Water Data Catalogue Service
Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant facets to the users. Firstly, a unified modeling algorithm is proposed to construct the unified metadata model in XML for heterogeneous water metadata. Based on this model, candidate facet terms can be extracted and filtered, in order to retrieve the various water metadata uniformly. At last, a facet recommendation algorithm is proposed to help the users to sharpen their queries by prompting less but more accurate facets as the search gets deeper, after excluding those "irrelevant facets" and "redundant facets". The experimental results demonstrate that our facet recommendation algorithm can significantly improve the retrieval precision on querying the water data.
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