耦合关系分析下的Top-k关键字推荐方法  

Coupling Relationship-based Top-k Keyword Suggestion Method

作　　者：崔婉秋 李昕[2] 孟祥福[3] 崔岩 

机构地区：[1]辽宁工业大学电子与信息工程学院,辽宁锦州121001 [2]辽宁工业大学计算中心,辽宁锦州121001 [3]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105 [4]辽宁省农业经济学校,辽宁锦州121001

出　　处：《小型微型计算机系统》2016年第8期1686-1691,共6页Journal of Chinese Computer Systems

基　　金：国家青年科学基金项目(61003162)资助;辽宁省自然科学基金项目(2013020028)资助;辽宁省教育厅杰出青年学者成长计划项目(LJQ2013038)资助;辽宁省自然基金项目(2013020028)资助

摘　　要：由于用户对关系数据库内容了解不够充分,使他们很难找出合适的关键字表达自己的查询意图.因此提出一种语义相关关键字推荐方法,通过分析关系数据库中的词条与用户初始查询所提供的关键字之间的语义相关性,为用户提供top-k个与初始查询语义相关的候选关键字来拓宽用户对目标数据库内容的了解,从而帮助他们表达出有效的关键字查询条件.为了评估数据库中词条与查询关键字之间的语义相关性,提出反映词条之间显式和隐式关联的词条耦合关系.然后,利用阈值算法快速返回前k个与其语义相关的候选关键字.实验证明了提出的词条耦合关系评估方法能够有效捕获到词条之间的复杂语义关系,同时也验证了top-k相关关键字选取算法的性能.Due to the insufficient knowledge of users about the database content, most of them cannot easy to find appropriate keywords to express their query intentions. This paper proposes a novel approach, which can provide a list of keywords that semantically related to the set of given query keywords by analyzing the correlations between terms in database and query keywords. The suggestion would broaden the knowledge of users and help them to formulate more efficient keyword queries. To capture the correlations between terms in database and query keywords, a coupling relationship measuring method is proposed to model both the term couplings, which can reveal the explicit and implicit relationships between terms. Then an order of terms in database is created for each query keyword and then the threshold algorithm （TA） is to expeditiously generate top-k ranked semantically related terms. The experiments demonstrate that our term coupling relationship measuring method can efficiently capture the semantic correlations between terms. The performance of top-k related term selection algorithm is also demonstrated.

关 键 词：关系数据库 关键字查询 词条耦合关系 top-k的选择 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]