摘要
在信息检索中,查询扩展一直被用来帮助提供更好的查询结果。作为一个热门话题,很多查询扩展方法被提出来,但其中大多数方法都是面向通用搜索引擎的,而没有考虑查询背景和领域背景,更没有考虑两个背景知识的演化。给出一种基于领域本体和查询日志的自适应的本体查询扩展方法 Adap-On。Adap-On首先自动地构建一个领域知识模型,之后通过查询日志增强这个模型,应用中通过基于查询模型和知识模型的混合方法给出扩展关键字,从而最终得到一个自适应查询扩展策略。实验结果表明,Adap-On是有效的,并且优于已有的方法。
Query expansion has long been suggested as an enhancing way for better query results in information retrieval.As a hot issue,many query expansion methods have been put forward.However,most of them focus on the general search engine without taking into account the query space and domain knowledge space,nor even the evolutions of both spaces.In this study,an adaptive ontological query expansion method named Adap-On using query logs and domain ontology is proposed.Adap-On first builds a domain knowledge model automatically and then enhances it by query logs.It will give expansion query keywords by combining the query model and the domain knowledge model in application,and to introduce an expansion strategy into the adaptive query eventually.Experimental results indicate that Adap-On is effective,and outperforms other existing methods.
出处
《计算机应用与软件》
CSCD
北大核心
2014年第11期29-32,39,共5页
Computer Applications and Software
关键词
垂直搜索引擎
查询扩展
查询日志
领域本体
Vertical search engine
Query expansion
Query logs
Domain ontology
