CAJ | 학술논문

空间关键词搜索立足于查找满足用户查询意图且空间距离相近的兴趣点（point of interest ， POI），在地图搜索等领域有着广泛的应用．传统的空间关键词搜索方法仅考虑关键词与POI点在文本上的匹配程度，忽略了查询的语义信息，因而会导致相关结果丢失以及无关结果引入等问题．针对传统方法的局限，提出了语义增强的空间关键词搜索方法S3（semantic‐enhanced spatial keyword search）．该方法对查询关键词中包含的语义信息进行分析，并结合语义相关性和空间距离对POI点进行有效的排序．S3方法主要有以下2个技术挑战：1）如何对语义信息进行分析．为此，S3引入了知识库对POI数据进行语义扩充，并提出了一种基于图的语义距离度量方式．结合语义距离和空间距离，S3给出POI点的综合排序方案．2）如何在大规模数据上即时地返回top‐k搜索结果．针对这一挑战，提出了一种新型的语义空间混合索引结构GRT ree（graph rectangle tree），并研究了有效的剪枝策略．在大规模真实数据集上的实验表明，S3不仅能够返回更为相关的结果，而且有着很好的效率和可扩展性．
공간관건사수색립족우사조만족용호사순의도차공간거리상근적흥취점（point of interest ， POI），재지도수색등영역유착엄범적응용．전통적공간관건사수색방법부고필관건사여POI점재문본상적필배정도，홀략료사순적어의신식，인이회도치상관결과주실이급무관결과인입등문제．침대전통방법적국한，제출료어의증강적공간관건사수색방법S3（semantic‐enhanced spatial keyword search）．해방법대사순관건사중포함적어의신식진행분석，병결합어의상관성화공간거리대POI점진행유효적배서．S3방법주요유이하2개기술도전：1）여하대어의신식진행분석．위차，S3인입료지식고대POI수거진행어의확충，병제출료일충기우도적어의거리도량방식．결합어의거리화공간거리，S3급출POI점적종합배서방안．2）여하재대규모수거상즉시지반회top‐k수색결과．침대저일도전，제출료일충신형적어의공간혼합색인결구GRT ree（graph rectangle tree），병연구료유효적전지책략．재대규모진실수거집상적실험표명，S3불부능구반회경위상관적결과，이차유착흔호적효솔화가확전성．
Spatial keyword search finds the points‐of‐interest (POIs) which are not only relevant to users’ query intent ,but also close to query location .Spatial keyword search has many important applications ,such as map search .Previous methods for spatial keyword search have the limitation that they only consider textual relevance of POIs to query keywords ,and neglect the semantics of queries .So these methods may not be able to return relevant results or return many irrelevant results . To address this problem ,this paper introduces a semantic‐enhanced spatial keyword search method , named S3 (semantic‐enhanced spatial keyw ord search) .Given a query ,S3 analyzes the semantics of the query keywords to measure semantic distances of POIs to the query .Then ,it utilizes a novel POI ranking mechanism by combining both semantic and spatial distance for effective POI search .S3 has the following challenges .Firstly ,S3 introduces knowledge bases to help capture query semantics and introduces a ranking scoring function that considers both semantic distance and spatial distance . Secondly ,it calls for instant search on large‐scale POI data sets .To address this challenge ,we devise a novel index structure GRT ree , and develop some effective pruning techniques based on this structure .The extensive experiments on a real dataset show that S3 not only produces high‐quality results ,but also has good efficiency and scalability .