Exploiting Cluster-Skipping Inverted Index for Semantic Place Retrieval
PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023(2023)
摘要
Semantic place retrieval aims to find the top-k place entities, which are both textually relevant and spatially close to a given query, from a knowledge graph. In this work, our contribution toward improving the efficiency of semantic place retrieval is two-fold. First, we show that by applying an ad hoc yet intuitive restriction on the depth of search on the knowledge graph, it is possible to adopt IR-tree indexing scheme [7], which has been introduced for processing spatial keyword queries, for the semantic place retrieval scenario. Secondly, as a novel solution to this problem, we adapt the idea of cluster-skipping inverted index (CS-IIS) [1, 4], which has been originally proposed for retrieval over topically clustered document collections. Our experiments show that CS-IIS is comparable to IR-tree in terms of CPU time, while it yields substantial efficiency gains in terms of I/O time during query processing.
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关键词
location-based search,spatial keyword query processing
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