Collective Keyword Query on a Spatial Knowledge Base
| Autor: | Xiongnan Jin, Kyong-Ho Lee, Eunju Jo, Sangjin Shin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information retrieval
Computer science business.industry InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 02 engineering and technology computer.file_format User requirements document Computer Science Applications Data modeling Set (abstract data type) Knowledge-based systems Tree (data structure) Computational Theory and Mathematics Knowledge base 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Pruning (decision trees) RDF business computer Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 31:2051-2062 |
| ISSN: | 2326-3865 1041-4347 |
| DOI: | 10.1109/tkde.2018.2873376 |
| Popis: | The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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