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Conference Paper: OntoCA: Ontology-Aware Caching for Distributed Subgraph Matching

TitleOntoCA: Ontology-Aware Caching for Distributed Subgraph Matching
Authors
KeywordsCaching
Ontology
Partial evaluation
Subgraph matching
Issue Date2023
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, v. 13421 LNCS, p. 527-535 How to Cite?
AbstractWith the growing applications of knowledge graphs in diverse domains, the scale of knowledge graphs is dramatically increasing. Based on the fact that a high percentage of queries in practice is similar to previous queries, extensive caching methods have been proposed to accelerate subgraph matching queries by reusing the results of previous queries. However, most existing methods show poor performance when dealing with distributed subgraph matching queries, as numerous intermediate results from the caching should be transmitted to the worker nodes for further validation, leading to extra communication and computation overhead. In this paper, we propose a novel ontology-aware caching method, called OntoCA, which leverages ontology information for efficient distributed queries. Unlike the existing caching methods, our approach fully employs semantic reasoning to filter intermediate results at an early stage, thus improving the query performance. Furthermore, a workload-adaptive prefetching strategy is proposed to increase the hit ratio of OntoCA. The experimental results show that our proposed OntoCA and prefetching strategy outperforms the existing state-of-the-art distributed query method, reducing the query times by 56.16%.
Persistent Identifierhttp://hdl.handle.net/10722/330324
ISSN
2023 SCImago Journal Rankings: 0.606

 

DC FieldValueLanguage
dc.contributor.authorQin, Yuzhou-
dc.contributor.authorWang, Xin-
dc.contributor.authorHao, Wenqi-
dc.contributor.authorLiu, Pengkai-
dc.contributor.authorSong, Yanyan-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:09:36Z-
dc.date.available2023-09-05T12:09:36Z-
dc.date.issued2023-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, v. 13421 LNCS, p. 527-535-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/330324-
dc.description.abstractWith the growing applications of knowledge graphs in diverse domains, the scale of knowledge graphs is dramatically increasing. Based on the fact that a high percentage of queries in practice is similar to previous queries, extensive caching methods have been proposed to accelerate subgraph matching queries by reusing the results of previous queries. However, most existing methods show poor performance when dealing with distributed subgraph matching queries, as numerous intermediate results from the caching should be transmitted to the worker nodes for further validation, leading to extra communication and computation overhead. In this paper, we propose a novel ontology-aware caching method, called OntoCA, which leverages ontology information for efficient distributed queries. Unlike the existing caching methods, our approach fully employs semantic reasoning to filter intermediate results at an early stage, thus improving the query performance. Furthermore, a workload-adaptive prefetching strategy is proposed to increase the hit ratio of OntoCA. The experimental results show that our proposed OntoCA and prefetching strategy outperforms the existing state-of-the-art distributed query method, reducing the query times by 56.16%.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectCaching-
dc.subjectOntology-
dc.subjectPartial evaluation-
dc.subjectSubgraph matching-
dc.titleOntoCA: Ontology-Aware Caching for Distributed Subgraph Matching-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-031-25158-0_42-
dc.identifier.scopuseid_2-s2.0-85151138916-
dc.identifier.volume13421 LNCS-
dc.identifier.spage527-
dc.identifier.epage535-
dc.identifier.eissn1611-3349-

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