A generic ontology framework for indexing keyword search on massive graphs (extended abstract)

Jiaxin Jiang, Byron Choi, Jianliang Xu, Sourav S. Bhowmick

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this work, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. Novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure {\mathbb{G}}. BiG-index is generic since it is applicable to keyword search algorithms that have two properties. BiG-index reduced the runtimes of popular keyword search work Blinks by 50.5% and r-clique by 29.5%.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE
Pages2338-2339
Number of pages2
ISBN (Electronic)9781728191843
ISBN (Print)9781728191850
DOIs
Publication statusPublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021
https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding

Publication series

NameProceedings of IEEE International Conference on Data Engineering (ICDE)
Volume2021-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period19/04/2122/04/21
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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