PPKWS: An efficient framework for keyword search on public-private networks

Jiaxin Jiang, Xin Huang, Koon Kau Choi, Jianliang Xu, Sourav S. Bhowmick, Lyu Xu

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

4 Citations (Scopus)


Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public- private networks has not yet been studied. For example, query answers on private networks and on a combination of private and public networks can be different. In this paper, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. Since there have been plenty of keyword search semantics, we select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that spans through them.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781728129037
Publication statusPublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: 20 Apr 202024 Apr 2020
https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding (Link to conference proceedings)

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems


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