TY - GEN
T1 - PPKWS
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
AU - Jiang, Jiaxin
AU - Huang, Xin
AU - Choi, Koon Kau
AU - Xu, Jianliang
AU - Bhowmick, Sourav S.
AU - Xu, Lyu
N1 - Funding Information:
In future work, we plan to investigate PPKWS for other query semantics which are relevant to the shortest distance computation, (e.g., community search). We will extend the PPKWS to support keyword search on dynamic graphs. Acknowledgements. This work is partly supported by HKRGC GRF 12232716, 12201119, 12201518, 12201018, and 12200917, and NSFC 61702435. REFERENCES
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085863355&partnerID=8YFLogxK
U2 - 10.1109/ICDE48307.2020.00046
DO - 10.1109/ICDE48307.2020.00046
M3 - Conference proceeding
AN - SCOPUS:85085863355
T3 - Proceedings - International Conference on Data Engineering
SP - 457
EP - 468
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
Y2 - 20 April 2020 through 24 April 2020
ER -