Abstract
Graph query autocompletion (gQAC) helps users formulate graph queries in a visual environment (a.k.a GUI). It takes a graph query that the user is formulating as input and generates a ranked list of query suggestions. Since it is impossible to accurately predict the user’s target query, the current state-of-the-art of gQAC sometimes fails to produce useful suggestions. In such scenarios, it is natural for the user to ask why are useful suggestions not returned. In this paper, we address the why-not questions of gQAC. Specifically, given an intermediate query q, a target query \(q_t\), and a gQAC system X, the why-not questions of gQAC seek for the minimal refinement of the configuration of X, with respect to a penalty model, such that at least one useful suggestion towards \(q_t\) appears in the returned suggestions. We propose a generic ranking function for existing gQAC systems. We propose a search algorithm for the why-not questions.
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References
Chapman, A., Jagadish, H.V.: Why not? In: SIGMOD, pp. 523–534 (2009)
Islam, M.S., Liu, C., Li, J.: Efficient answering of why-not questions in similar graph matching. TKDE 27, 2672–2686 (2015)
Jayaram, N., Goyal, S., Li, C.: VIIQ: auto-suggestion enabled visual interface for interactive graph query formulation. PVLDB 8, 1940–1951 (2015)
Li, G., Ng, N., Yi, P., Zhang, Z., Choi, B.: Answering the why-not questions of graph query autocompletion (2018). https://goo.gl/4Hpt5m
Mottin, D., Bonchi, F., Gullo, F.: Graph query reformulation with diversity. In: KDD, pp. 825–834 (2015)
Nandi, A., Jagadish, H.V.: Effective phrase prediction. In: PVLDB, pp. 219–230 (2007)
NLM: PubChem. ftp://ftp.ncbi.nlm.nih.gov/pubchem/
Pienta, R., Hohman, F., Tamersoy, A., Endert, A., Navathe, S., Tong, H., Chau, D.H.: Visual graph query construction and refinement. In: SIGMOD, pp. 1587–1590 (2017)
Yi, P., Choi, B., Bhowmick, S., Xu, J.: AutoG: a visual query autocompletion framework for graph databases. VLDB J. 26, 347–372 (2017)
Yi, P., Choi, B., Zhang, Z., Bhowmick, S.S., Xu, J.: Gfocus: user focus-based graph query autocompletion (2018). https://goo.gl/MYYw94
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Li, G., Ng, N., Yi, P., Zhang, Z., Choi, B. (2018). Answering the Why-Not Questions of Graph Query Autocompletion. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_22
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DOI: https://doi.org/10.1007/978-3-319-91452-7_22
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