Efficient Answering of Why-Not Questions in Similar Graph Matching

Abstract

Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph q and a set of missing (why-not) graphs, we aim to modify q into a new query graph q * such that the missing graphs are included in the new answer set of q * . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for q * . Then, we present a two-phase algorithm to find the new query q * . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query q within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for q * . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.

Big-Graphs: Querying, Mining, and Beyond

“…(5) visual query [16,52], (6) natural language questions [119,28], (7) incorporating users' feedback [19,134], (8) query auto-completion and recommendation [77], (9) answers explanation [131,150,59], (10) conversational QA [176], etc. A one-time answer might not be satisfactory.…”

“…Neural approaches are increasingly becoming popular for these tasks. (6) Interactive methods include (a) graph query suggestion, expansion, refinement, and autocompletion aiming to retrieve more detailed and relevant answers [77]; (b) a user's time-bounded search to provide 'early' answers within the user's response time bound and incrementally improving the quality of answers with time [155]; (c) incorporating a user's feedback for personalized graph querying [19,134]; (d) answer explanation to support 'why', 'why-not', 'why empty', and 'why so many' questions on query results [131,150,59].…”