Efficient processing of keyword queries over graph databases for finding effective answers

KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources

“…Using visualisations, especially of graphs/ontologies as an output of retrieval systems has also been proposed, mainly in QA and NLIDB that are based on knowledge graphs [2,13,23].…”

Keyword Search over Graph-structured Data for Finding Effective and Non-redundant Answers

“…Most previous approaches to keyword search on graph data find minimal sub-trees containing query keywords based on either Steiner-tree semantics [1,4,6] or distinct-root semantics [2,3,5,10]. Under distinct-root semantics, sub-trees returned as query answers should be rooted at a distinct node.…”

“…For efficient search of a large graph data, [5] suggests creating and utilizing a multi-granular representation of graph data, and presents search algorithms on a multigranular graph extended from BANKS [1] and Bi-directional Search. A recent study in [10] has proposed an extended answer structure with a new relevance measure and proposed an indexing and query processing scheme similar to BLINKS to produce effective and various top-k answers.…”

“…As the amount of data increases rapidly, an efficient and effective query system is much needed. Keyword search has been attracting a lot of attention since it allows users to express their information need using simple keywords [1][2][3][4][5][6][7][8][9][10].…”

“…There have been proposed several approaches based on the distinct root semantics, where the relevance of a sub-tree is computed as a function of the shortest paths from the root to the nodes containing query keywords. For each node in the graph, they choose at most one sub-tree rooted at the node as a candidate answer to the query [2,3,5,10]. By reducing the number of candidates significantly, they can process top-k query over a large volume of data more efficiently than other approaches.…”