1 research outputs found
Relaxing Relationship Queries on Graph Data
In many domains we have witnessed the need to search a large entity-relation
graph for direct and indirect relationships between a set of entities specified
in a query. A search result, called a semantic association (SA), is typically a
compact (e.g., diameter-constrained) connected subgraph containing all the
query entities. For this problem of SA search, efficient algorithms exist but
will return empty results if some query entities are distant in the graph. To
reduce the occurrence of failing query and provide alternative results, we
study the problem of query relaxation in the context of SA search. Simply
relaxing the compactness constraint will sacrifice the compactness of an SA,
and more importantly, may lead to performance issues and be impracticable.
Instead, we focus on removing the smallest number of entities from the original
failing query, to form a maximum successful sub-query which minimizes the loss
of result quality caused by relaxation. We prove that verifying the success of
a sub-query turns into finding an entity (called a certificate) that satisfies
a distance-based condition about the query entities. To efficiently find a
certificate of the success of a maximum sub-query, we propose a best-first
search algorithm that leverages distance-based estimation to effectively prune
the search space. We further improve its performance by adding two fine-grained
heuristics: one based on degree and the other based on distance. Extensive
experiments over popular RDF datasets demonstrate the efficiency of our
algorithm, which is more scalable than baselines.Comment: 16 pages, accepted to JoW