Keyword Search on RDF Graphs - A Query Graph Assembly Approach

TL;DR

This paper presents a heuristic approach for assembling query graphs from keywords for RDF data, addressing the NP-complete QGA problem with an efficient algorithm that scales well in large graphs.

Contribution

It introduces a novel heuristic algorithm for the NP-complete query graph assembly problem in RDF keyword search, ensuring scalability and effectiveness.

Findings

The proposed algorithm outperforms existing methods in effectiveness.

It maintains efficiency even with large RDF graphs.

Experiments confirm the system's scalability and accuracy.

Abstract

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is $O(k^{2l} \cdot l^{3l})$, where $l$ is the number of the keywords and $k$ is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both $l$ and $k$ are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency.