Spec-QP: Speculative Query Planning for Joins over Knowledge Graphs

TL;DR

Spec-QP introduces a speculative query planning method for knowledge graph queries that efficiently identifies relevant relaxations, reducing computation and response times while maintaining result quality.

Contribution

The paper presents a novel speculative query planning framework for knowledge graph query relaxations, improving efficiency by focusing only on relaxations likely to impact top-k results.

Findings

Significantly reduces query processing time.

Maintains high accuracy in top-k results.

Effective on datasets like XKG and Twitter.

Abstract

Organisations store huge amounts of data from multiple heterogeneous sources in the form of Knowledge Graphs (KGs). One of the ways to query these KGs is to use SPARQL queries over a database engine. Since SPARQL follows exact match semantics, the queries may return too few or no results. Recent works have proposed query relaxation where the query engine judiciously replaces a query predicate with similar predicates using weighted relaxation rules mined from the KG. The space of possible relaxations is potentially too large to fully explore and users are typically interested in only top-k results, so such query engines use top-k algorithms for query processing. However, they may still process all the relaxations, many of whose answers do not contribute towards top-k answers. This leads to computation overheads and delayed response times. We propose Spec-QP, a query planning framework that speculatively determines which relaxations will have their results in the top-k answers. Only these relaxations are processed using the top-k operators. We, therefore, reduce the computation overheads and achieve faster response times without adversely affecting the quality of results. We tested Spec-QP over two datasets - XKG and Twitter, to demonstrate the efficiency of our planning framework at reducing runtimes with reasonable accuracy for query engines supporting relaxations.