Improving the recall of decentralised linked data querying through implicit knowledge

TL;DR

This paper explores how implicit knowledge, such as owl:sameAs and RDFS reasoning, can enhance recall in decentralized Linked Data querying, especially when relying on dereferenced documents and link traversal.

Contribution

It introduces methods to incorporate owl:sameAs and lightweight RDFS reasoning to improve recall in decentralized Linked Data query processing.

Findings

Considering rdfs:seeAlso links improves recall.

owl:sameAs links further increase recall.

Applying lightweight RDFS reasoning yields additional results.

Abstract

Aside from crawling, indexing, and querying RDF data centrally, Linked Data principles allow for processing SPARQL queries on-the-fly by dereferencing URIs. Proposed link-traversal query approaches for Linked Data have the benefits of up-to-date results and decentralised (i.e., client-side) execution, but operate on incomplete knowledge available in dereferenced documents, thus affecting recall. In this paper, we investigate how implicit knowledge - specifically that found through owl:sameAs and RDFS reasoning - can improve the recall in this setting. We start with an empirical analysis of a large crawl featuring 4 m Linked Data sources and 1.1 g quadruples: we (1) measure expected recall by only considering dereferenceable information, (2) measure the improvement in recall given by considering rdfs:seeAlso links as previous proposals did. We further propose and measure the impact of additionally considering (3) owl:sameAs links, and (4) applying lightweight RDFS reasoning (specifically {\rho}DF) for finding more results, relying on static schema information. We evaluate our methods for live queries over our crawl.