Dealing with Plethoric Answers of SPARQL Queries

Abstract

When querying Knowledge Bases (KBs), users are faced with large sets of data, often without knowing their underlying structures. It follows that users may make mistakes when formulating their queries, therefore receiving an unhelpful response. In this paper, we address the plethoric answers problem, the situation where a query produces significantly more results than the user was expecting. We deal with this problem by identifying the parts of the failing query, called Minimal Failure Inducing Subqueries (MFIS), that cause plethoric answers. As long as the query contains an MFIS, it will fail to reach a sufficiently low amount of answers. Thanks to these MFIS, interactive and automatic approaches can be set up to help the user reformulate their query. The dual notion of MFIS, maXimal Succeeding Subqueries (XSS), is also useful. They are queries with the most parts of the original query that return non plethoric answers. Our goal is to compute MFIS and XSS efficiently, so that they may be used to solve the plethoric answers problem. We propose two algorithms that leverage query and data properties to compute MFIS and XSS. We show experimentally that our algorithms clearly outperform a baseline method on generated queries as well as real user-submitted queries.

Explaining Unexpected Answers of SPARQL Queries

“…Minimal Failing Subqueries (MFS) have then been used to describe the smallest parts of a query that lead to empty answers [9,7,5]. A related notion, Minimal Failure Inducing Subquery, is used for the why so many problem [20]. Failure causes have also been used in the why not problem in relational databases [1], and KBs [26] to identify the triple pattern or SPARQL operator responsible for an absent answer.…”

“…Our aim is to identify the parts of a query responsible for its failure. Building on the Minimal Failing Subquery (MFS) and maXimal Succeeding Subqueries (XSS) introduced in the why empty problem, the following definitions have been proposed to deal with the why so many answers problem [20]. Definition 1.…”

“…Pruning Properties To reduce the number of query executions, some query properties can be leveraged. A general property can be applied to all problems [20]:…”

“…Using this property, subqueries of XSS do not need to be studied, so we start by checking that the query does not have a succeeding query, and only study its failure if that is the case. The why so many study also introduces properties that use the failure of a query to determine the failure of another query [20]. We have adapted these to be useful to any cardinality based problem.…”

A cooperative treatment of the plethoric answers problem in RDF

“…Drawing on previous work on the empty-answers problem in KBs (Fokou et al, 2015), we have introduced the basis of a cooperative method to deal with the plethoric answers problem in a previous paper (Parkin et al, 2021). In this paper we provide an extended presentation of our method with the following contributions.…”