A cooperative treatment of the plethoric answers problem in RDF

When querying Knowledge Bases, users are faced with large sets of data, often without knowing their underlying structures. It follows that may make mistakes when formulating queries, therefore receiving an unhelpful response. In this paper, we address the plethoric answers problem, situation where user query produces significantly more results than was expecting. The common approach to solving i.e. top-K approach, reduces query’s result size by applying various criteria select only some answers. This selection is performed considering causes producing answers, and can miss issue within query. We deal problem proposing identifies parts failing query, called Minimal Failure Inducing Subqueries (MFIS), cause As long as contains MFIS, it will fail reach a sufficiently low amount Thus, thanks these interactive automatic approaches be set up help in reformulating dual notion Maximal Succeeding (XSS), also useful. They provide queries maximal number original return non-plethoric Our goal compute MFIS XSS efficiently, so they used solve problem. show computing information $$\texttt {NP}$$ -hard baseline exhaustive search method cannot for most queries. propose two algorithms leverage properties data efficiently reasonable size. experimentally our clearly outperform on generated well real user-submitted