Handling qualitative preferences in SPARQL over virtual ontology-based data access

Abstract

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

A semantics-enabled approach for personalised Data Lake exploration

“…In the literature, binary preferences have been studied in recommender systems under a twofold perspective: quantitative [33][34][35], relying upon a scoring function to determine a total order of results; qualitative [13,36,37], using binary relations to express a (strict) partial order of results. In the scope of this paper, we focused on qualitative preferences, yielding a higher expressiveness with respect to quantitative ones.…”

“…The work in [35] is a quantitative approach which tackles the problem of Table Union Search (TUS) in the presence of preferences, used for table unionability, as a way to reduce the search space and focus on rows and columns that are important for the follow-up operations. Authors in [37] propose a framework implementing a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. Instead, the approach in [34] implements sorting methods to dynamically query and visualise the relatively more important transportation stations within the users' visible range.…”

“…The approach in [34] employs preferences for data exploration, despite users' profiles are not exploited to harness exploration contexts for the different categories of users. Instead, other approaches, even though mentioning the usage of preference-based systems, disregard user-related exploration issues [35,37] and do not focus on multi-dimensional data exploration. Binary preferences are used in PERSEUS as they pose as intuitive constructs, which are directly specified by users at exploration time.…”

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

“…The skyline ranking is exactly the most satisfactory set E 1 , the most preferred outcomes, given by the weak comparative preferences approach described in Section 6.2, which can be obtained in polynomial complexity. Authors in Goncalves et al (2022) have also proposed an approach based on skylines, where SPARQL qualitative preference queries are translated directly into queries that can be processed by a relational database management system. The authors of Abidi et al (2018) introduce a possibilistic variant of the Skylines concept.…”

Using Domain Knowledge to Rank SPARQL Query Results According to User Preferences

“…In both cases, preferences are addressed by the form SELECT ?X WHERE {P1 PREFERRING P2}. [7] presents a framework called Morph-Skyline++ which processes SPARQL qualitative preferences by converting them into SQL queries to be evaluated by a relational database management system. One of the limitations of the qualitative approach is that it does not allow multiple preferences to be handled at the same time.…”