Preference-based inconsistency-tolerant query answering under existential rules

Greco, Sergio; Molinaro, Cristian; Trubitsyna, Irina

doi:10.1016/j.artint.2022.103772

Cited by 11 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since explaining query answering has recently drawn considerably attention under existential rule languages (e.g., see [10,11,12,13,14,15]), and knowledge representation in general (e.g., in the context of argumentation [16]) an interesting direction for future work is to address such issue in our setting. Also, it would be interesting to account for user preferences when answering queries, as recently done in [17,18], possibly considering other ways of expressing preferences, e.g. by means of CP-nets [19,20].…”

Section: Next Stepsmentioning

confidence: 99%

Querying Data Exchange Settings Beyond Positive Queries

Greco¹,

Molinaro²,

Trubitsyna³

2023

Theory and Practice of Logic Programming

Self Cite

View full text Add to dashboard Cite

Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early work, but little attention has been paid to more general queries. A few proposals of semantics for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings, for example, with weakly-acyclic dependencies. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is $\text{co}\text{NP}\text{-complete}$ when the dependencies are weakly-acyclic. Moreover, in the latter case, we show that exact answers under our semantics can be computed by means of logic programs with choice, thus exploiting existing efficient systems. For more efficient computations, we also show that our semantics allows for the construction of a representative target instance, similar in spirit to a universal solution, that can be exploited for computing approximate answers in polynomial time.

show abstract

Section: Next Stepsmentioning

confidence: 99%

Querying Data Exchange Settings Beyond Positive Queries

Greco¹,

Molinaro²,

Trubitsyna³

2023

Theory and Practice of Logic Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…Manual intervention can often solve these problems, but it is timeconsuming, and thus not scalable with respect to the amount of data nowadays available. The activities of data cleaning (possibly to be placed or repeated after data integration) include data transformation, data reduction, deduplication, error detection, missing value imputation, and space transformations in the case of multimedia data [5][6] [7]. The overall task is complicated also by the fact that optimizing one dimension of data quality might cause a quality loss for another dimension.…”

Section: Data Science Pipeline Figure 3 Reports a Typical Data Scienc...mentioning

confidence: 99%

The MICS Project: A Data Science Pipeline for Industry 4.0 Applications

Cristaldi,

Esmaili,

Gruosso

et al. 2023

2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAI

View full text Add to dashboard Cite

The goal of the MICS (Made in Italy, Circular and Sustainable) project is to develop new models and techniques to support the entire pipeline for applying Data Science algorithms to data from industrial processes. Although many libraries and tools are already available to aid the analysis of data, we believe that each different application domain requires individual study to propose appropriate methods and tools that accommodate the specific peculiarities of its data. In this position paper, we discuss the following points by also outlining our case studies.

show abstract

“…The Datalog ± languages here considered guaranteeing decidability are among the most frequently analyzed in the literature, namely, linear (L) (Calì, Gottlob, and Lukasiewicz 2012), guarded (G) (Calì, Gottlob, and Kifer 2013), sticky (S) (Calì, Gottlob, and Pieris 2012), and acyclic TGDs (A), the "weak" generalizations weakly sticky (WS) (Calì, Gottlob, and Pieris 2012) and weakly acyclic TGDs (WA) (Fagin et al 2005), their "full" (i.e., existential-free) restrictions linear full (LF), guarded full (GF), sticky full (SF), and acyclic full TGDs (AF), respectively, and full TGDs (F) in general. We refer to (Calautti et al 2022; for a more detailed overview.…”

Section: Preliminariesmentioning

confidence: 99%

“…These notions of optimal repairs have been recently applied to DLs by Bienvenu and Bourgaux (2020;, who studied the data complexity of query entailment under the AR, IAR, and brave semantics based on optimal repairs, existence of a unique optimal repair, and enumeration of all optimal repairs. The data and combined complexity of the AR, IAR, and ICR semantics for existential rules when preferences are expressed via so-called preference rules have been investigated by Calautti et al (2022). The crucial difference between the body of work above and the current paper is that the former considers subsetmaximal repairs only (where preferred ones are identified on the basis of user preferences), while we consider different notions of maximality to define repairs in the first place.…”

mentioning

confidence: 99%

Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Preferred Repairs

Lukasiewicz

Malizia

Molinaro

2023

Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning

View full text Add to dashboard Cite

Inconsistency-tolerant semantics have been proposed to provide meaningful ontological query answers even in the presence of inconsistencies. Several such semantics rely on the notion of a repair, which is a "maximal" consistent subset of the database, where different maximality criteria might be adopted depending on the application at hand. Previous work in the context of Datalog+/- has considered only the subset and cardinality maximality criteria. We take here a step further and study inconsistency-tolerant semantics under maximality criteria based on weights and priority levels. We provide a thorough complexity analysis for a wide range of existential rule languages and for several complexity measures.

show abstract

Preference-based inconsistency-tolerant query answering under existential rules

Cited by 11 publications

References 57 publications

Querying Data Exchange Settings Beyond Positive Queries

Querying Data Exchange Settings Beyond Positive Queries

The MICS Project: A Data Science Pipeline for Industry 4.0 Applications

Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Preferred Repairs

Contact Info

Product

Resources

About