Optimizing query rewriting in ontology-based data access

Pinto, Floriana Di; Lembo, Domenico; Lenzerini, Maurizio; Mancini, Riccardo; Poggi, Antonella; Rosati, Riccardo; Ruzzi, Marco; Savo, Domenico Fabio

doi:10.1145/2452376.2452441

Cited by 36 publications

(33 citation statements)

References 16 publications

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“…Obviously, unfolding might not be the most efficient strategy, and therefore, more sophisticated techniques for mapping rewriting have been studied (See, for example,[9]). …”

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confidence: 99%

Inconsistency-tolerant query answering in ontology-based data access

Lembo

Lenzerini

Rosati

et al. 2015

Journal of Web Semantics

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a b s t r a c tOntology-based data access (OBDA) is receiving great attention as a new paradigm for managing information systems through semantic technologies. According to this paradigm, a Description Logic ontology provides an abstract and formal representation of the domain of interest to the information system, and is used as a sophisticated schema for accessing the data and formulating queries over them. In this paper, we address the problem of dealing with inconsistencies in OBDA. Our general goal is both to study DL semantical frameworks that are inconsistency-tolerant, and to devise techniques for answering unions of conjunctive queries under such inconsistency-tolerant semantics. Our work is inspired by the approaches to consistent query answering in databases, which are based on the idea of living with inconsistencies in the database, but trying to obtain only consistent information during query answering, by relying on the notion of database repair. We first adapt the notion of database repair to our context, and show that, according to such a notion, inconsistency-tolerant query answering is intractable, even for very simple DLs. Therefore, we propose a different repair-based semantics, with the goal of reaching a good compromise between the expressive power of the semantics and the computational complexity of inconsistency-tolerant query answering. Indeed, we show that query answering under the new semantics is first-order rewritable in OBDA, even if the ontology is expressed in one of the most expressive members of the DL-Lite family.

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“…Obviously, unfolding might not be the most efficient strategy, and therefore, more sophisticated techniques for mapping rewriting have been studied (See, for example,[9]). …”

mentioning

confidence: 99%

Inconsistency-tolerant query answering in ontology-based data access

Lembo

Lenzerini

Rosati

et al. 2015

Journal of Web Semantics

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“…However, query rewriting and answering approaches to OBDA enforce limitations for expressiveness of the ontology language used [18]. Although there exist methods for approximating ontologies, i.e., moving OWL 2 ontologies to less expressive ontology languages [19], [20], we do not consider them due to the risk of losing model properties and quality of data, and thereby contradicting the goals of our work.…”

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confidence: 98%

Extending statistical data quality improvement with explicit domain models

Solomakhina

Hubauer

Lamparter

et al. 2014

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

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Automatic processing of data for the purpose of determining operating states and identifying faults has become essential for many modern industrial systems. Typical sources of this data include hundreds of sensors mounted at the industrial machinery measuring qualities such as temperature, vibration, pressure, and many more. However, sensors are complex technical devices, which means that they can fail and their readings may contain noise or imprecise values. Such low quality data makes it hard to solve the original task of assessing system and process status. We present an approach which brings together several well-known techniques from computer science and statistics and enhances monitoring of technical systems by improving results of detection and correction of data quality issues in sensor data. The application domain and the dependencies between its objects are represented as a knowledge-based model, while statistics identifies data anomalies, such as outlying or missing values, in sensor measurement data. Combining information from the knowledge-based model and statistical computations allows to validate and improve data analysis results. We demonstrate the proposed approach on a real-world industrial use case from the power generation domain. Our evaluation shows that the combined solution improves precision indexes while maintaining high accuracy and recall values.

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“…On the other hand, it may perform several kinds of transformations of the ontology and the mappings: for instance, it should transform an input ontology which is in a language not supported by the OBDA systems (e.g., OWL 2) and return a version of the ontology into the supported language (e.g., OWL 2 QL profile). Also, it may perform transformations of the mappings related to formal properties of the mappings or to optimisation strategies [24]. This may involve changes in the syntax and/or semantics of the ontology.…”

Section: Ontology and Mapping Management Componentmentioning

confidence: 99%