Reasoning about Explanations for Negative Query Answers in DL-Lite

Calvanese, Diego; Ortiz, Magdalena; Šimkus, Mantas; Stefanoni, Giorgio

doi:10.1613/jair.3870

Cited by 43 publications

(51 citation statements)

References 25 publications

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“…The problems of providing why explanations and why-not explanations have also been investigated in the context of OBDA in [11] and [13], respectively. The why-not explanations of [13] follow the data-centric approach to why-not provenance as we discussed earlier where their goal is to modify the assertions that describe the extensions of concepts in the ontology so that the missing tuple will appear in the query result.…”

Section: More Related Workmentioning

confidence: 99%

High-Level Why-Not Explanations using Ontologies

Cate

Civili

Sherkhonov

et al. 2015

Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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We propose a novel foundational framework for why-not explanations, that is, explanations for why a tuple is missing from a query result. Our why-not explanations leverage concepts from an ontology to provide high-level and meaningful reasons for why a tuple is missing from the result of a query.A key algorithmic problem in our framework is that of computing a most-general explanation for a why-not question, relative to an ontology, which can either be provided by the user, or it may be automatically derived from the data and/or schema. We study the complexity of this problem and associated problems, and present concrete algorithms for computing why-not explanations. In the case where an external ontology is provided, we first show that the problem of deciding the existence of an explanation to a why-not question is NP-complete in general. However, the problem is solvable in polynomial time for queries of bounded arity, provided that the ontology is specified in a suitable language, such as a member of the DL-Lite family of description logics, which allows for efficient concept subsumption checking. Furthermore, we show that a most-general explanation can be computed in polynomial time in this case. In addition, we propose a method for deriving a suitable (virtual) ontology from a database and/or a schema, and we present an algorithm for computing a most-general explanation to a why-not question, relative to such ontologies. This algorithm runs in polynomial-time in the case when concepts are defined in a selection-free language, or if the underlying schema is fixed. Finally, we also study the problem of computing short most-general explanations, and we briefly discuss alternative definitions of what it means to be an explanation, and to be most general.

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Section: More Related Workmentioning

confidence: 99%

High-Level Why-Not Explanations using Ontologies

Cate

Civili

Sherkhonov

et al. 2015

Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

View full text Add to dashboard Cite

show abstract

“…On the other hand, however, the implementation of existing OWL reasoners does not support such an explanation mechanism even through tracing the execution of query answering. For this reason, extensive efforts have been devoted to equip ontology-based systems with various explanation facilities [22,23,4,15,24,6]. While early research was mainly on explaining derived answers (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

“…positive answers, like "Tom" in the above example), explaining missing answers (a.k.a. negative answers, like "Mary") has attracted much attention lately [3,6]. Such an explanation facility is essential to understand why the ontology-based system fails to derive certain desired answers, and to enrich incomplete data according to user observations.…”

Section: Introductionmentioning

confidence: 99%

“…First, while ontology-based reasoning empowers query answering, it also complicates query abduction. The computational complexity of query abduction is indeed higher than query answering [6]. Also, efficiency of abduction implementation has not been a central concern in AI but it is essential for ontology-based systems, and it is highly non-trivial to develop a scalable algorithm for query abduction.…”

Section: Introductionmentioning

confidence: 99%

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Towards Scalable and Complete Query Explanation with OWL 2 EL Ontologies

Wang

Chitsaz

Wang

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Ontology-mediated data access and management systems are rapidly emerging. Besides standard query answering, there is also a need for such systems to be coupled with explanation facilities, in particular to explain missing query answers (i.e. desired answers of a query which are not derivable from the given ontology and data). This support is highly demanded for debugging and maintenance of big data, and both theoretical results and algorithms proposed. However, existing query explanation algorithms either cannot scale over relative large data sets or are not guaranteed to compute all desired explanations. To the best of our knowledge, no existing algorithm can efficiently and completely explain conjunctive queries (CQs) w.r.t. ELH ⊥ ontologies. In this paper, we present a hybrid approach to achieve this. An implementation of the proposed query explanation algorithm has been developed using an off-the-shelf Prolog engine and a datalog engine. Finally, the system is evaluated over practical ontologies. Experimental results show that our system scales over large data sets.

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“…[1] emphasizes abduction as a feature of support tools for ontology quality control. [3] applies abduction to explain why a tuple is not an answer to a conjunctive query. All of these approaches, just like [7] and [8], focus on reasoning over the ABox.…”

Section: A Bird's Eye On Abductionmentioning

confidence: 99%

Abduction for Learning Smart City Rules

Bjørner¹,

Marinescu²,

Sagiv³

EPiC Series in Computing

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We propose using abduction for inferring implicit rules for Smart City ontologies. We show how we can use Z3 to extract candidate abducers from partial ontologies and leverage them in an iterative process of evolving an ontology by refining relations and restrictions, and populating relations. Our starting point is a Smart City initiative of the city of Barcelona, where a substantial ontology is being developed to support processes such as city planning, social services, or improving the quality of the data concerning (for instance) legal entities, whose incompleteness may sometimes hide fraudulent behavior. In our scenario we are supporting semantic queries over heterogeneous and noisy data. The approach we develop would allow evolving ontologies in an iterative fashion as new relations and restrictions are discovered.

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Reasoning about Explanations for Negative Query Answers in DL-Lite

Cited by 43 publications

References 25 publications

High-Level Why-Not Explanations using Ontologies

High-Level Why-Not Explanations using Ontologies

Towards Scalable and Complete Query Explanation with OWL 2 EL Ontologies

Abduction for Learning Smart City Rules

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