Tractable query answering and rewriting under description logic constraints

Perez-Urbina, Hector; Motik, Boris; Horrocks, Ian

doi:10.1016/j.jal.2009.09.004

Cited by 105 publications

(124 citation statements)

References 22 publications

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“…Our implementation uses the system REQUIEM [13] for the computation of TBox rewritings. We evaluated our approach using the following two well-known ontologies.…”

Section: Discussionmentioning

confidence: 99%

“…Depending on the language that T is expressed in, systems such as REQUIEM [13] and KAON2 [5] can compute a (possibly disjunctive) datalog rewriting; now whenever the rewriting is a datalog program, each datalog rule in the rewriting can always be 'rolled-up' into an OWL 2 RL axiom. Therefore, in order to simplify the presentation, we consider such rewritings to be OWL 2 RL TBoxes rather than datalog programs.…”

Section: Repairing a Class Of Algorithms Complete For Owl 2 Rlmentioning

confidence: 99%

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Repairing Ontologies for Incomplete Reasoners

Stoilos

Grau

Motik

et al. 2011

The Semantic Web – ISWC 2011

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Abstract. The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of 'repairing' an ontology T -that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not significantly affect reasoner performance.

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“…Our implementation uses the system REQUIEM [13] for the computation of TBox rewritings. We evaluated our approach using the following two well-known ontologies.…”

Section: Discussionmentioning

confidence: 99%

Section: Repairing a Class Of Algorithms Complete For Owl 2 Rlmentioning

confidence: 99%

Repairing Ontologies for Incomplete Reasoners

Stoilos

Grau

Motik

et al. 2011

The Semantic Web – ISWC 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…OWL-QL is a lightweight language that sacrifices some expressiveness in comparison with OWL 2. Here, query-answering is based on REQUIEM (REsolution-based QUery rewrIting for Expressive Models) [26], a prototypical implementation of a query rewriting algorithm [27]. …”

Section: Owl-ql Query and Anwsermentioning

confidence: 99%

Ontology-Guided Image Interpretation for GEOBIA of High Spatial Resolution Remote Sense Imagery: A Coastal Area Case Study

Huang

Chen

et al. 2017

IJGI

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Image interpretation is a major topic in the remote sensing community. With the increasing acquisition of high spatial resolution (HSR) remotely sensed images, incorporating geographic object-based image analysis (GEOBIA) is becoming an important sub-discipline for improving remote sensing applications. The idea of integrating the human ability to understand images inspires research related to introducing expert knowledge into image object-based interpretation. The relevant work involved three parts: (1) identification and formalization of domain knowledge; (2) image segmentation and feature extraction; and (3) matching image objects with geographic concepts. This paper presents a novel way that combines multi-scaled segmented image objects with geographic concepts to express context in an ontology-guided image interpretation. Spectral features and geometric features of a single object are extracted after segmentation and topological relationships are also used in the interpretation. Web ontology language-query language (OWL-QL) formalize domain knowledge. Then the interpretation matching procedure is implemented by the OWL-QL query-answering. Compared with a supervised classification, which does not consider context, the proposed method validates two HSR images of coastal areas in China. Both the number of interpreted classes increased (19 classes over 10 classes in Case 1 and 12 classes over seven in Case 2), and the overall accuracy improved (0.77 over 0.55 in Case 1 and 0.86 over 0.65 in Case 2). The additional context of the image objects improved accuracy during image classification. The proposed approach shows the pivotal role of ontology for knowledge-guided interpretation.

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“…Hydrowl accomplishes this by combining ans with a second system ans which can explicate such type of information. One such family of systems are query rewriting systems which take as input a (possibly non-SPARQL) query Q and a TBox T and compute a so-called query rewriting Rew [13][14][15]. Roughly speaking, a rewriting Rew for Q, T consists of two parts, a set of datalog rules Rew D which captures ground entailments of T and a union of conjunctive queries Rew Q which captures all inferences related to non-ground entailments.…”

Section: Techniques Used In Hydrowlmentioning

confidence: 99%

Hydrowl: A Hybrid Query Answering System for OWL 2 DL Ontologies

Stoilos

2014

Web Reasoning and Rule Systems

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Abstract. This system description paper introduces the OWL 2 query answering system Hydrowl. Hydrowl is based on novel hybrid techniques which in order to compute the query answers combine at run-time a reasoner ans1 supporting a (tractable) fragment of OWL 2 (e.g., OWL 2 QL and OWL 2 RL) with a fully-fledged OWL 2 DL reasoner ans2. The motivation is that if most of the (query answering) work is delegated to the (usually) very scalable system ans1 while the interaction with ans2 is kept to a bare minimum, then we can possibly provide with scalable query answering even over expressive fragments of OWL 2 DL. We discuss the system's architecture and we present an overview of the techniques used. Finally, we present some first encouraging experimental results.

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Tractable query answering and rewriting under description logic constraints

Cited by 105 publications

References 22 publications

Repairing Ontologies for Incomplete Reasoners

Repairing Ontologies for Incomplete Reasoners

Ontology-Guided Image Interpretation for GEOBIA of High Spatial Resolution Remote Sense Imagery: A Coastal Area Case Study

Hydrowl: A Hybrid Query Answering System for OWL 2 DL Ontologies

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