41 research outputs found
Epistemic Reasoning in OWL 2 DL
We extend the description logic SROIQ (OWL 2 DL) with the epistemic operator K and argue that unintended effects occur when imposing the semantics traditionally employed. Consequently, we identify the most expressive DL for which the traditional approach can still be adapted. For the epistemic extension of SROIQ and alike expressive DLs, we suggest a revised semantics that behaves more intuitively in these cases and coincides with the traditional semantics on less expressive DLs
Epistemic querying of OWL knowledge bases
Abstract. Epistemic querying extends standard ontology inferencing by allowing for deductive introspection. We propose a technique for epistemic querying of OWL 2 ontologies not featuring nominals and universal roles by a reduction to a series of standard OWL 2 reasoning steps thereby enabling the deployment of off-the-shelf OWL 2 reasoning tools for this task. We prove formal correctness of our method, justify the omission of nominals and universal role, and provide an implementation as well as evaluation results
Real Time Reasoning in OWL2 for GDPR Compliance
This paper shows how knowledge representation and reasoning techniques can be
used to support organizations in complying with the GDPR, that is, the new
European data protection regulation. This work is carried out in a European
H2020 project called SPECIAL. Data usage policies, the consent of data
subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2
called PL (policy language); compliance checking and policy validation are
reduced to subsumption checking and concept consistency checking. This work
proposes a satisfactory tradeoff between the expressiveness requirements on PL
posed by the GDPR, and the scalability requirements that arise from the use
cases provided by SPECIAL's industrial partners. Real-time compliance checking
is achieved by means of a specialized reasoner, called PLR, that leverages
knowledge compilation and structural subsumption techniques. The performance of
a prototype implementation of PLR is analyzed through systematic experiments,
and compared with the performance of other important reasoners. Moreover, we
show how PL and PLR can be extended to support richer ontologies, by means of
import-by-query techniques. PL and its integration with OWL2's profiles
constitute new tractable fragments of OWL2. We prove also some negative
results, concerning the intractability of unrestricted reasoning in PL, and the
limitations posed on ontology import
From Horn-SRIQ to Datalog: A Data-Independent Transformation that Preserves Assertion Entailment: Extended Version
Ontology-based access to large data-sets has recently gained a lot of attention. To access data e_ciently, one approach is to rewrite the ontology into Datalog, and then use powerful Datalog engines to compute implicit entailments. Existing rewriting techniques support Description Logics (DLs) from ELH to Horn-SHIQ. We go one step further and present one such data-independent rewriting technique for Horn-SRIQ⊓, the extension of Horn-SHIQ that supports role chain axioms, an expressive feature prominently used in many real-world ontologies. We evaluated our rewriting technique on a large known corpus of ontologies. Our experiments show that the resulting rewritings are of moderate size, and that our approach is more efficient than state-of-the-art DL reasoners when reasoning with data-intensive ontologies.This is an extended version of the article to appear in the proceedings of AAAI 2019
Epistemic querying of OWL knowledge bases
Abstract. Epistemic querying extends standard ontology inferencing by allowing for deductive introspection. We propose a technique for epistemic querying of OWL 2 ontologies not featuring nominals and universal roles by a reduction to a series of standard OWL 2 reasoning steps thereby enabling the deployment of off-the-shelf OWL 2 reasoning tools for this task. We prove formal correctness of our method, justify the omission of nominals and universal role, and provide an implementation as well as evaluation results
Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?
Extracting a subset of a given OWL ontology that captures all the ontology's
knowledge about a specified set of terms is a well-understood task. This task
can be based, for instance, on locality-based modules (LBMs). These come in two
flavours, syntactic and semantic, and a syntactic LBM is known to contain the
corresponding semantic LBM. For syntactic LBMs, polynomial extraction
algorithms are known, implemented in the OWL API, and being used. In contrast,
extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL,
and these algorithms had not been implemented yet for expressive ontology
languages. We present the first implementation of semantic LBMs and report on
experiments that compare them with syntactic LBMs extracted from real-life
ontologies. Our study reveals whether semantic LBMs are worth the additional
extraction effort, compared with syntactic LBMs