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Description Logics (DLs) are increasingly successful knowledge representation formalisms, useful for any application requiring implicit derivation of knowledge from explicitly known facts. A prominent example domain benefiting from these formalisms since the 1990s is the biomedical field. This area contributes an intangible amount of facts and relations between low- and high-level concepts such as the constitution of cells or interactions between studied illnesses, their symptoms and remedies. DLs are well-suited for handling large formal knowledge repositories and computing inferable coherences throughout such data, relying on their well-founded first-order semantics. In particular, DLs of reduced expressivity have proven a tremendous worth for handling large ontologies due to their computational tractability. In spite of these assets and prevailing influence, classical DLs are not well-suited to adequately model some of the most intuitive forms of reasoning. The capability for abductive reasoning is imperative for any field subjected to incomplete knowledge and the motivation to complete it with typical expectations. When such default expectations receive contradicting evidence, an abductive formalism is able to retract previously drawn, conflicting conclusions. Common examples often include human reasoning or a default characterisation of properties in biology, such as the normal arrangement of organs in the human body. Treatment of such defeasible knowledge must be aware of exceptional cases - such as a human suffering from the congenital condition situs inversus - and therefore accommodate for the ability to retract defeasible conclusions in a non-monotonic fashion. Specifically tailored non-monotonic semantics have been continuously investigated for DLs in the past 30 years. A particularly promising approach, is rooted in the research by Kraus, Lehmann and Magidor for preferential (propositional) logics and Rational Closure (RC). The biggest advantages of RC are its well-behaviour in terms of formal inference postulates and the efficient computation of defeasible entailments, by relying on a tractable reduction to classical reasoning in the underlying formalism. A major contribution of this work is a reorganisation of the core of this reasoning method, into an abstract framework formalisation. This framework is then easily instantiated to provide the reduction method for RC in DLs as well as more advanced closure operators, such as Relevant or Lexicographic Closure. In spite of their practical aptitude, we discovered that all reduction approaches fail to provide any defeasible conclusions for elements that only occur in the relational neighbourhood of the inspected elements. More explicitly, a distinguishing advantage of DLs over propositional logic is the capability to model binary relations and describe aspects of a related concept in terms of existential and universal quantification. Previous approaches to RC (and more advanced closures) are not able to derive typical behaviour for the concepts that occur within such quantification. The main contribution of this work is to introduce stronger semantics for the lightweight DL EL_bot with the capability to infer the expected entailments, while maintaining a close relation to the reduction method. We achieve this by introducing a new kind of first-order interpretation that allocates defeasible information on its elements directly. This allows to compare the level of typicality of such interpretations in terms of defeasible information satisfied at elements in the relational neighbourhood. A typicality preference relation then provides the means to single out those sets of models with maximal typicality. Based on this notion, we introduce two types of nested rational semantics, a sceptical and a selective variant, each capable of deriving the missing entailments under RC for arbitrarily nested quantified concepts. As a proof of versatility for our new semantics, we also show that the stronger Relevant Closure, can be imbued with typical information in the successors of binary relations. An extensive investigation into the computational complexity of our new semantics shows that the sceptical nested variant comes at considerable additional effort, while the selective semantics reside in the complexity of classical reasoning in the underlying DL, which remains tractable in our case.

Many modern applications of description logics (DLs, for short), such as biomedical ontologies and semantic web policies, provide fresh motivations for extending DLs with nonmonotonic inferences—a topic that has attracted a significant amount of attention along the years. Despite this, nonmonotonic inferences are not yet supported by DL technology due to a number of issues related to expressiveness, computational complexity, and optimizations. This paper contributes to the practical support of nonmonotonic inferences in description logics by introducing a new semantics expressly designed to address knowledge engineering needs. This formalism has appealing expressiveness, enjoys nice computational properties, and constitutes an interesting solution to an ample class of application needs. The formalism is validated through extensive comparison with the other nonmonotonic DLs, and systematic scalability tests. The test case generator and its novel validation methodology constitute a further contribution of this paper.

In querying semantic data, access control must take into account the information that is implicitly entailed by the accessible part of triple stores and ontologies. While there exist inference control frameworks for this purpose, they still have a limitation: the confidentiality criterion does not take into account the probabilistic knowledge of the attacker. Therefore, the existing controlled query evaluation methods may return answers that actually reveal that a secret is true with very high probability. Given that such probabilistic knowledge is becoming more and more widely available as a result of analytics of various sorts, it is im- portant to develop a refined confidentiality framework where probabilistic knowl- edge is taken into due account. Accordingly, in this paper, we extend and gener- alize an abstract data filtering framework for confidentiality-preserving, policy- based data publishing. The confidentiality requirement is strengthened so that the probability that a secret is true is bounded by a small constant ε. We formally de- fine such a probabilistic setting, then we study two greedy data publishing meth- ods based on refusals and lies, respectively. The refusal-based method is proved to be secure and maximally cooperative among a class of “reasonable” methods. We prove also that the natural generalization of the lying method is not secure. Furthermore, we extend the complexity hardness results from the deterministic framework to the probabilistic one.

In this paper, we leverage annotated logic programs for tracking indicators of provenance and trust during reasoning, specifically focussing on the use-case of applying a scalable subset of OWL 2 RL/RDF rules over static corpora of arbitrary Linked Data (Web data). Our annotations encode three facets of information: (i) blacklist: a (possibly manually generated) boolean annotation which indicates that the referent data are known to be harmful and should be ignored during reasoning; (ii) ranking: a numeric value derived by a PageRank-inspired technique — adapted for Linked Data — which determines the centrality of certain data artefacts (such as RDF documents and statements); (iii) authority: a boolean value which uses Linked Data principles to conservatively determine whether or not some terminological information can be trusted. We formalise a logical framework which annotates inferences with the strength of derivation along these dimensions of trust and provenance; we formally demonstrate some desirable properties of the deployment of annotated logic programming in our setting, which guarantees (i) a unique minimal model (least fixpoint); (ii) monotonicity; (iii) finitariness; and (iv) finally decidability. In so doing, we also give some formal results which reveal strategies for scalable and efficient implementation of various reasoning tasks one might consider. Thereafter, we discuss scalable and distributed implementation strategies for applying our ranking and reasoning methods over a cluster of commodity hardware; throughout, we provide evaluation of our methods over 1 billion Linked Data quadruples crawled from approximately 4 million individual Web documents, empirically demonstrating the scalability of our approach, and how our annotation values help ensure a more robust form of reasoning. We finally sketch, discuss and evaluate a use-case for a simple repair of inconsistencies detectable within OWL 2 RL/RDF constraint rules using ranking annotations to detect and defeat the “marginal view”, and in so doing, infer an empirical “consistency threshold” for the Web of Data in our setting.

We introduce optimization techniques for reasoning in $$\mathcal {DL}^\mathrm {N} $$---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size.

Context-aware access control systems should reactively adapt access control decisions to dynamic environmental conditions. In this paper we present ERBAC - an event-driven extension of the TRBAC model that allows the specification and enforcement of general reactive policies - and its implementation. While almost all the individual features of ERBAC occur separately in some previous model, the detailed design of the policy language, its implementation in XACML, and its testing contribute to the development of expressive, event-driven policy frameworks by demonstrating that this rich model can be satisfactorily implemented, and that its expressivity and performance are compatible with a variety of realistic application scenarios. In particular, a number of examples illustrate ERBAC's expressive power, and its ability of handling exceptional situations in a flexible way, while keeping policies compact and manageable. The prototype extends XACML's language and the implementation of the PDP to support the new model. Systematic scalability experiments show that the computational cost of policy rule evaluation in ERBAC is compatible with real-world applications.

We revisit non-monotonic description logics based on circumscription with preferences and prove several decidability results for their satisfiability problem. In particular, we consider circumscribed description logics without the finite model property DL-Lite $_{\mathcal{F}}$ and $\mathcal{ALCFI}$ and with fixed roles DL-Lite $_{\mathcal{F}}$ and a fragment of DL-Lite $_{\mathcal{R}}$ , improving upon previous decidability results that are limited to logics which have the finite model property and do not allow to fix roles during minimization.

In this work we generalize standard Decision Theory by assuming that two outcomes can also be incomparable. Two motivating scenarios show how incomparability may be helpful to represent those situations where, due to lack of information, the decision maker would like to maintain different options alive and defer the final decision. In particular, a new axiomatization is given which turns out to be a weakening of the classical set of axioms used in Decision Theory. Preliminary results show how preferences involving complex distributions are related to judgments on single alternatives., In Proceedings SR 2014, arXiv:1404.0414

Role-based access control (RBAC) models are receiving increasing attention as a generalized approach to access control. Roles may be available to users at certain time periods, and unavailable at others. Moreover, there can be temporal dependencies among roles. To tackle such dynamic aspects, we introduce Temporal-RBAC (TRBAC), an extension of the RBAC model. TRBAC supports periodic role enabling and disabling---possibly with individual exceptions for particular users---and temporal dependencies among such actions, expressed by means of role triggers. Role trigger actions may be either immediately executed, or deferred by an explicitly specified amount of time. Enabling and disabling actions may be given a priority, which is used to solve conflicting actions. A formal semantics for the specification language is provided, and a polynomial safeness check is introduced to reject ambiguous or inconsistent specifications. Finally, a system implementing TRBAC on top of a conventional DBMS is presented.

As fragments of first-order logic, Description logics (DLs) do not provide nonmonotonic features such as defeasible inheritance and default rules. Since many applications would benefit from the availability of such features, several families of nonmonotonic DLs have been developed that are mostly based on default logic and autoepistemic logic. In this paper, we consider circumscription as an interesting alternative approach to nonmonotonic DLs that, in particular, supports defeasible inheritance in a natural way. We study DLs extended with circumscription under different language restrictions and under different constraints on the sets of minimized, fixed, and varying predicates, and pinpoint the exact computational complexity of reasoning for DLs ranging from ALC to ALCIO and ALCQO. When the minimized and fixed predicates include only concept names but no role names, then reasoning is complete for NExpTime^NP. It becomes complete for NP^NExpTime when the number of minimized and fixed predicates is bounded by a constant. If roles can be minimized or fixed, then complexity ranges from NExpTime^NP to undecidability.

The ample literature on confidentiality-preserving data publishing --- and controlled query evaluation CQE in particular --- leaves several questions open. Are the greedy data-filtering algorithms adopted in the literature maximally cooperative? Can novel secure view formats or answer distortion methods improve security or cooperativeness? What is the inherent complexity of confidentiality-preserving data publishing under different constraints, such as cooperativeness and availability? Can the theoretical results on CQE be systematically extended to more general settings? In this paper we answer the above questions using a completely generic, abstract data filtering framework, independent from any syntactic details and data source encodings, and compatible with all possible distortion methods. Some of the main results are: Refusal-based filterings can be adopted as a normal form for all kinds of filterings; greedy refusal-based filterings are optimal; cooperativeness checks and some availability checks are coNP-hard in the simplest case.

The cloud computing paradigm requires solutions supporting customers in the selection of services that satisfy their functional and non-functional requirements. These solu- tions must i) support the dynamic, multi-cloud nature of service provisioning, ii) manage scenarios where no total preference relation over service properties is available, and iii) prevent providers from misrepresenting or overstating their properties. In this paper we put forward the idea of modeling multi- cloud provisioning scenarios as procurement e-auctions (where the auctioneer is the customer and the bidders are service providers). We introduce a service selection process based on matching and ranking algorithms, and an e-auction mechanism that addresses the above requirements, encouraging trustwor- thy bids and therefore improving the truthfulness on the e- auction outcome. Finally we describe the implementation of a prototype used to evaluate the performance of our approach with respect to traditional query-based engines.