We propose a general framework for assessing the exposure of sensitive knowledge in policy-protected knowledge bases (KBs), where knowledge is represented as logical theories and data protection policies are defined declaratively using epistemic dependencies. The framework models scenarios in which confidential parts of the KB may be publicly known due to security breaches. We study two fundamental decision problems: determining whether the exposed knowledge violates the data protection policy (leakage), and whether there exists a secure view of the KB that complies with the policy. We analyze the computational complexity (specifically, data complexity) of these problems, focusing on the DL-Lite_R and EL_\bot Description Logics. Our findings show that, for DL-Lite_R with restricted forms of policy, both the problems can be efficiently solved through query rewriting methods. For EL_\bot, we establish conditions for tractable computational bounds. Our results highlight the potential of this framework for practical applications in confidentiality-preserving knowledge management.

Controlled Query Evaluation (CQE) is a methodology designed to maintain confidentiality by either rejecting specific queries or adjusting responses to safeguard sensitive information. In this investigation, our focus centers on CQE within Description Logic ontologies, aiming to ensure that queries are answered truthfully as long as possible before resorting to deceptive responses, a cooperativity property which is called the "longest honeymoon". Our work introduces new semantics for CQE, denoted as MC-CQE, which enjoys the longest honeymoon property and outperforms previous methodologies in terms of cooperativity. We study the complexity of query answering in this new framework for ontologies expressed in the Description Logic DL-LiteR. Specifically, we establish data complexity results under different maximally cooperative semantics and for different classes of queries. Our results identify both tractable and intractable cases. In particular, we show that the evaluation of Boolean unions of conjunctive queries is the same under all the above semantics and its data complexity is in AC0. This result makes query answering amenable to SQL query rewriting. However, this favorable property does not extend to open queries, even with a restricted query language limited to conjunctions of atoms. While, in general, answering open queries in the MC-CQE framework is intractable, we identify a sub-family of semantics under which answering full conjunctive queries is tractable.

In this paper we study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor, which defines the answers to each query posed to the data/knowledge base. We investigate both semantic and computational properties of CQE in the context of OWL ontologies, and specifically in the description logic DL-LiteR, which underpins the OWL 2 QL profile. In our analysis, we focus on semantics of CQE based on censors (called optimal GA censors) that enjoy the so-called indistinguishability property, analyzing the trade-off between maximizing the amount of data disclosed by query answers and minimizing the computational cost of privacy-preserving query answering. We first study the data complexity of skeptical entailment of unions of conjunctive queries under all the optimal GA censors, showing that the computational cost of query answering in this setting is intractable. To overcome this computational issue, we then define a different semantics for CQE centered around the notion of intersection of all the optimal GA censors. We show that query answering over OWL 2 QL ontologies under the new intersection-based semantics for CQE enjoys tractability and is first-order rewritable, i.e. amenable to be implemented through SQL query rewriting techniques and the use of standard relational database systems; on the other hand, this approach shows limitations in terms of amount of data disclosed. To improve this aspect, we add preferences between ontology predicates to the CQE framework, and identify a semantics under which query answering over OWL 2 QL ontologies maintains the same computational properties of the intersection-based approach without preferences.

We propose a novel framework, Multi-OBDM, that extends ontology-based data management (OBDM) by treating multidimensional data cubes and hierarchies as first-class constructs. In our framework, these components are intentionally defined over a domain ontology, which is connected to operational data sources through mappings, in accordance with the OBDM paradigm. This design explicitly and formally clarifies the relationship between aggregated data and the underlying individual-level data, while preserving a declarative specification of data lineage. Furthermore, we introduce essential reasoning services tailored to multidimensional entities, including various types of analysis and quality assessment tasks, as well as query answering.

In this paper, we propose the use of epistemic dependencies to express data protection policies in Controlled Query Evaluation (CQE), which is a form of confidentiality-preserving query answering over ontologies and databases. The resulting policy language goes significantly beyond those proposed in the literature on CQE so far, allowing for very rich and practically interesting forms of data protection rules. We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries when ontologies are specified in the Description Logic DL-Lite_R. Interestingly, while we show that the problem is in general intractable, we prove tractability for the case of acyclic epistemic dependencies by providing a suitable query rewriting algorithm. The latter result paves the way towards the implementation and practical application of this new approach to CQE.

In this paper, we propose the use of epistemic dependencies to express data protection policies in Controlled Query Evaluation (CQE), which is a form of confidentiality-preserving query answering over ontologies and databases. The resulting policy language goes significantly beyond those proposed in the literature on CQE so far, allowing for very rich and practically interesting forms of data protection rules. We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries when ontologies are specified in the Description Logic DL-LiteR. Interestingly, while we show that the problem is in general intractable, we prove tractability for the case of acyclic epistemic dependencies by providing a suitable query rewriting algorithm. The latter result paves the way towards the implementation and practical application of this new approach to CQE.

Controlled query evaluation (CQE) is an approach for confidentiality-preserving query answering where a function called censor alters query answers so that users can never infer data that are protected by a policy given in terms of logic formulae. In this paper, we review some foundational results we have recently found in the context of CQE over Description Logic ontologies. In more detail, we discuss the main characteristics of two notions of censor, CQ censor and GA censor, focusing on the computational complexity of query answering and on the notion of indistinguishability. The latter is a desirable property imposing that a censor always makes a user believe that the underlying data instance might not contain confidential data. As for computational aspects, we characterize the data complexity of answering conjunctive queries for the relevant and practical case of DL-Lite _ℛ ontologies. Since neither CQ censors nor GA censors enjoy both indistinguishability and tractability of query answering in the analyzed setting, we finally recall the notion of IGA censors, a sound approximation of GA censors which instead enjoys both properties, thus paving the way for robust and practical CQE for DL-Lite _ℛ ontologies.

Controlled Query Evaluation (CQE) is a framework for the protection of confidential data, where a policy given in terms of logic formulae indicates which information must be kept private. Functions called censors filter query answering so that no answers are returned that may lead a user to infer data protected by the policy. The preferred censors, called optimal censors, are the ones that conceal only what is necessary, thus maximizing the returned answers. Typically, given a policy over a data or knowledge base, several optimal censors exist.Our research on CQE is based on the following intuition: confidential data are those that violate the logical assertions specifying the policy, and thus censoring them in query answering is similar to processing queries in the presence of inconsistent data as studied in Consistent Query Answering (CQA). In this paper, we investigate the relationship between CQE and CQA in the context of Description Logic ontologies. We borrow the idea from CQA that query answering is a form of skeptical reasoning that takes into account all possible optimal censors. This approach leads to a revised notion of CQE, which allows us to avoid making an arbitrary choice on the censor to be selected, as done by previous research on the topic.We then study the data complexity of query answering in our CQE framework, for conjunctive queries issued over ontologies specified in the popular Description Logics DL-LiteR and EL⊥. In our analysis, we consider some variants of the censor language, which is the language used by the censor to enforce the policy. Whereas the problem is in general intractable for simple censor languages, we show that for DL-LiteR ontologies it is first-order rewritable, and thus in AC0 in data complexity, for the most expressive censor language we propose.

This extended abstract summarizes our recent work [1] in which we study a dynamic Controlled Query Evaluation method over Description Logic ontologies.

We describe a new methodology for modeling aggregate data and explicitly connecting them to the individual-level data from which aggregates are generated. The approach makes use of OWL2 ontologies that formalize both the application domain and multidimensional constructs, such as data cubes, measures, dimensions, and hierarchies. This contribution stems from a collaboration among ISTAT, Sapienza University of Rome, and OBDA Systems, within the project INTERSTAT.

We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules. We define the semantics of the KB in terms of special instances that involve equivalence classes of entities and sets of values. Intuitively, the former collect all entities denoting the same real-world object, while the latter collect all alternative values for an attribute. This approach allows us to both resolve entities and bypass possible inconsistencies in the data. We then design a chase procedure that is tailored to this new framework and has the feature that it never fails; moreover, when the chase procedure terminates, it produces a universal solution, which in turn can be used to obtain the certain answers to conjunctive queries. We finally discuss challenges arising when the chase does not terminate.

In this paper, we present a comparative analysis of the leading rule-based information extraction systems in both research and industry, focusing on their main characteristics and their performance. Our evaluation was performed on a dataset of text documents about financial product descriptions from a real-world application scenario. In this study, we demonstrate that, while the considered tools share similarities in terms of expressiveness of their extractors and produce results of comparable quality, the implementation choices of their engines have a substantial impact on their overall execution time. Moreover, we emphasize that some of the considered tools offer seamless support for writing extraction rules, effectively addressing one of the common challenges associated with rule-based approaches.

In this paper we study Graphol, a fully graphical language inspired by standard formalisms for conceptual modeling, similar to the UML class diagram and the ER model, but equipped with formal semantics. We formally prove that Graphol is equivalent to OWL 2, i.e., it can capture every OWL 2 ontology and vice versa. We also present some usability studies indicating that Graphol is suitable for quick adoption by conceptual modelers that are familiar with UML and ER. This is further testified by the adoption of Graphol for ontology representation in several industrial projects.