Controlled query evaluation over OWL 2 RL ontologies

We study confidentiality enforcement in ontology-based information systems where ontologies are expressed in OWL 2 RL, a profile of OWL 2 that is becoming increasingly popular in Semantic Web applications. We formalise a natural adaptation of the Controlled Query Evaluation (CQE) framework to ontologies. Our goal is to provide CQE algorithms that (i) ensure confidentiality of sensitive information; (ii) are efficiently implementable by means of RDF triple store technologies; and (iii) ensure maximality of the answers returned by the system to user queries (thus restricting access to information as little as possible). We formally show that these requirements are in conflict and cannot be satisfied without imposing restrictions on ontologies. We propose a fragment of OWL 2 RL for which all three requirements can be satisfied. For the identified fragment, we design a CQE algorithm that has the same computational complexity as standard query answering and can be implemented by relying on state-of-the-art triple stores.</p

Controlled Query Evaluation over OWL 2 RL Ontologies

We study confidentiality enforcement in ontology-based information systems where ontologies are expressed in OWL 2 RL, a profile of OWL 2 that is becoming increasingly popular in Semantic Web applications. We formalise a natural adaptation of the Controlled Query Evaluation (CQE) framework to ontologies. Our goal is to provide CQE algorithms that (i) ensure confidentiality of sensitive information; (ii) are efficiently implementable by means of RDF triple store technologies; and (iii) ensure maximality of the answers returned by the system to user queries (thus restricting access to information as little as possible). We formally show that these requirements are in conflict and cannot be satisfied without imposing restrictions on ontologies. We propose a fragment of OWL 2 RL for which all three requirements can be satisfied. For the identified fragment, we design a CQE algorithm that has the same computational complexity as standard query answering and can be implemented by relying on state-of-the-art triple stores

Secrecy-preserving reasoning in simple description logic knowledge bases

In this dissertation, we study the problem of secrecy-preserving query answering (SPQA) against knowledge bases (KBs) under the open world assumption (OWA) - the assumption that typical KBs are incomplete. Protection of secret information is a critical requirement for the design of information systems in semantic web applications. Recently, semantic web technolo- gies are widely used in many application domains like healthcare, bioinformatics, intelligence and national security. So, there is a pressing need for developing robust secret protection mech- anisms suitable for ontology-based information systems. In our work, we use a logical approach to enforce secrecy where the domain knowledge is represented in an appropriate description logic (DL). In particular, to protect secret information we take advantage of OWA. Under OWA, a querying agent cannot distinguish whether a query is being protected or it cannot be inferred from the KB. The central idea in our approach to protect the secret information is to build a logical shield called “envelope” around the confidential information and answers queries correctly as much as possible without compromising the secrecy. We have chosen lightweight DL languages like DL-LiteR and ELH for studying SPQA problem with single querying agent in the first half of this dissertation. We have considered DL-LiteR KB with acyclic TBox and the secrecy set containing both assertional queries and Boolean Conjunctive Queries (BCQs). By computing a suitable envelope, we protect the secrets in the secrecy set. We have used Kleenes 3-valued semantics to prove the correctness of the query answering procedure. We have also performed a detailed analysis of computational complexities of various algorithms used in this dissertation. In ELH logic, we define a secrecy set that contains both assertional and general concept inclusion queries. A new strategy has been employed to construct the SPQA system for the given ELH KB. This includes designing efficient query answering algorithms based on recursive decomposition of queries and have shown that the query answering algorithms are sound and complete, thus providing correctness proof. In the second half of this dissertation, we have studied the SPQA problem in ELH♦ (ELH augmented with modal operator ♦). Given a ELH♦ KB and a finite secrecy set, we compute a SPQA system in the form of a tree, called secrecy-preserving tree. In this case the secrecy set contains only assertions. Since the information available in secrecy-preserving tree is not sufficient to answer all the queries, we further augment the query answering procedure with a recursive procedure. The recursive procedure is based on th idea of breaking the query into smaller assertions all the way until the information in the secrecy-preserving tree can be used

A semantic Bayesian network for automated share evaluation on the JSE

Advances in information technology have presented the potential to automate investment decision making processes. This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. Current decision models often do not reflect the real investment decision making process used by the broader investment community or may not be well-grounded in established investment theory. This research investigates the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was constructed based on the concepts outlined in the ontology for automatic share evaluation. The Bayesian network allows decision makers to predict future share performance and provides an investment recommendation for a specific share. The decision model was designed, refined and evaluated through an analysis of the literature on value investing theory and consultation with expert investment professionals. The performance of the decision model was validated through back testing and measured using return and risk-adjusted return measures. The model was found to provide superior returns and risk-adjusted returns for the evaluation period from 2012 to 2018 when compared to selected benchmark indices of the JSE. The result is a concrete share evaluation model grounded in investing theory and validated by investment experts that may be employed, with small modifications, in the field of value investing to identify shares with a higher probability of positive risk-adjusted returns

OPTIMIZATION OF NONSTANDARD REASONING SERVICES

The increasing adoption of semantic technologies and the corresponding increasing complexity of application requirements are motivating extensions to the standard reasoning paradigms and services supported by such technologies. This thesis focuses on two of such extensions: nonmonotonic reasoning and inference-proof access control. Expressing knowledge via general rules that admit exceptions is an approach that has been commonly adopted for centuries in areas such as law and science, and more recently in object-oriented programming and computer security. The experiences in developing complex biomedical knowledge bases reported in the literature show that a direct support to defeasible properties and exceptions would be of great help. On the other hand, there is ample evidence of the need for knowledge confidentiality measures. Ontology languages and Linked Open Data are increasingly being used to encode the private knowledge of companies and public organizations. Semantic Web techniques facilitate merging different sources of knowledge and extract implicit information, thereby putting at risk security and the privacy of individuals. But the same reasoning capabilities can be exploited to protect the confidentiality of knowledge. Both nonmonotonic inference and secure knowledge base access rely on nonstandard reasoning procedures. The design and realization of these algorithms in a scalable way (appropriate to the ever-increasing size of ontologies and knowledge bases) is carried out by means of a diversified range of optimization techniques such as appropriate module extraction and incremental reasoning. Extensive experimental evaluation shows the efficiency of the developed optimization techniques: (i) for the first time performance compatible with real-time reasoning is obtained for large nonmonotonic ontologies, while (ii) the secure ontology access control proves to be already compatible with practical use in the e-health application scenario.