Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables

Ontologies on the semantic web

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT

Design and foundations of ontologies with meta-modelling.

Ontologies are broadly used and proved modelling artifacts to conceptualize a domain. In particular the W3C standard ontology language OWL, based on description logics, allows the ontology engineer to formally represent a domain as a set of assertions about concepts, individuals and roles. Nowadays, complex applications leads to combine autonomously built ontologies into ontology networks by relating them through di erent kind of relations. Some relations, such as the mapping of two concepts from di erent ontologies, can be expressed by the standard ontology language OWL, i.e. by the description logics behind it. However, there are other kind of relations that are not soundly represented by OWL, such as the meta-modelling relation. The meta-modelling relation has to do with the modelling of the same real object with di erent abstraction levels, e.g. as a concept in one ontology and as an individual in another ontology. Even though there are a set of approaches that extend description logics to deal with meta-modelling, they do not solve relevant requirements of some real scenarios. The present thesis work introduces an extension to the description logic SHIQ which provides a exible syntax and a strong semantics, and moreover ensures the well-foundedness of the interpretation domain. This approach is di erent from existing meta-modelling approaches either in the syntax or in the semantics (or both), and moreover ensures the well-foundedness of the domain which is an original contribution from the theoretical point of view. The meta-modelling extension of SHIQ introduced in the present work is justi ed by a detailed description of a set of real case studies, with an analysis of the bene ts of the new approach to solve some relevant requirements. Finally, the present work addresses the methodological issue by introducing a design pattern to help the ontology engineer in the use of the proposed meta-modelling approach

Approximate Assertional Reasoning Over Expressive Ontologies

In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed

Semantic Information Assurance for Secure Distributed Knowledge Management: A Business Process Perspective

Secure knowledge management for eBusiness processes that span multiple organizations requires intraorganizational and interorganizational perspectives on security and access control issues. There is paucity in research on information assurance of distributed interorganizational eBusiness processes from a business process perspective. This paper presents a framework for secure semantic eBusiness processes integrating three streams of research, namely: 1) eBusiness processes; 2) information assurance; and 3) semantic technology. This paper presents the conceptualization and analysis of a secure semantic eBusiness process framework and architecture, and provides a holistic view of a secure interorganizational semantic eBusiness process. This paper fills a gap in the existing literature by extending role-based access control models for eBusiness processes that are done by using ontological analysis and semantic Web technologies to develop a framework for computationally feasible secure eBusiness process knowledge representations. An integrated secure eBusiness process approach is needed to provide a unifying conceptual framework to understand the issues surrounding access control over distributed information and knowledge resources

A Semantic Approach to Secure Collaborative Inter-Organizational eBusiness Processes (SSCIOBP)

The information supply chain (ISC) involves the exchange, organization, selection, and synthesis of relevant knowledge and information about production, purchase planning, demand forecasting, and inventory among collaborating business partners in a value chain. Information and knowledge sharing in an ISC occurs in a business process context. Seamless knowledge exchange within and across organizations involved in secure business processes is critically needed to secure and cultivate the information supply chain. Extant literature does not explicitly consider or systematically represent component knowledge, process knowledge and security knowledge for business processes within and across organizations. As a result, organizations engaged in collaborative inter-organizational processes continue to be plagued with issues such as semantic conflict issues, lack of integration of heterogeneous systems, and lack of security knowledge regarding authorized access to resources. Without appropriate security controls, manual interventions lead to unauthorized access to resources. These problems motivate our Semantic Approach to Secure Collaborative Inter-Organizational eBusiness Processes (SSCIOBP). We follow a design science paradigm to identify meta-requirements of SSCIOBP and develop the design artifact. SSCIOBP is evaluated using observational and descriptive evaluation methods following Hevner et al. (2004). We apply our approach to show how the Collaborative Planning Forecasting and Replenishment (CPFR) industry standard models can be enhanced using the proposed design artifact. We apply SSCIOBP to a case study to illustrate its applicability in mapping core business processes of organizations to solve semantic inter-operability issues and systematically incorporate component, process and security knowledge in the design of secure business processes across the information supply chain

OWL Reasoners still useable in 2023

In a systematic literature and software review over 100 OWL reasoners/systems were analyzed to see if they would still be usable in 2023. This has never been done in this capacity. OWL reasoners still play an important role in knowledge organisation and management, but the last comprehensive surveys/studies are more than 8 years old. The result of this work is a comprehensive list of 95 standalone OWL reasoners and systems using an OWL reasoner. For each item, information on project pages, source code repositories and related documentation was gathered. The raw research data is provided in a Github repository for anyone to use

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as $\mathcal{AL}$-log that integrates the description logic $\mathcal{ALC}$ and the function-free Horn clausal language \textsc{Datalog}. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of $\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement. Keywords: Inductive Logic Programming, Hybrid Knowledge Representation and Reasoning Systems, Ontologies, Semantic Web. Note: To appear in Theory and Practice of Logic Programming (TPLP)Comment: 30 pages, 6 figure