A survey of current, stand-alone OWL Reasoners

Abstract. We present a survey of the current OWL reasoner landscape. Through literature and web search we have identified 35 OWL reasoners that are, at least to some degree, actively maintained. We conducted a survey directly addressing the respective developers, and collected 33 responses. We present an analysis of the survey, characterising all reasoners across a wide range of categories such as supported expressiveness and reasoning services. We will also provide some insight about ongoing research efforts and a rough categorisation of reasoner calculi

NORA: Scalable OWL reasoner based on NoSQL databasesand Apache Spark

Reasoning is the process of inferring new knowledge and identifying inconsis-tencies within ontologies. Traditional techniques often prove inadequate whenreasoning over large Knowledge Bases containing millions or billions of facts.This article introduces NORA, a persistent and scalable OWL reasoner built ontop of Apache Spark, designed to address the challenges of reasoning over exten-sive and complex ontologies. NORA exploits the scalability of NoSQL databasesto effectively apply inference rules to Big Data ontologies with large ABoxes. Tofacilitatescalablereasoning,OWLdata,includingclassandpropertyhierarchiesand instances, are materialized in the Apache Cassandra database. Spark pro-grams are then evaluated iteratively, uncovering new implicit knowledge fromthe dataset and leading to enhanced performance and more efficient reasoningover large-scale ontologies. NORA has undergone a thorough evaluation withdifferent benchmarking ontologies of varying sizes to assess the scalability of thedeveloped solution.Funding for open access charge: Universidad de Málaga / CBUA This work has been partially funded by grant (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41,AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploita-tion). Antonio Benítez-Hidalgo is supported by Grant PRE2018-084280 (Spanish Ministry of Science, Innovation andUniversities)

mini me swift the first mobile owl reasoner for ios

Mobile reasoners play a pivotal role in the so-called Semantic Web of Things. While several tools exist for the Android platform, iOS has been neglected so far. This is due to architectural differences and unavailability of OWL manipulation libraries, which make porting existing engines harder. This paper presents Mini-ME Swift, the first Description Logics reasoner for iOS. It implements standard (Subsumption, Satisfiability, Classification, Consistency) and non-standard (Abduction, Contraction, Covering, Difference) inferences in an OWL 2 fragment. Peculiarities are discussed and performance results are presented, comparing Mini-ME Swift with other state-of-the-art OWL reasoners

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

Context Reasoning for Role-Based Models

In a modern world software systems are literally everywhere. These should cope with very complex scenarios including the ability of context-awareness and self-adaptability. The concept of roles provide the means to model such complex, context-dependent systems. In role-based systems, the relational and context-dependent properties of objects are transferred into the roles that the object plays in a certain context. However, even if the domain can be expressed in a well-structured and modular way, role-based models can still be hard to comprehend due to the sophisticated semantics of roles, contexts and different constraints. Hence, unintended implications or inconsistencies may be overlooked. A feasible logical formalism is required here. In this setting Description Logics (DLs) fit very well as a starting point for further considerations since as a decidable fragment of first-order logic they have both an underlying formal semantics and decidable reasoning problems. DLs are a well-understood family of knowledge representation formalisms which allow to represent application domains in a well-structured way by DL-concepts, i.e. unary predicates, and DL-roles, i.e. binary predicates. However, classical DLs lack expressive power to formalise contextual knowledge which is crucial for formalising role-based systems. We investigate a novel family of contextualised description logics that is capable of expressing contextual knowledge and preserves decidability even in the presence of rigid DL-roles, i.e. relational structures that are context-independent. For these contextualised description logics we thoroughly analyse the complexity of the consistency problem. Furthermore, we present a mapping algorithm that allows for an automated translation from a formal role-based model, namely a Compartment Role Object Model (CROM), into a contextualised DL ontology. We prove the semantical correctness and provide ideas how features extending CROM can be expressed in our contextualised DLs. As final step for a completely automated analysis of role-based models, we investigate a practical reasoning algorithm and implement the first reasoner that can process contextual ontologies

Policy-Driven Governance in Cloud Service Ecosystems

Cloud application development platforms facilitate new models of software co-development and forge environments best characterised as cloud service ecosystems. The value of those ecosystems increases exponentially with the addition of more users and third-party services. Growth however breeds complexity and puts reliability at risk, requiring all stakeholders to exercise control over changes in the ecosystem that may affect them. This is a challenge of governance. From the viewpoint of the ecosystem coordinator, governance is about preventing negative ripple effects from new software added to the platform. From the viewpoint of third-party developers and end-users, governance is about ensuring that the cloud services they consume or deliver comply with requirements on a continuous basis. To facilitate different forms of governance in a cloud service ecosystem we need governance support systems that achieve separation of concerns between the roles of policy provider, governed resource provider and policy evaluator. This calls for better modularisation of the governance support system architecture, decoupling governance policies from policy evaluation engines and governed resources. It also calls for an improved approach to policy engineering with increased automation and efficient exchange of governance policies and related data between ecosystem partners. The thesis supported by this research is that governance support systems that satisfy such requirements are both feasible and useful to develop through a framework that integrates Semantic Web technologies and Linked Data principles. The PROBE framework presented in this dissertation comprises four components: (1) a governance ontology serving as shared ecosystem vocabulary for policies and resources; (2) a method for the definition of governance policies; (3) a method for sharing descriptions of governed resources between ecosystem partners; (4) a method for evaluating governance policies against descriptions of governed ecosystem resources. The feasibility and usefulness of PROBE are demonstrated with the help of an industrial case study on cloud service ecosystem governance

Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018

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