Preventing, Detecting, and Revising Flaws in Object Property Expressions

The OWL 2 DL ontology language is very expressive and has many features for declaring complex object property expressions. Standard reasoning services for OWL ontologies take these expressions as correct and according to the ontologist's intention. However, the more one can do, the higher the chance modelling flaws are introduced; hence, an unexpected or undesired classification or inconsistency in the class hierarchy may actually be due to a mistake in the 'object property box', not the class axioms. We analyse the principles of subsumption in object property hierarchies, and use it to identify the types of flaws that can occur in object property expressions. We propose the compatibility services SubProS and ProChainS that check for meaningful property hierarchies and property chaining and propose how to revise a flaw. These insights can also be used to prevent flaws and to choose the best option, which we demonstrate with the chain pattern for upward and downward distributivity over parthood relations. SubProS and ProChainS were evaluated with several ontologies, which demonstrates that such flaws do exist, that they can be isolated effectively, and useful suggestions for revisions can be proposed

The Current Landscape of Pitfalls in Ontologies

A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development projects. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems, and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!’s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies

Pitfalls in Ontologies and TIPS to Prevent Them

Abstract. A growing number of ontologies are already available thanks to development initiatives in many different fields. In such ontology developments, developers must tackle a wide range of difficulties and handicaps, which can result in the appearance of anomalies in the resulting ontologies. Therefore, ontology evaluation plays a key role in ontology development. OOPS! is an on-line tool that automatically detects pitfalls, considered as potential errors or problems-and thus may help ontology developers to improve their ontologies. To gain insight in the existence of pitfalls and to assess whether there are differences among ontologies developed by novices, a random set of already scanned ontologies, and existing well-known ones, data of 406 OWL ontologies were analysed on OOPS!'s 21 pitfalls, of which 24 ontologies were also examined manually on the detected pitfalls. The various analyses performed show only minor differences between the three sets of ontologies, therewith providing a general landscape of pitfalls in ontologies. We also propose guidelines to avoid the inclusion of such common pitfalls in new ontologies, the Typical pItfalls Prevention Scheme (TIPS), so as to increase the baseline quality of OWL ontologies

Exploring Reasoning with the DMOP Ontology

We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. DMOP contains in-depth descriptions of DM tasks, data, algorithms, hypotheses, and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. The choices made led to v5.4 of the DMOP ontology. We report some evaluations on processing DMOP with a standard reasoner by considering different DMOP features

The Data Mining OPtimization Ontology

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner

Towards a knowledge driven framework for bridging the gap between software and data engineering

In this paper we present a collection of ontologies specifically designed to model the information exchange needs of combined software and data engineering. Effective, collaborative integration of software and big data engineering for Web-scale systems, is now a crucial technical and economic challenge. This requires new combined data and software engineering processes and tools. Our proposed models have been deployed to enable: tool-chain integration, such as the exchange of data quality reports; cross-domain communication, such as interlinked data and software unit testing; mediation of the system design process through the capture of design intents and as a source of context for model-driven software engineering processes. These ontologies are deployed in webscale, data-intensive, system development environments in both the commercial and academic domains. We exemplify the usage of the suite on case-studies emerging from two complex collaborative software and data engineering scenarios: one from the legal sector and the other from the Social sciences and Humanities domain

A model-driven approach to the conceptual modeling of situations : from specification to validation

A modelagem de situações para aplicações sensíveis ao contexto, também chamadas de aplicações sensíveis a situações, é, por um lado, uma tarefa chave para o funcionamento adequado dessas aplicações. Por outro lado, essa também é uma tafera árdua graças à complexidade e à vasta gama de tipos de situações possíveis. Com o intuito de facilitar a representação desses tipos de situações em tempo de projeto, foi criada a Linguagem de Modelagem de Situações (Situation Modeling Language - SML), a qual se baseia parcialmente em ricas teorias ontológicas de modelagem conceitual, além de fornecer uma plataforma de detecção de situação em tempo de execução. Apesar do benefício da existência dessa infraestrutura, a tarefa de definir tipos de situação é ainda não-trivial, podendo carregar problemas que dificilmente são detectados por modeladores via inspeções manuais. Esta dissertação tem o propósito de melhorar e facilitar ainda mais a definição de tipos de situação em SML propondo: (i) uma maior integração da linguagem com as teorias ontológicas de modelagem conceitual pelo uso da linguagem OntoUML, visando aumentar a expressividade dos modelos de situação; e (ii) uma abordagem para validação de tipos de situação usando um método formal, visando garantir que os modelos criados correspondam à intenção do modelador. Tanto a integração quanto a validação são implementadas em uma ferramenta para especificação, verificação e validação de tipos de situação ontologicamente enriquecidos.The modeling of situation types for context-aware applications, also called situationaware applications, is, on the one hand, a key task to the proper functioning of those applications. On the other hand, it is also a hard task given the complexity and the wide range of possible situation types. Aiming at facilitating the representation of those types of situations at design-time, the Situation Modeling Language (SML) was created. This language is based partially on rich ontological theories of conceptual modeling and is accompanied by a platform for situation-detection at runtime. Despite the benefits of the availability of this suitable infrastructure, the definition of situation types, being a non-trivial task, can still pose problems that are hardly detected by modelers by manual model inspection. This thesis aims at improving and facilitating the definition of situation types in SML by proposing: (i) the integration between the language and the ontological theories of conceptual modeling by using the OntoUML language, with the purpose of increasing the expressivity of situation type models; and (ii) an approach for the validation of situation type models using a lightweight formal method, aiming at increasing the correspondence between the created models’ instances and the modeler’s intentions. Both the integration and the validation are implemented in a tool for specification, verification and validation of ontologically-enriched situation types.CAPE

Plant Information Modelling, Using Artificial Intelligence, for Process Hazard and Risk Analysis Study

In this research, the application of Artificial Intelligence and knowledge engineering, automation of equipment arrangement design, automation of piping and support design, using machine learning to automate the stress analysis, and finally, using information modelling to shift ‘field weld locating’ activity from the construction to the design phase were investigated. The results of integrating these methods on case studies, to increase the safety in the lifecycle of process plants were analysed and discussed