OWL: Experiences and Directions – Reasoner Evaluation

Despite meriting the growing consensus between researchers and practitioners of ontology modeling, the Web Ontology Language OWL still has a modest presence in the communities of “traditional” web developers and software engineers. This resulted in hoarding the semantic web field in a rather small circle of people with a certain profile of expertise. In this paper we present OntoJIT, our novel approach toward a democratized semantic web where we bring OWL ontologies into the comfort-zone of end-application developers. We focus particularly on parsing OWL source files into executable ontologies in an object oriented programming paradigm. We finally demonstrate the dynamic code-base created as the result of parsing some reference OWL DL ontologies.

Healthy lifestyle is not only a today trend fostered by the explosion of gluten-free foods (or similar) or by the presence on the market of many devices for monitoring how many steps you do during a day and how many calories you spent in the last twenty-four hours. Following a healthy lifestyle means also to prevent diseases as consequence of an incorrect diet or to avoid chronic pathologies that may occur after sensitive surgeries. In this paper, we present the first version of the PerKApp ontology. Here, we model concepts representing detailed foods properties, with the goal of supporting the construction of intelligent interfaces for domain experts. This ontology is part of the PerKApp project aiming to provide a full-fledged platform supporting the remote lifestyle monitoring of users by providing real-time feedback through persuasive context-based messages when necessary. Beside the ontology, the paper will also provide an overview of the PerKApp project and how the presented ontology will be used.

In order to provide support for the construction of MCQs, there have been recent efforts to generate MCQs with controlled difficulty from OWL ontologies. Preliminary evaluation suggests that automatically generated questions are not field ready yet and highlight the need for further evaluations. In this study, we have presented an extensive evaluation of automatically generated MCQs. We found that even questions that adhere to guidelines are subject to the clustering of distractors. Hence, the clustering of distractors must be realised as this could affect the prediction of difficulty.

Units of measurement are an essential part of dataset descriptions as they are required for a valid interpretation of the data. One obvious choice for representing units are ontologies, but as every application supports different use cases a multitude of ontologies has been created. Each of these is suited best for just a subset of the possible use cases. The problem of choosing an ontology for a new project hence consists of two major aspects: What use cases need to be covered and which ontology caters best to them?

We describe possible use cases and analyze their requirements. The results are then used to assess the modeling of the domain in different ontologies with respect to their suitability for those use cases. This analysis shows the differences in the support for different use cases. It can help developers to choose the best ontology for their specific needs and also highlights areas for further ontology improvement.

In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. In addition, we discuss the results of an experiment where we asked nine people (with no or limited expertise in Semantic Web technologies and Ontology Engineering) to use SAMOD for developing a small ontology.

Sustainability indicators are increasingly being used to measure the economic, environmental and social properties of complex systems across different temporal and spatial scales. This motivates their inclusion in open distributed knowledge systems such as the Semantic Web. The diversity of such indicator sets provides considerable choice but also poses problems for those who need to measure and report. To address the modelling problems of indicator sets, we propose the use of Value Partition pattern to construct two design candidates: generic and specific. The generic design is more abstract, with fewer classes and properties, than the specific design. Documents describing two indicator systems – the Global Reporting Initiative and the Organisation for Economic Co-operation and Development – are used in the design of both candidate ontologies. We show the use of existing structural ontology design patterns can help to solve problems of ontology representations for modelling sustainability indicator sets.

Testing of a software system is resource-consuming activity. One of the promising ways to improve the efficiency of the software testing process is to use ontologies for testing. This paper presents an approach to test case generation based on the use of an ontology and inference rules. The ontology represents requirements from a software requirements specification, and additional knowledge about components of the software system under development. The inference rules describe strategies for deriving test cases from the ontology. The inference rules are constructed based on the examination of the existing test documentation and acquisition of knowledge from experienced software testers. The inference rules are implemented in Prolog and applied to the ontology that is translated from OWL functional-style syntax to Prolog syntax. The first experiments with the implementation showed that it was possible to generate test cases with the same level of detail as the existing, manually produced, test cases.

Since more than a decade, theoretical research on ontology evolution has been published in literature and several frameworks for managing ontology changes have been proposed. However, there are less studies that analyze widely used ontologies that were developed in a collaborative manner to understand community-driven ontology evolution in practice. In this paper, we perform an empirical analysis on how four well-known ontologies (DBpedia, Schema.org, PROV-O, and FOAF) have evolved through their lifetime and an analysis of the data quality issues caused by some of the ontology changes. To that end, the paper discusses the composition of the communities that developed the aforementioned ontologies and the ontology development process followed. Further, the paper analyses the changes in those ontologies in the 53 versions of them examined in this study. Depending of the use case, the community involved, and other factors different approaches for the ontology development and evolution process are used (e.g., bottom-up approach with high automation or top-down approach with a lot of manual curation). This paper concludes that one model for managing changes does not fit all. Furthermore, it is also clear that none of the selected ontologies follow the theoretical frameworks found in literature. Nevertheless, in communities where industrial participants are dominant more rigorous editorial processes are followed, largely influenced by software development tools and processes. Based on the analysis, the most common quality problems caused by ontology changes include the use of abandoned classes and properties in data and introduction of duplicate classes and properties.

The rapid change and heterogeneity of today’s generated data calls for real-time decision making systems that can cope with the presented heterogeneity. In this paper, we present an Ontology Based Event Processing system that bridges the gap between ontology-based reasoning and event processing. We propose both a language and an architecture to perform event processing over abstract ontology concepts. This allows to perform efficient temporal reasoning, while the high-level ontological definitions reduce the need for knowledge of the underlying data structure in complex domains.

An ontology signature (set of entities) can express more than its constituent concept, role and individual names, since rewriting permits defined entities to be replaced by syntactically different, albeit semantically equivalent definitions. Identifying whether a given signature permits the definition of a particular entity is a well-understood problem, while determining the smallest (minimal) signature that covers a set of entities (i.e. a task signature) poses a challenge: the complete set of alternative definitions, or even just their signature, needs to be obtained, and all combinations of such definition signatures need to be explored, for each of the entities under consideration. In this paper, we present and empirically evaluate our novel approach for efficiently computing an approximation of minimal signature cover sets.

Semantic Web and Internet of Things are progressively converging, but the lack of reasoning tools for mobile devices on the iOS platform may hinder the progress of this vision. The paper presents an early redesign of OWL API for iOS. A partial port has been developed, effective enough to support mobile reasoning engines in a moderately expressive fragment of OWL 2. Both architecture and mobile-oriented optimization are sketched and preliminary performance results are discussed.