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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
An ontology is a machine-processable representation of knowledge about a domain of interest.
The amount of data exposed in the form of RDF and OWL continues to increase exponentially. Some approaches have already been proposed for the scalable reasoning over several language profiles such as RDFS, OWL Horst, OWL 2 EL, OWL 2 RL etc. But all those approaches are limited to the particular ruleset that the reasoner supports. In this work, we propose the idea for a rule-based distributed reasoning framework that can support any given ruleset and highlight some of the challenges that needs to be solved in order to implement such a framework.
We propose adding two additional steps to OWL ontology development and offer tools supporting it. A so-called PURO background model of an example situation to be covered by the ontology is first created, then a seed of the ontology is generated automatically from it, allowing users to choose suitable modeling style and import the ontology seed into a common ontology editor where it can be finalized. Using PURO as intermediary model should enable better collaboration, documentation and early detection of design problems. The paper focuses on OBOWLMorph: a tool for ontology generation from a PURO model.
SKOS and OWL are quite different but complimentary languages. SKOS is targeted at "cognitive" or "navigational" representations, that is, thesauri, controlled vocabularies, and the like. OWL is targeted at logical representations of conceptual knowledge. To a first approximation, SKOS vocabularies try to capture useful relations between concepts, whereas OWL ontologies aim to capture true relations between concepts. Now, of course, the true is sometimes useful and the useful often true, thus SKOS and OWL overlap to some degree. However, there are applications where we need to know true relations (e.g., generating multiple choice questions). Furthermore, SKOS relations are not precisely specified (by design). For example, many different ways of being useful can be covered by the same SKOS relation, but only one way of being useful is actually applicable to some application.In this paper, we present a case study of modifying a large, existing SKOS vocabulary partially into OWL. This lifting is motivated by an application (generating multiple choice questions) that requires more precision in the representation than SKOS alone supports.
Ontologies are widely used in biology and biomedicine for the annotation and integration of data, and hundreds of ontologies have been developed for this purpose. These ontologies also constitute large volumes of formalized domain knowledge, usually expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within them relies on the use of automated reasoning. We have developed Aber-OWL, an ontology repository that provides OWL EL reasoning to answer queries and verify the consistency of ontologies. Aber-OWL also provides a set of web services which provide ontology-based access to scientific literature in Pubmed and Pubmed Central, SPARQL query expansion to retrieve linked data, and integration with Bio2RDF. Here, we report on our experiences with Aber-OWL and outline a roadmap for future development. Aber-OWL is freely available at http://aber-owl.net .
With the growing interest in using ontologies in semantically-enabled applications, the interest in enhancing the quality of such ontologies has grown as well. Standard reasoning services focus on certain obvious dimensions of quality, e.g., to detect inconsistencies and incoherence. In addition, bespoke tools have been presented to address the completeness dimension of quality, e.g., missing entailments. These tools are usually focused on very restricted subsets of all the possible missing entailments, i.e., only atomic subsumptions. We present a new protocol to detect both existing invalid entailments and missing valid entailments. We also present a case study to evaluate the usefulness of the presented protocol for ontology validation purposes.
We describe the core module of the Occupational Safety and Health Domain Ontology (OSHDO-Core) v1.0, we have developed. We also discuss the requirements specification and modeling and ontology engineering issues encountered during the design process. The resulting OSHDO-Core contains the core vocabulary of the domain with basic ontological distinctions. The ultimate goal is to establish a common vocabulary and a core formal model of the domain.
In ontology engineering, particularly when dealing with heterogeneous domains and their subfields, legacy data, various data models, existing standards, code lists, and so forth, there is a frequently recurring need to express certain types of axioms that allow diverse representational choices interoperate. Some of these axioms, which we call typecasting axioms, point to limitations of the Web Ontology Language (OWL), while others require best practice guides for the community. Here, we introduce these typecasting axioms and elaborate how such axioms can help the development of data integration using ontology and ontology patterns. We then conclude with a brief catalog of open research problems motivated by typecasting axioms, which may be of potential interest to both application developers and researchers working on logical foundations of OWL.
In this paper we present two tools that we are developing at Cognitum for managing large knowledge bases: Fluent Editor and the Ontorion Server. We have been able to build a collaborative knowledge management system using these two tools. We show how this system can be used for the concurrent modification of knowledge and how we can manage multiple modifications to the same knowledge.
