Foundational Ontologies meet Ontology Matching: A Survey

Ontology matching is a research area aimed at finding ways to make different ontologies interoperable. Solutions to the problem have been proposed from different disciplines, including databases, natural language processing, and machine learning. The role of foundational ontologies for ontology matching is an important one. It is multifaceted and with room for development. This paper presents an overview of the different tasks involved in ontology matching that consider foundational ontologies. We discuss the strengths and weaknesses of existing proposals and highlight the challenges to be addressed in the future

Inferring Ontological Categories of OWL Classes Using Foundational Rules

Several efforts that leverage the tools of formal ontology (such as OntoClean, OntoUML, and UFO) have demonstrated the fruitfulness of considering key metaproperties of classes in ontology engineering. These metaproperties include sortality, rigidity, and external dependence, and give rise to many fine-grained ontological categories for classes, including, among others, kinds, phases, roles, mixins, etc. Despite that, it is still common practice to apply representation schemes and approaches - such as OWL - that do not benefit from identifying these ontological categories, and simplistically treat all classes in the same manner. In this paper, we propose an approach to support the automated classification of classes into the ontological categories underlying the (g)UFO foundational ontology. We propose a set of inference rules derived from (g)UFO's axiomatization that, given an initial classification of the classes in an OWL ontology, can support the inference of the classification for the remaining classes in the ontology. We formalize these rules, implement them in a computational tool and assess them against a catalog of ontologies designed by a variety of users for a number of domains.</p

Editorial: Formal Ontologies meet Industry

The Formal Ontologies meet Industry (FOMI) workshop series is a scientific initiative supported by the International Association for Ontology and its Applications (IAOA) aimed at bringing together academics and practitioners interested in ontologies for industry. FOMI addresses research and application topics concerning, e.g., the design of domain-specific ontologies, the development of ontology-based information systems, or the investigation of the theoretical underpinnings of formal ontology when tuned to engineering applications

The Need and Requirements to a Strategy Ontology

The importance of strategy and strategy construct is not a new phenomenon. However as strategy work becomes less tangible, concerns with understanding, describing, and managing strategies develops into an increasingly complex subject. Current strategy concepts are dispersed and lack integration. Moreover, the enablement of modelling practices around strategy concepts considering the entire strategy lifecycle are also missing. Consequently, this paper focuses on issues with strategy in theory and practice, why a strategy ontology is needed and how this can be developed

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

The Requirements for Ontologies in Medical Data Integration: A Case Study

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In the naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable

Boosting D3FEND: Ontological Analysis and Recommendations

Formal Ontology is a discipline whose business is to develop formal theories about general aspects of reality such as identity, dependence, parthood, truthmaking, causality, etc. A foundational ontology is a specific consistent set of these ontological theories that support activities such as domain analysis, conceptual clarification, and meaning negotiation. A (well-founded) core ontology specifies, under a foundational ontology, the central concepts and relations of a given domain. Foundational and core ontologies can be seen as ontology engineering frameworks to systematically address the laborious task of building large (more specific) domain ontologies. However, both in research and industry, it is common that ontologies as computational artifacts are built without the aid of any framework of this kind, favoring the occurrence of numerous modeling mistakes and gaps. Through a case study, here we show an exemplar of such a case in the domain of cybersecurity by providing an ontological analysis of D3FEND, an OWL knowledge graph of cybersecurity countermeasure techniques proposed by the MITRE Corporation. Based on the Reference Ontology for Security Engineering (ROSE), a core ontology of the security domain founded in the Unified Foundational Ontology (UFO), our investigation reveals a number of semantic deficiencies in D3FEND, including missing concepts, semantic overload of terms, and a systematic lack of constraints that renders that model under-specified. As a result of our ontological analysis, we propose several suggestions for the appropriate redesign of D3FEND to overcome those issues.</p