Heinrich Herre

On the General Ontological Foundations of Conceptual Modeling

As pointed out in the pioneering work of [WSW99, EW01], an upper level ontology allows to evaluate the ontological correctness of a conceptual model and to develop guidelines how the constructs of a conceptual modeling language should be used. In this paper we adopt the General Ontological Language (GOL), proposed in [DHHS01], for this purpose. We discuss a number of issues that arise when applying the concepts of GOL to UML class diagrams as a conceptual modeling language. We also compare our ontological analysis of some parts of the UML with the one proposed in [EW01].

General Formal Ontology (GFO): A Foundational Ontology for Conceptual Modelling

The current chapter presents an overview about the current stage of the foundational ontology GFO. GFO (General Formal Ontology). GFO is a foundational ontology integrating objects and processes. It is being developed by the research group Onto-Med (Ontologies in Medicine) at the University of Leipzig. Unique selling properties of GFO are the following: it includes categories of objects (3D objects) as well as of processes (4D entities) and both are integrated into one coherent framework. GFO presents a multi-categorial approach by admitting universals, concepts, and symbol structures and their interrelations. GFO adopts categories pertaining to levels of reality, and it is designed to support interoperability by principles of ontological mapping and reduction. GFO contains several novel ontological modules, in particular, a module for functions and a module for roles. GFO is designed for applications, firstly in medical, biological, and biomedical areas, but also in the fields of economics and sociology.

Ontological Categories in GOL

General Ontological Language (GOL) is a formal framework for representing and building ontologies. The purpose of GOL is to provide a system of top-level ontologies which can be used as a basis for building domain-specific ontologies. The present paper gives an overview about the basic categories of the GOL-ontology. GOL is part of the work of the research group Ontologies in Medicine (Onto-Med) at the University of Leipzig which is based on the collaborative work of the Institute of Medical Informatics (IMISE) and the Institute for Computer Science (IfI). It represents work in progress toward a proposal for an integrated family of top-level ontologies and will be applied to several fields of medicine, in particular to the field of Clinical Trials.

A versioning and evolution framework for RDF knowledge bases

We present an approach to support the evolution of online, distributed, reusable, and extendable ontologies based on the RDF data model. The approach works on the basis of atomic changes, basically additions or deletions of statements to or from an RDF graph. Such atomic changes are aggregated to compound changes, resulting in a hierarchy of changes, thus facilitating the human reviewing process on various levels of detail. These derived compound changes may be annotated with meta-information and classified as ontology evolution patterns. The introduced ontology evolution patterns in conjunction with appropriate data migration algorithms enable the automatic migration of instance data in distributed environments.

Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies

BackgroundCurrent efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.ResultsWe have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach.ConclusionBiomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.

Relations as patterns: bridging the gap between OBO and OWL

BackgroundMost biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately.ResultsWe developed a method to provide definition patterns for relations between classes using OWL and describe a novel implementation of the RO based on this method. We implemented our extension in software that converts ontologies in the OBO Flatfile Format to OWL, and also provide a prototype to extract relational patterns from OWL ontologies using automated reasoning. The conversion software is freely available at http://bioonto.de/obo2owl, and can be accessed via a web interface.ConclusionsExplicitly defining relations permits their use in reasoning software and leads to a more flexible and powerful way of representing biomedical ontologies. Using the extended langua0067e and semantics avoids several mistakes commonly made in formalizing biomedical ontologies, and can be used to automatically detect inconsistencies. The use of our method enables the use of graph-based ontologies in OWL, and makes complex OWL ontologies accessible in a graph-based form. Thereby, our method provides the means to gradually move the representation of biomedical ontologies into formal knowledge representation languages that incorporates an explicit semantics. Our method facilitates the use of OWL-based software in the back-end while ontology curators may continue to develop ontologies with an OBO-style front-end.

Towards Ontological Foundations for UML Conceptual Models

UML class diagrams can be used as a language for expressing a conceptual model of a domain. We use the General Ontological Language (GOL) and its underlying upper level ontology, proposed in [1], to evaluate the ontological correctness of a conceptual UML class model and to develop guidelines for how the constructs of the UML should be used in conceptual modeling. In particular, we discuss the UML metaconcepts of classes and objects, power-types, association and aggregation/composition from an ontological point of view. We make some proposals of how to extend version 1.4 of the UML in order to obtain a more satisfactory treatment of aggregation.

A top-level ontology of functions and its application in the Open Biomedical Ontologies

MOTIVATION A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the communitys knowledge about biological functions. RESULTS We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

On the Foundations of UML as an Ontology Representation Language

There is a growing interest in the use of UML class diagrams as a modeling language to represent domain ontologies. In a series of papers [1,2] we have been using the General Ontological Language (GOL) and its underly-ing foundational ontology, proposed in [3,4], to evaluate the ontological correctness of a conceptual UML class model and to develop guidelines for how the constructs of the UML should be used in conceptual modeling and ontology representation. This paper can be seen as a continuation of this work, in which we focus on analyzing the UML metaconcepts of classes, attributes, data types and associations from an ontological point of view

Modeling surgical processes: A four-level translational approach

MOTIVATION The precise and formal specification of surgical interventions is a necessary requirement for many applications in surgery, including teaching and learning, quality assessment and evaluation, and computer-assisted surgery. Currently, surgical processes are modeled by following various approaches. This diversity lacks a commonly agreed-upon conceptual foundation and thus impedes the comparability, the interoperability, and the uniform interpretation of process data. OBJECTIVE However, it would be beneficial if scientific models, in the same context, shared a coherent conceptual and formal mathematical basis. Such a uniform foundation would simplify the acquisition and exchange of data, the transition and interpretation of study results, and the transfer and adaptation of methods and tools. Therefore, we propose a generic, formal framework for specifying surgical processes, which is presented together with its design methodology. METHODS The methodology follows a four-level translational approach and comprises an ontological foundation for the formal level that orients itself by linguistic theories. RESULTS A unifying framework for modeling surgical processes that is ontologically founded and formally and mathematically precise was developed. The expressive power and the unifying capacity of the presented framework are demonstrated by applying it to four contemporary approaches for surgical process modeling by using the common underlying formalization. CONCLUSIONS The presented four-level approach allows for capturing the knowledge of the surgical intervention formally. Natural language terms are consistently translated to an implementation level to support research fields where users express their expert knowledge about processes in natural language, but, in contrast to this, statistical analysis or data mining need to be performed based on mathematically formalized data sets. The availability of such a translational approach is a valuable extension for research regarding the operating room of the future.