A unified framework for building ontological theories with application and testing in the field of clinical trials

The objective of this research programme is to contribute to the establishment of the emerging science of Formal Ontology in Information Systems via a collaborative project involving researchers from a range of disciplines including philosophy, logic, computer science, linguistics, and the medical sciences. The re­searchers will work together on the construction of a unified formal ontology, which means: a general framework for the construction of ontological theories in specific domains. The framework will be constructed using the axiomatic-deductive method of modern formal ontology. It will be tested via a series of applications relating to on-going work in Leipzig on medical taxonomies and data dictionaries in the context of clinical trials. This will lead to the production of a domain-specific ontology which is designed to serve as a basis for applications in the medical field

Solving Practical Reasoning Poblems with Extended Disjunctive Logic Programming

We present a definition of stable generated models for extended generalized logic programs (EGLP) which a) subsumes the definition of the answer set semantics for extended normal logic programs [GL91]; and b) does not refer to negation-as-failure by allowing for arbitrary quantifier free formulas in the body and in the head of as rule (i.e. does not depend on the presence of any specific connective, nor any specific syntax of rules). We show how to solve classical ATP problems in the framework of extended disjunctive logic programming (EDLP) where neither Contraposition nor the Law of the Excluded Middle are admitted principles of inference. Besides being able to solve classical ATP problems in a monotonic reasoning mode, EDLP can as well treat commonsense reasoning problems by employing its intrinsic nonmonotonic inference capabilities based on stable generated models. EDLP thus proves itself as a general-purpose AI reasoning system

OntoStudyEdit: a new approach for ontology-based representation and management of metadata in clinical and epidemiological research

Background: The specification of metadata in clinical and epidemiological study projects absorbs significant expense. The validity and quality of the collected data depend heavily on the precise and semantical correct representation of their metadata. In various research organizations, which are planning and coordinating studies, the required metadata are specified differently, depending on many conditions, e.g., on the used study management software. The latter does not always meet the needs of a particular research organization, e.g., with respect to the relevant metadata attributes and structuring possibilities. Methods: The objective of the research, set forth in this paper, is the development of a new approach for ontology-based representation and management of metadata. The basic features of this approach are demonstrated by the software tool OntoStudyEdit (OSE). The OSE is designed and developed according to the three ontology method. This method for developing software is based on the interactions of three different kinds of ontologies: a task ontology, a domain ontology and a top-level ontology. Results: The OSE can be easily adapted to different requirements, and it supports an ontologically founded representation and efficient management of metadata. The metadata specifications can by imported from various sources; they can be edited with the OSE, and they can be exported in/to several formats, which are used, e.g., by different study management software. Conclusions: Advantages of this approach are the adaptability of the OSE by integrating suitable domain ontologies, the ontological specification of mappings between the import/export formats and the DO, the specification of the study metadata in a uniform manner and its reuse in different research projects, and an intuitive data entry for non-expert users

Stable Semantics of Temporal Deductive Databases

We define a preferential semantics based on stable generated models for a very general class of temporal deductive databases. We allow two kinds of temporal information to be represented and queried: timepoint and timestamp formulas, and show how each of them can be translated into the other. Because of their generality, our formalism and our semantics can serve as a basis for comparing and extending other temporal deductive database frameworks

The ontology of biological sequences.

BACKGROUND: Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike. RESULTS: We provide both an ontological analysis of biological sequences and a formal representation that can be used in knowledge-based applications and other ontologies. We distinguish three distinct kinds of entities that can be referred to as "biological sequence": chains of molecules, syntactic representations such as those in biological databases, and the abstract information-bearing entities. For use in knowledge-based applications and inclusion in biomedical ontologies, we implemented the developed axiom system for use in automated theorem proving. CONCLUSION: Axioms are necessary to achieve the main goal of ontologies: to formally specify the meaning of terms used within a domain. The axiom system for the ontology of biological sequences is the first elaborate axiom system for an OBO Foundry ontology and can serve as starting point for the development of more formal ontologies and ultimately of knowledge-based applications

An ontologically founded architecture for information systems in clinical and epidemiological research

This paper presents an ontologically founded basic architecture for information systems, which are intended to capture, represent, and maintain metadata for various domains of clinical and epidemiological research. Clinical trials exhibit an important basis for clinical research, and the accurate specification of metadata and their documentation and application in clinical and epidemiological study projects represents a significant expense in the project preparation and has a relevant impact on the value and quality of these studies

Towards Improving Phenotype Representation in OWL

BACKGROUND: Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize human diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected entities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies. METHODS: We analyze formalizations of complex phenotype descriptions in the Web Ontology Language (OWL) that are based on the EQ model, identify several representational challenges and analyze potential solutions to address these challenges. RESULTS: In particular, we suggest a novel, role-based approach to represent relational qualities such as concentration of iron in spleen, discuss its ontological foundation in the General Formal Ontology (GFO) and evaluate its representation in OWL and the benefits it can bring to the representation of phenotype annotations. CONCLUSION: Our analysis of OWL-based representations of phenotypes can contribute to improving consistency and expressiveness of formal phenotype descriptions

OBML - Ontologies in Biomedicine and Life Sciences

The OBML 2010 workshop, held at the University of Mannheim on September 9-10, 2010, is the 2(nd) in a series of meetings organized by the Working Group “Ontologies in Biomedicine and Life Sciences” of the German Society of Computer Science (GI) and the German Society of Medical Informatics, Biometry and Epidemiology (GMDS). Integrating, processing and applying the rapidly expanding information generated in the life sciences — from public health to clinical care and molecular biology — is one of the most challenging problems that research in these fields is facing today. As the amounts of experimental data, clinical information and scientific knowledge increase, there is a growing need to promote interoperability of these resources, support formal analyses, and to pre-process knowledge for further use in problem solving and hypothesis formulation. The OBML workshop series pursues the aim of gathering scientists who research topics related to life science ontologies, to exchange ideas, discuss new results and establish relationships. The OBML group promotes the collaboration between ontologists, computer scientists, bio-informaticians and applied logicians, as well as the cooperation with physicians, biologists, biochemists and biometricians, and supports the establishment of this new discipline in research and teaching. Research topics of OBML 2010 included medical informatics, Semantic Web applications, formal ontology, bio-ontologies, knowledge representation as well as the wide range of applications of biomedical ontologies to science and medicine. A total of 14 papers were presented, and from these we selected four manuscripts for inclusion in this special issue. An interdisciplinary audience from all areas related to biomedical ontologies attended OBML 2010. In the future, OBML will continue as an annual meeting that aims to bridge the gap between theory and application of ontologies in the life sciences. The next event emphasizes the special topic of the ontology of phenotypes, in Berlin, Germany on October 6-7, 2011