A formal theory for spatial representation and reasoning in biomedical ontologies

Department of Philosophy, New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, 135 Park Hall, Buffalo, NY 14260, USA.
Artificial Intelligence in Medicine (Impact Factor: 2.02). 02/2006; 36(1):1-27. DOI: 10.1016/j.artmed.2005.07.004
Source: PubMed


The objective of this paper is to demonstrate how a formal spatial theory can be used as an important tool for disambiguating the spatial information embodied in biomedical ontologies and for enhancing their automatic reasoning capabilities.
This paper presents a formal theory of parthood and location relations among individuals, called Basic Inclusion Theory (BIT). Since biomedical ontologies are comprised of assertions about classes of individuals (rather than assertions about individuals), we define parthood and location relations among classes in the extended theory Basic Inclusion Theory for Classes (BIT+Cl). We then demonstrate the usefulness of this formal theory for making the logical structure of spatial information more precise in two ontologies concerned with human anatomy: the Foundational Model of Anatomy (FMA) and GALEN.
We find that in both the FMA and GALEN, class-level spatial relations with different logical properties are not always explicitly distinguished. As a result, the spatial information included in these biomedical ontologies is often ambiguous and the possibilities for implementing consistent automatic reasoning within or across ontologies are limited.
Precise formal characterizations of all spatial relations assumed by a biomedical ontology are necessary to ensure that the information embodied in the ontology can be fully and coherently utilized in a computational environment. This paper can be seen as an important beginning step toward achieving this goal, but much more work along these lines is required.

Download full-text


Available from: Thomas Bittner, Sep 08, 2014
  • Source
    • "Because of their generality and significance, spatial relations have received particular attention. Work in this area includes that of Smith et al. (2005), Donnelly et al. (2006) and Bittner (2009). The work of Rosse et al. (2003) on the development of a Foundational Model of Anatomy (FMA) should also be mentioned. "

    Spatial Information Theory, 11th International Conference, COSIT 2013; 01/2013
  • Source
    • "There are some more specific relations (or location with additional conditions) that could be defined. Based on the Loc-In(x, y) and ~Oxy definitions (localization and overlapping relations) from [4] "
    [Show abstract] [Hide abstract]
    ABSTRACT: The most emphasized way of representing the knowledge from domains of real world is the ontology, which is undoubtedly the trend of current years. In medical applications such as breast cancer grading, formal ontological representation is of high relevance due to the importance of grading in the prognosis process and to the semantic gap. However, since the representation deals with histopathology images, a spatial representation and reasoning is required. We extend our breast cancer grading ontology with spatial representation and spatial reasoning support and we show how it helps in overcoming the inconsistencies and ambiguities. The ontology is integrated in a cognitive virtual microscope platform guiding the image exploration and assisting the grading process.
    Computational Cybernetics and Technical Informatics (ICCC-CONTI), 2010 International Joint Conference on; 06/2010
  • Source
    • "They do not provide an explicit and operational mathematical formalism for all the types of spatial concepts and spatial relations. For instance, in medicine, these ontologies are often restricted to concepts from the mereology theory [29]. These concepts are fundamental for spatial relations ontologies [66], and these ontologies are useful for qualitative and symbolic reasoning on topological relations, but there is still a gap to fill before using them for image interpretation. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.
    Fuzzy Sets and Systems 08/2008; 159(15-159):1929-1951. DOI:10.1016/j.fss.2008.02.011 · 1.99 Impact Factor
Show more