The Potential of the Digital Anatomist Foundational Model for Assuring Consistency in UMLS Sources

Inconsistent anatomical concept representation can be identified in anatomy textbooks and hard copy term lists, as well as in UMLS source vocabularies and other controlled medical terminologies. In this report we select some examples of inconsistent representations of anatomical concepts, and illustrate how these inconsistencies can be explained and reconciled by the Digital Anatomist Foundational Model1. We use this process for gaining a measure of the validity of the logic-based Model

The Foundational Model of Anatomy Ontology

Anatomy is the structure of biological organisms. The term also denotes the scientific discipline devoted to the study of anatomical entities and the structural and developmental relations that obtain among these entities during the lifespan of an organism. Anatomical entities are the independent continuants of biomedical reality on which physiological and disease processes depend, and which, in response to etiological agents, can transform themselves into pathological entities. For these reasons, hard copy and in silico information resources in virtually all fields of biology and medicine, as a rule, make extensive reference to anatomical entities. Because of the lack of a generalizable, computable representation of anatomy, developers of computable terminologies and ontologies in clinical medicine and biomedical research represented anatomy from their own more or less divergent viewpoints. The resulting heterogeneity presents a formidable impediment to correlating human anatomy not only across computational resources but also with the anatomy of model organisms used in biomedical experimentation. The Foundational Model of Anatomy (FMA) is being developed to fill the need for a generalizable anatomy ontology, which can be used and adapted by any computer-based application that requires anatomical information. Moreover it is evolving into a standard reference for divergent views of anatomy and a template for representing the anatomy of animals. A distinction is made between the FMA ontology as a theory of anatomy and the implementation of this theory as the FMA artifact. In either sense of the term, the FMA is a spatial-structural ontology of the entities and relations which together form the phenotypic structure of the human organism at all biologically salient levels of granularity. Making use of explicit ontological principles and sound methods, it is designed to be understandable by human beings and navigable by computers. The FMA’s ontological structure provides for machine-based inference, enabling powerful computational tools of the future to reason with biomedical data

Symbolic modeling of structural relationships in the Foundational Model of Anatomy

The need for a sharable resource that can provide deep anatomical knowledge and support inference for biomedical applications has recently been the driving force in the creation of biomedical ontologies. Previous attempts at the symbolic representation of anatomical relationships necessary for such ontologies have been largely limited to general partonomy and class subsumption. We propose an ontology of anatomical relationships beyond class assignments and generic part-whole relations and illustrate the inheritance of structural attributes in the Digital Anatomist Foundational Model of Anatomy. Our purpose is to generate a symbolic model that accommodates all structural relationships and physical properties required to comprehensively and explicitly describe the physical organization of the human body

The Role of Definitions in Biomedical Concept Representation

The Foundational Model (FM) of anatomy, developed as an anatomical enhancement of UMLS, classifies anatomical entities in a structural context. Explicit definitions have played a critical role in the establishment of FM classes. Essential structural properties that distinguish a group of anatomical entities serve as the differentiae for defining classes. These, as well as other structural attributes, are introduced as template slots in Protege, a frame-based knowledge acquisition system, and are inherited by descendants of the class. A set of desiderata has evolved during the instantiation of the FM for formulating definitions. We contend that 1. these desiderata generalize to non-anatomical domains and 2. satisfying them in constituent vocabularies of UMLS would enhance the quality of information retrievable through UMLS

Representing Complexity in Part-Whole Relationships within the Foundational Model of Anatomy

The Foundational Model of Anatomy (FMA) is a frame-based ontology that represents declarative knowledge about the structural organization of the human body. Part-whole relationships play a particularly important role in this representation. In order to assure that knowledge-based applications relying on the FMA as a resource can reason about anatomy, we have modified and enhanced currently available schemes of meronymic relationships. We have introduced and defined distinct partitions for decomposing anatomical structures and attributed the part relationships in order to eliminate ambiguity and enhance specificity in the richness of meronymic relationships within the FMA

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models

Evolution of a Foundational Model of Physiology: Symbolic Representation for Functional Bioinformatics

We describe the need for a Foundational Model of Physiology (FMP) as a reference ontology for functional bioinformatics . The FMP is intended to support symbolic lookup, logical inference and mathematical analysis by integrating descriptive, qualitative and quantitative functional knowledge. The FMP will serve as a symbolic representation of biological functions initially pertaining to human physiology and ultimately extensible to other species. We describe the evolving architecture of the FMP, which is based on the ontological principles of the BioD biological description language and the Foundational Model of Anatomy (FMA)

Anatomical information science

The Foundational Model of Anatomy (FMA) is a map of the human body. Like maps of other sorts – including the map-like representations we find in familiar anatomical atlases – it is a representation of a certain portion of spatial reality as it exists at a certain (idealized) instant of time. But unlike other maps, the FMA comes in the form of a sophisticated ontology of its objectdomain, comprising some 1.5 million statements of anatomical relations among some 70,000 anatomical kinds. It is further distinguished from other maps in that it represents not some specific portion of spatial reality (say: Leeds in 1996), but rather the generalized or idealized spatial reality associated with a generalized or idealized human being at some generalized or idealized instant of time. It will be our concern in what follows to outline the approach to ontology that is represented by the FMA and to argue that it can serve as the basis for a new type of anatomical information science. We also draw some implications for our understanding of spatial reasoning and spatial ontologies in general

Challenges in Reconciling Different Views of Neuroanatomy in a Reference Ontology of Anatomy

A fundamental requirement for integrating neuroscience data is a well-structured ontology that can incorporate, accommodate and reconcile different neuroanatomical views. Here we describe the challenges in creating such ontology, and, because of its principled design, illustrate the potential of the Foundational Model of Anatomy to be that ontology

Relationship auditing of the FMA ontology

The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relation- ship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection