Systematic identification and correction of spelling errors in the Foundational Model of Anatomy

We describe a method for automating the detection and correction of spelling errors in the Foundational Model of Anatomy (FMA). The FMA was tokenized into 4893 distinct words; misspellings were identified and corrected using the National Library of Medicine’s SPECIALIST GSpell Spelling Suggestion API. We identified 43 errors occurring in 97 terms, and 6 words of questionable or inconsistent spelling occurring in 26 terms. These errors are replicated in other reference terminologies that use the FMA. Our approach may be useful as part of a quality assurance process for other large-scale biomedical knowledge resources

Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets

The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform, and new ontologies being created, on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved in the future

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

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

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

Expressing OWL axioms by English sentences: dubious in theory, feasible in practice

With OWL (Web Ontology Language) established as a standard for encoding ontologies on the Semantic Web, interest has begun to focus on the task of verbalising OWL code in controlled English (or other natural language). Current approaches to this task assume that axioms in OWL can be mapped to sentences in English. We examine three potential problems with this approach (concerning logical sophistication, information structure, and size), and show that although these could in theory lead to insuperable difficulties, in practice they seldom arise, because ontology developers use OWL in ways that favour a transparent mapping. This result is evidenced by an analysis of patterns from a corpus of over 600,000 axioms in about 200 ontologies

MIREOT: the Minimum Information to Reference an External Ontology Term

While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions

Modularization for the Cell Ontology

One of the premises of the OBO Foundry is that development of an orthogonal set of ontologies will increase domain expert contributions and logical interoperability, and decrease maintenance workload. For these reasons, the Cell Ontology (CL) is being re-engineered. This process requires the extraction of sub-modules from existing OBO ontologies, which presents a number of practical engineering challenges. These extracted modules may be intended to cover a narrow or a broad set of species. In addition, applications and resources that make use of the Cell Ontology have particular modularization requirements, such as the ability to extract custom subsets or unions of the Cell Ontology with other OBO ontologies. These extracted modules may be intended to cover a narrow or a broad set of species, which presents unique complications.

We discuss some of these requirements, and present our progress towards a customizable simple-to-use modularization tool that leverages existing OWL-based tools and opens up their use for the CL and other ontologies