Ontology

An ontology is an organized and coherent structure of domain knowledge, represented in a formal, logic-based language, which reduces terminological heterogeneity, facilitates data interoperability and enables knowledge discovery (Cimino and Zhu, 2006) (Bodenreider, 2010, Bodenreider, 2008).

Ontologies can be used seamlessly as components of information systems and data management tools. They represent not only the concepts/classes used in scientific work, but just as importantly, the relationships between the concepts/classes.

For example, the term “generalized 3–4 hertz spike wave” is an electroencephalographic description of an ictal or inter-ictal epileptic phenomenon. The term however, is also used in literature to describe or typify a group of genetic generalized epilepsies. These epilepsies may be linked not just through electrophysiological similarities but through phenotype (e.g. typical absence seizures) and conceivably genotype. In large database or electronic medical record query management, definition of such terms and linking their relationships can allow tailored data mining to “lump” or “split” according to the clinician or researcher’s questions.

Ontologies have thus become a central component in biomedical information management. Familiar in their role in supporting application menus similar to those generated by MeSH Headings, ontologies are also becoming valuable for designing intuitive and novel interfaces to query, access, and visualize large sets of distributed biomedical datasets (Zhang et al., 2010a).

Challenge 1 – Enabling Terminology and Classification All have the common goal of providing usable, reliable, reproducible and standardized epilepsy diagnoses and terminologies.

ILAE classifications, the ILAE’s recommendations on standardization of epidemiological studies and surveillance in epilepsy (Thurman et al., 2011)

The NINDS Common Data Elements (CDE) project (Loring et al., 2011)

Challenge 2 – Incorporation of Existing Terminological Resources

Clinical and experimental epilepsy encompasses a wide range of sub-domains. Any epilepsy informatics resource has to incorporate epilepsy terminology extensively as well as include related terminologies that do not directly fall under the remit of epilepsy but are integral to practice and research.

Examples: The Gene Ontology (GO) (Ashburner, 2000), anatomical features in the Foundational Model of Anatomy (FMA) (Rosse, 2003), and electrophysiological concepts in the Neural ElectroMagnetic Ontologies (NEMO) (Dou, 2007).

Challenge 3 – Technological Challenges of Multi-modal Epilepsy Data The International Epilepsy Electrophysiology Portal of the International Collaborative Seizure-Prediction Group is a model for such collaboration (http://braintrust.seas.upenn.edu)

Ontology is increasingly recognized as the key to drive data capture, data search and query, and data integration in research involving alphanumeric as well as electrophysiological signal data.

Road Map

Development of an epilepsy ontology involves several sequential steps requiring close collaboration between informatics experts and epilepsy domain experts.

There is firstly an exhaustive and comprehensive listing of controlled vocabulary using terms from all existing and proposed classification systems, ICD systems, NINDS CDE and epilepsy literature.

standard set of ontological relationships is then used to link together all these terms in a formal logic language (e.g. Web Ontology Language (OWL) that the informatician uses to create a rich epilepsy domain ontological structure that can be used for databases, electronic medical records, diagnostic manuals and other digital applications with diverse utility.