Abstraction networks for terminologies: Supporting management of “big knowledge”

• The “big knowledge” challenge is managing terminologies with large concept networks.
• An abstraction network denotes a high-level compact network for a terminology.
• Characteristics and the derivation of abstraction networks are discussed.
• Example abstraction networks for some leading terminologies are surveyed.
• Meta-abstraction networks, representing further layers of abstraction, are presented.

ObjectiveTerminologies and terminological systems have assumed important roles in many medical information processing environments, giving rise to the “big knowledge” challenge when terminological content comprises tens of thousands to millions of concepts arranged in a tangled web of relationships. Use and maintenance of knowledge structures on that scale can be daunting. The notion of abstraction network is presented as a means of facilitating the usability, comprehensibility, visualization, and quality assurance of terminologies.Methods and materialsAn abstraction network overlays a terminology's underlying network structure at a higher level of abstraction. In particular, it provides a more compact view of the terminology's content, avoiding the display of minutiae. General abstraction network characteristics are discussed. Moreover, the notion of meta-abstraction network, existing at an even higher level of abstraction than a typical abstraction network, is described for cases where even the abstraction network itself represents a case of “big knowledge.” Various features in the design of abstraction networks are demonstrated in a methodological survey of some existing abstraction networks previously developed and deployed for a variety of terminologies.ResultsThe applicability of the general abstraction-network framework is shown through use-cases of various terminologies, including the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), the Medical Entities Dictionary (MED), and the Unified Medical Language System (UMLS). Important characteristics of the surveyed abstraction networks are provided, e.g., the magnitude of the respective size reduction referred to as the abstraction ratio. Specific benefits of these alternative terminology-network views, particularly their use in terminology quality assurance, are discussed. Examples of meta-abstraction networks are presented.ConclusionsThe “big knowledge” challenge constitutes the use and maintenance of terminological structures that comprise tens of thousands to millions of concepts and their attendant complexity. The notion of abstraction network has been introduced as a tool in helping to overcome this challenge, thus enhancing the usefulness of terminologies. Abstraction networks have been shown to be applicable to a variety of existing biomedical terminologies, and these alternative structural views hold promise for future expanded use with additional terminologies.

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