Comparing Small Graph Retrieval Performance for Ontology Concepts in Medical Texts

  • Daniel R. SchlegelEmail author
  • Jonathan P. Bona
  • Peter L. Elkin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9579)


Some terminologies and ontologies, such as SNOMED CT, allow for post–coordinated as well as pre-coordinated expressions. Post–coordinated expressions are, essentially, small segments of the terminology graphs. Compositional expressions add logical and linguistic relations to the standard technique of post-coordination. In indexing medical text, many instances of compositional expressions must be stored, and in performing retrieval on that index, entire compositional expressions and sub-parts of those expressions must be searched. The problem becomes a small graph query against a large collection of small graphs. This is further complicated by the need to also find sub-graphs from a collection of small graphs. In previous systems using compositional expressions, such as iNLP, the index was stored in a relational database. We compare retrieval characteristics of relational databases, triplestores, and general graph databases to determine which is most efficient for the task at hand.


Small Graphs General Graph Database Triplestore Medical Text Indexer Compositional Expressions 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel R. Schlegel
    • 1
    Email author
  • Jonathan P. Bona
    • 1
  • Peter L. Elkin
    • 1
  1. 1.Department of Biomedical InformaticsUniversity at BuffaloBuffaloUSA

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