TY - GEN
T1 - Comparing small graph retrieval performance for ontology concepts in medical texts
AU - Schlegel, Daniel R.
AU - Bona, Jonathan P.
AU - Elkin, Peter L.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84977508979
U2 - 10.1007/978-3-319-41576-5_3
DO - 10.1007/978-3-319-41576-5_3
M3 - Conference contribution
AN - SCOPUS:84977508979
SN - 9783319415758
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 32
EP - 44
BT - Biomedical Data Management and Graph Online Querying - VLDB 2015 Workshops, Big-O(Q) and DMAH, Revised Selected Papers
A2 - Khan, Arijit
A2 - Luo, Gang
A2 - Weng, Chunhua
A2 - Wang, Fusheng
A2 - Mitra, Prasenjit
A2 - Yu, Cong
PB - Springer Verlag
T2 - 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015
Y2 - 31 August 2015 through 4 September 2015
ER -