MARC 主機 00000nam  2200349   4500 
001    AAI3444973 
005    20111017084415.5 
008    111017s2011    ||||||||||||||||| ||eng d 
020    9781124532097 
035    (UMI)AAI3444973 
040    UMI|cUMI 
100 1  Sharp, Mark E 
245 10 Dimensions of drug information 
300    237 p 
500    Source: Dissertation Abstracts International, Volume: 72-
       05, Section: A, page:  
500    Adviser: Nicholas Belkin 
502    Thesis (Ph.D.)--Rutgers The State University of New Jersey
       - New Brunswick, 2011 
520    The high number, heterogeneity, and inadequate integration
       of drug information resources constitute barriers to many 
       drug information usage scenarios. In the biomedical domain
       there is a rich legacy of knowledge representation in 
       ontology-like structures that allows us to connect this 
       problem both to the very mature field of library and 
       information science classification research and the very 
       new field of ontology matching/merging (OM). We argue for 
       a broad view of OM that makes room not only for the "pre-
       formal" phase/type of multi-ontology integration 
       exemplified by RxNorm and the UMLS Metathesaurus, but also
       for an even earlier phase/type when "What is there?" in a 
       domain has to deal with implicit and poorly structured 
       "ontologies" that barely qualify as such. Such is the case
       in the drug domain. We introduce dimensions of drug 
       information  as an approach to early, pre-formal OM in the
       drug domain that draws inspiration and incorporates 
       principles from facet analysis, domain analysis, and 
       Semantic Web research on linked data and mashups. By 
       surveying 23 publically available drug information 
       resources, we identified 39 dimensions relevant to four 
       drug (sub)domains - pharmacy, chemistry, biology, and 
       clinical medicine - and mapped them to the resources An 
       arbitrary four-domain, monohierarchical classification of 
       the dimensions produced, by extension, a reasonable four-
       domain resource classification. Correspondence analysis 
       and hierarchical cluster analysis also produced evidence 
       of its partial validity. Detailed analysis of information 
       on nine parent drug compounds from 15 resources refined 
       this high-level dimensional mapping and identified 
       hundreds of subdimensions which could be expressed as a 
       six-level hierarchy. Based on these dimensions, we 
       integrated this information in an experimental database 
       and showed that it was useful (1) as a training set for 
       automating the normalization of additional raw data from 
       the same 15 sources, bringing the important goal of 
       building an integrated, comprehensive (all drugs) database
       within reach, and (2) for satisfying a variety of use 
       cases, some quite complex, derived from published 
       literature representing the user types corresponding to 
       our domain focus 
590    School code: 0190 
650  4 Library Science 
650  4 Health Sciences, Pharmacy 
650  4 Information Science 
690    0399 
690    0572 
690    0723 
710 2  Rutgers The State University of New Jersey - New 
       Brunswick.|bGraduate School - New Brunswick 
773 0  |tDissertation Abstracts International|g72-05A 
856 40 |u