Title :
LITCHI: knowledge integrity testing for taxonomic databases
Author :
Sutherland, Iain ; Embury, Suzanne M. ; Jones, Andrew C. ; Gray, W. Alex ; White, Richard J. ; Robinson, John S. ; Bisby, Frank A. ; Brandt, Sue M.
Author_Institution :
Dept. of Comput. Sci., Cardiff Univ., UK
Abstract :
Summary form only given. The LITCHI project (Logic-based Integration of Taxonomic Conflicts in Heterogeneous Information Systems) aims to develop software to enable the automated detection and, where possible, resolution of conflicts in taxonomic checklists. A taxonomic checklist is a list of the names of species (and other taxa) used within a particular biological database. Since species names are typically used to gain access to data within biological databases, checklists provide a concise representation of the data values that can act as keys when querying such databases. More importantly, species names are also typically used as the join attribute when integrating several biological databases. However, naming of species is a subjective activity, and different scientific communities will have different ideas about the names that should be used for particular species. These conflicts of opinion arise as a result of the subjective nature of the classification process and geographical or historical differences in background knowledge. Some communities may use different names for the same species, while other groups of scientists may use the same name to refer to different species. Often, there is no one right naming scheme, but some consistent set of names must be used if biological databases are to be integrated. Therefore, there is a real need for a tool which will assist biologists in the integration of checklists, prior to the integration of species databases, so that these differences of opinion can be resolved
Keywords :
biology computing; classification; data integrity; distributed databases; scientific information systems; LITCHI; Logic-based Integration of Taxonomic Conflicts in Heterogeneous Information Systems; automated detection; biological database; classification process; data values; database querying; join attribute; knowledge integrity testing; naming scheme; species databases; species names; subjective nature; taxonomic checklists; taxonomic databases; Biodiversity; Biological system modeling; Biology; Computer science; Databases; Informatics; Laboratories; Plants (biology); Systematics; Testing;
Conference_Titel :
Scientific and Statistical Database Management, 1999. Eleventh International Conference on
Conference_Location :
Cleveland, OH
Print_ISBN :
0-7695-0046-3
DOI :
10.1109/SSDM.1999.787645