Overview of systems of subject headings in the field of animal sciences, production and protection in selected agricultural databases | Pregled sistemov predmetnih oznak za podrocje znanosti o zivalih v izbranih biotehniskih podatkovnih zbirkah
2009
Bartol, T., Ljubljana Univ. (Slovenia). Biotechnical Fac., Agronomy Dept.
The article tackles three most important agricultural databases (DB) Agris, Agricola and CAB Abstracts (CABA), produced by FAO, NAL, CABI, with regard to subject headings related to animal sciences, production, protection or health-related veterinary issues. The initial part reviews different approaches to DB and respective indexing and classification schemes. Concepts, such as ontologies and metadata, are presented. Animal, aquatic sciences and fisheries subject categories are shown. Inter-database differences are addressed, e.g. employment of different names for similar concepts. Tree-structures, indexing systems of thesaurus-based keywords-descriptors (DE) are analyzed with emphasis on narrow and broader terms, preferential terms (non-descriptors) and related terms. There exist different tree-structures, depending either on production or taxonomy. CABA exhibits hierarchically the most complex tree with regard to taxonomy. In different DB, keywords are used in relations DE vs. non-DE vs. related terms. Mapping of a concept depends on particular DB. Subject headings are assigned by information specialists, indexers, thus possessing an important degree of subjective choice. Original web-based thesauri screenshots are presented. Emphasis is placed on multilingual functionality of Agrovoc. Portals or search platforms are tackled with regard to retrieval, search syntax, priority, phrases, Boolean logic, wildcards and truncation. Inter-database differences affect retrieval precision, recall, and noise. The complex schemas, subject trees, and headings can sometimes account for a less successful retrieval because they may be too sophisticated and can remain disregarded by users. End-users should acquire better expertise in order to use more effectively the existing information systems and databases.
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