Mapping AGROVOC and the Chinese Agricultural Thesaurus: Definitions, tools, procedures

Abstract

This paper describes the procedures for a concept-based mapping of two agricultural thesauri, the multilingual AGROVOC, created and maintained by the Food and Agricultural Organization, and the bilingual Chinese Agricultural Thesaurus, created and maintained by the Chinese Academy of Agricultural Science. Conducted under the auspices of FAO's Agricultural Ontology Service, the mapping project aims to extend AGROVOC with an additional set of perspectives on the agricultural domains, enrich its domain and language coverage, and to make use of AGROVOC as a common data model for data exchange among a wide range of multilingual repositories within agriculture.

A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes

“…An example of manual CLOM is discussed in [28], where an English thesaurus: AGROVOC 17 (developed by the FAO containing a set of agricultural vocabularies) is mapped to a Chinese thesaurus: CAT 18 (Chinese Agricultural Ontology, developed by the Chinese Academy of Agricultural Science). The thesauri are loaded in the Protégé 19 editor, and segments of the thesauri are assigned to groups of terminologists to generate mappings.…”

“…Current approaches to CLOM can be grouped into five categories: manual CLOM [28], corpus-based CLOM [40], CLOM via linguistic enrichment [41], CLOM via indirect alignment [42] and translation-based CLOM [10,43,8]. Each category is discussed next.…”

“…In agriculture, the Food and Agriculture Organisation (FAO) provides reference standards for defining and structuring agricultural terminologies. Since all FAO official documents must be made available in five official languages including Arabic, Chinese, English, French and Spanish, a great deal of research has been carried out on the translations of large multilingual agricultural thesauri [26], mapping methodologies for them [27,28] and a definition of requirements to improve the interoperability between these multilingual resources [29]. In education, the Bologna declaration has introduced an ontologybased framework for qualification recognition [30] across the European Union (EU).…”

Reducing semantic complexity in distributed digital libraries

“…Most vocabularies in the project have been related bilaterally; that is, there is a cross-concordance relating terms from vocabulary A to vocabulary B as well as a cross-concordance relating terms from vocabulary B to vocabulary A (note: bilateral relations are not necessarily symmetrical). Other definitions and examples of crosswalks between controlled vocabularies exist in an international context (see overview in Zeng/Chan, 2004;Vizine-Goetz et al, 2004;Liang and Sini, 2006).…”

Resolving controlled vocabulary in DITA markup: a case example in agroforestry

“…ontologymatching.org/2007, with the Chinese Agricultural Thesaurus (Liang and Sini, 2006) as well as with other thesauri such as GEMET or EUROVOC as part of the effort to publish the AGROVOC thesaurus as Linked Data (Morshed and Sini, 2009;Morshed et al, 2010Morshed et al, , 2011. These exercises conclude that automatic mapping is certainly helpful and effective, in particular when mapping a large corpus as well as aligning thesauri in the process of publishing Linked Data.…”