AGROVOC Thesaurus Research Articles

Methodological approaches to creating a Russian-language version of the AGROVOC thesaurus

The article considers the methodological principles used in the creation of the Russian-language version of AGROVOC, the multilingual thesaurus of the international information system for agriculture and food of the UN FAO AGRIS. The stages and content of the works on the creation of the Russian version are considered. The results of the work done are presented: studying the lexical composition of the AGROVOC thesaurus; comparative analysis of approaches to assigning the status of a term in the dictionary entries of national thesauri; researching in solving issues of lexical polysemanticism and disambiguate in the AGROVOC thesaurus; analysis of the meaning of synonymy relations; studying the functions of the “Note” field. When creating the Russian-language version, the methodology of translating terms directly by agricultural specialists was used, ensuring thereby a high-quality selection of equivalents to English terms of the English-language version of the thesaurus and with the minimal revision needed. The work started with finding equivalents for the descriptors, then proceeded to translating synonyms. Word combinations and relators were used to eliminate lexical polysemanticism and disambiguate of terms. When selecting an equivalent to an English AGROVOC term, the developers of the Russian-language version were guided by the following basic rules: the undesirability of literal translation; the differentiation of concepts and the use of relators to emendate terms; artificial formation of equivalents when accurate translation was impossible. For precise selection of the Russian equivalent of a term of the thesaurus we analysed all its relationships with the other terms -broader and narrower, synonyms, associated terms, as well as the text of notes, which could indicate limitations of the scope of use of the term, emendation of a concept, etc.

Modelling Biodiversity Linked Data: Pragmatism May Narrow Future Opportunities

As the biodiversity community increasingly adopts Semantic Web (SW) standards to represent taxonomic registers, trait banks or museum collections, some questions come up relentlessly: How to model the data? For what goals? Can the same model fulfill different goals?So far, the community has mostly considered the SW standards through their most salient manifestation: the Web of Linked Data (Heath and Bizer 2011). Indeed, the 5-star Linked Data principles are geared towards the building of a large, distributed knowledge graph that may successfully fulfill biodiversity’s need for interoperability and data integration. However, the SW addresses a much broader set of problems involving automatic reasoning. For instance, reasoners can exploit ontological knowledge to improve query answering, leverage class definitions to infer class subsumption relationships, or classify individualsi.e.compute instance relationships between individuals and classes by applying reasoning techniques on class definitions and instance descriptions (Shearer et al. 2008).Whether a "thing" should be modelled as a class or a class instance has been debated at length in the SW community, and the answer is often a matter of perspective. In the context of taxonomic registers for example, the NCBI Organismal Classification (Federhen 2012) and Vertebrate Taxonomy Ontology (Midford et al. 2013) represent taxa as classes in the Ontology Web Language (OWL). By contrast, other initiatives represent taxa as instances of various classes,e.g.the SKOS Concept class (skos:Concept) in the AGROVOC thesaurus (Caracciolo et al. 2013) (we speak of the instances asSKOS concepts), the Darwin Core taxon class (dwc:Taxon)in Encyclopedia of Life (Parr et al. 2016), or classes depicting taxonomic ranks in GeoSpecies, DBpedia and the BBC Wildlife Ontology. Such modelling discrepancies impede linking congruent taxa throughout taxonomic registers. Indeed, one can state the equivalence between two classes (withowl:equivalentClass) or two class instances (withowl:sameAs, skos:exactMatch, etc.), but good practices discourage the alignment of classes with class instances (Baader et al. 2003).Recently, Darwin Core's popularity has fostered the modeling of taxa as instances of classdwc:Taxon(Senderov et al. 2018, Parr et al. 2016). In this context, pragmatism may incline a Linked Data provider to comply with this majority trend to ensure maximum interlinking. Although technically and conceptually valid, this choice entails certain drawbacks. First, considering a taxon only as a an instance misses the fact that it is a set of biological individuals with common characteristics. An OWL class exactly captures this semantics through the set of necessary and sufficient conditions that an individual must meet to be a class member. In turn, an OWL reasoner can leverage this knowledge to perform query answering, compute subsumption or instance relationships. By contrast, taxa depicted by class instances are notdefinedbutdescribedby stating their properties. Hence the second drawback: unless we develop bespoke reasoners, there is not much a standard OWL reasoner can deduce from instances.Yet, some works have demonstrated the effectiveness of logic representation and reasoning capabilities,e.g.computing the alignments of two primate classifications (Franz et al. 2016) using generic reasoners that nevertheless require proprietary input formats. OWL reasoners are typically designed to solve such classification problems. They may leverage taxonomic ontologies to compute alignments with other ontologies or apply reasoning to individuals' properties to infer their species. Hence, pragmatically following the instance-based approach may indeed maximize interlinking in the short term, but bears the risk of denying ourselves potentially desirable use cases in the longer term. We believe that developing class-based ontologies for biodiversity should help leverage the SW’s extensive theoretical and practical works to tackle a variety of use cases that so far have been addressed with bespoke solutions.

Evaluating the concept specialization distance from an end-user perspective

PurposeThe common understanding of generalization/specialization relations assumes the relation to be equally strong between a classifier and any of its related classifiers and also at every level of the hierarchy. Assigning a grade of relative distance to represent the level of similarity between the related pairs of classifiers could correct this situation, which has been considered as an oversimplification of the psychological account of the real-world relations. The paper aims to discuss these issues.Design/methodology/approachThe evaluation followed an end-user perspective. In order to obtain a consistent data set of specialization distances, a group of 21 persons was asked to assign values to a set of relations from a selection of terms from the AGROVOC thesaurus. Then two sets of representations of the relations between the terms were built, one according to the calculated concept of specialization weights and the other one following the original order of the thesaurus. In total, 40 persons were asked to choose between the two sets following an A/B test-like experiment. Finally, short interviews were carried out after the test to inquiry about their decisions.FindingsThe results show that the use of this information could be a valuable tool for search and information retrieval purposes and for the visual representation of knowledge organization systems (KOS). Furthermore, the methodology followed in the study turned out to be useful for detecting inconsistencies in the thesaurus and could thus be used for quality control and optimization of the hierarchical relations.Originality/valueThe use of this relative distance information, namely, “concept specialization distance,” has been proposed mainly at a theoretical level. In the current experiment, the authors evaluate the potential use of this information from an end-user perspective, not only for text-based interfaces but also its application for the visual representation of KOS. Finally, the methodology followed for the elaboration of the concept specialization distance data set showed potential for detecting possible inconsistencies in KOS.

Exploiting semantics for searching agricultural bibliographic data

Filtering and search mechanisms which permit to identify key bibliographic references are fundamental for researchers. In this paper we propose a fully automatic and semantic method for filtering/searching bibliographic data, which allows users to look for information by specifying simple keyword queries or document queries, i.e. by simply submitting existing documents to the system. The limitations of standard techniques, based on either syntactical text search and on manually assigned descriptors, are overcome by considering the semantics intrinsically associated to the document/query terms; to this aim, we exploit different kinds of external knowledge sources (both general and specific domain dictionaries or thesauri). The proposed techniques have been developed and successfully tested for agricultural bibliographic data, which play a central role to enable researchers and policy makers to retrieve related agricultural and scientific information by using the AGROVOC thesaurus.

Migrating bibliographic datasets to the Semantic Web: The AGRIS case

AGRIS is among the most comprehensive online collections of agricultural and related sciences information. It is a growing global catalog of 5 million high-quality structured bibliographic records indexed from a worldwide group of providers. AGRIS relies heavily on the AGROVOC thesaurus for its indexing. Following the conversion of that thesaurus into a SKOS concept-scheme and its publication as Linked Open Data (LOD), the entire set of AGRIS records was also triplified and released as LOD. As part of this exercise, OpenAGRIS, a semantic mashup application, was developed to dynamically combine AGRIS data with external data sources, using a mixture of SPARQL queries and web services. The re-engineering of AGRIS for the Semantic Web raised numerous issues regarding the relative lack of administrative metadata required to compellingly address the proof and trust layers of the Semantic Web stack, both within the AGRIS repository and in the external data pulled into OpenAGRIS. The AGRIS team began a process of disambiguation and enrichment to continue moving toward an entity-based view of its resources, beginning with the tens of thousands of journals attached to its records. The evolution of the system, the issues raised during the triplification process and the steps necessary for publishing the result as LOD content are hereby discussed and evaluated.

Using metadata formats and AGROVOC thesaurus for data description in the agrarian sector

The paper deals with a general solution of semantic description related to various electronic data formats in the domains of agriculture, aquaculture, forestry, food industry, environment, horticulture and rural areas. The solution presented was developed on the basis of metadata formats analysis and then complemented with its own software superstructure. It is based on the VOA<sup>3</sup>R metadata application profile (AP) that was developed within the framework of the Virtual Open Access Agriculture and Aquaculture Repository project for the sake of describing research papers and scientific publications. However, thanks to its complexity and comprehensiveness, it is also suitable for different kinds of data and while combining it with the AGROVOC thesaurus, which is elaborated by the Food and Agriculture Organization of the United Nations, it can become a robust and universal tool for data characteristics and semantic description. The potential of the proposed solution is illustrated by means of two examples. While the first example brings a metadata description of a photograph depicting anthocyanin pigmentation of barley straw caused by phosphorus deficiency, the second example is related to a research paper description. The developed system of metadata description is broadly applicable in agriculture such as in precision agriculture, in plant production or in ground cover monitoring and evaluation based on sensor or visual data.

The Persian Agrovoc in an indexing context 1

The author explores the Persian Agrovoc thesaurus from the point of view of the indexer, both human and automatic. It is found wanting, with problems both in the English version and in the Persian translation.