Frequently Asked Questions
- What is Caliper?
- Why Caliper?
- Is Caliper for me?
- Is Caliper an official FAO website?
- Who works on Caliper?
- Is Caliper the author of the classifications exposed?
- Can I use the classifications published on Caliper?
- I would like classification XYZ to be included in Caliper
- BROWSING section. I see a list of classifications, but where are the correspondences?
- BROWSING section. What is the meaning of tab "Groups"?
- Caliper hosts statistical classifications in RDF. What is the data/metadata model?
- Wasn't SDMX enough?
- Does Caliper use XKOS?
- Is there documentation available?
- What does it mean - SKOS
- What does it mean - SPARQL
- What does it mean - Vocabulary
- What is the license of Caliper?
Caliper is a web platform to test new ways to work with statistical classifications relevant to agriculture. We are interested in testing the use of open and standard formats along the entire life cycle of statistical classifications. Caliper hosts:
- a browsing/searching interface (SKOSMOS, an open source project developed by the National Library of Finland)
- an web-based editing tool (VocBench, an open source project developed by the University of Tor Vergata, Italy)
- a download area
- a SPARQL query area
If you are interested in other functionalities/tools, please do not hesitate to contact us.
Despite their importance, statistical classifications have not received much attention in the effort of modernization of official statistics. Caliper tries to address that gap. We aim at making statistical classifications available in formats that are fully machine readable, and easily accessible by humans for consultation and reuse. This work contributes to making statistical data better interoperable.
This platform aims at addressing different types of users.
If you are a statistician, you may be interested in getting the general ideas behind this work and how it connects with your daily job. We are developing content to meet your needs and curiosity. Meanwhile, you may want to start from section Classifications, where we also list a number of useful resources. We are also working on adding CSV as a format for download.
If you are semantic web expert or practitioner, you may be interested in testing the functionalities and the RDF data and modelling we make available. Please feel free to download the data available, query the SPARQL endpoints and test all other functionalities available.
If you are an expert in classifications, or an IT expert working with their management, you may be interested in comparing the extra functionalities that this technologies offer to you - e.g, more flexible browsing and searching, easy aggregation of information from different sources, open source editing tools supporting editing workflow.
NO. This is experimental work. All data is in draft state and not to be used as reference.
The platform Caliper is part of a project carried on at the Statistics Division of the Food and Agriculture Organization of the UN and funded by the Bill and Melinda Gates Foundation. The University of Tor Vergata provides technical and scientific support to it.
NO. The classifications published in Caliper are maintained and published by dedicated institutions (sometimes in collaborations with FAO but not necessarily). No classification or correspondence was developed within this project. Occasionally, we may have multilingual contents added to the original classifications for testing purposes (e.g., the Spanish translation for CRS Purpose Codes).
You're welcome to test our work. But pleases remember that the classifications presented on this web site do not replace in any ways the versions distributed by their original maintainers.
If XYZ is an international classification relevant to agriculture, maybe we are already working on it or have a plan to include it - or maybe not :) Either way, please do not hesitate to contact us!
Correspondences are included in the classifications. More precisely, they are included in the classification that is the source of the mapping: for instance, correspondences of CPC 2.0 terms with CPC 2.1 terms are included in CPC 2.0, while correspondences of CPC 2.1 terms with CPC 2.0 terms are included in CPC 2.1.
For the moment, you can see correspondences:
- in SKOSMOS, by clicking on a concept and looking at the CLOSELY MATCHING CONCEPTS and EXACTLY MATCHING CONCEPTS properties;
- on this website, under Browsing > Browsing in Drupal > Correspondences.
Technical note: (see data model below) correspondences between classifications are expressed using the matching properties of the Simple Knowledge Organization Systems (SKOS) vocabulary. In particular, we use skos:exactMatch to express 1-1 correspondences between classifications' entries, and skos:closeMatch to express partial correspondences. You can see these pieces of information displayed together with the rest of concept's information when available (main panel).
Our plans for the future include:
- Switching to the modelling style suggested by XKOS (SKOS extension for Statistical Classifications), which allows for more granular types of mappings, as soon as it is officially released by DDI.
- Browse correspondences also "independently" of the classifications they map.
The tab "Groups" displays concepts belonging to the SKOS structure "skos:collection". We are using this feature of SKOS to experiment with the possibility of marking classifications' fragments / subsets specific to some needs. See for example the group of concepts relevant to the FAO Fisheries and Aquaculture Dept, in CPC v2.1:
NOTE: Currently, concepts in a skos:collection are visualized in SKOSMOS as a flat list, although the hierarchical information remain available (see main panel, to the right).
The statistical classifications available in Caliper are rendered as SKOS Concept Schemes. The Simple Knowledge Organization Systems (SKOS) is an RDF vocabulary aimed at providing a standard model and terminology to express hierarchical structures, such as thesauri or classifications. Besides SKOS, we also use Dublin Core and the Ontology Web Language (OWL) to express certain pieces of metadata.
Each statistical classification is rendered as a SKOS Concept Scheme, where:
- items are skos:Concept, endowed with labels in different languages, definitions, notation, documentation notes (change, editorial, history...)
- the hierarchical structure of a classification is expressed by means of the standard SKOS properties skos:narrower, skos:broader.
- mappings between items in different classifications are expressed using the SKOS standard properties for semantic relations (skos:closeMatch and skos:exactMatch)
- subsets of a classification are defined by using SKOS collections.
The metadata elements expressed for each classifications belong to Dublin Core and OWL (ie. title, creator, publisher, description, date of creation, date of last update, version, history notes...).
SDMX is an XML-based standard for disseminating statistical data. The equivalent of SDMX in RDF is the RDF Data Cure (W3C Recommendatiton, 2014), which implements the SDMX cube model as Linked Data.
XKOS is the RDF vocabulary specific for statistical classifications, endorsed by DDI. No XML-based equivalent of SDMX for statistical classification exists.
Not yet, the RDFs in Caliper use "plain" SKOS, because XKOS, "An SKOS extension for representing statistical classifications" was officially released by DDI in May 2019, much after the start of the project Caliper. We plan on implementing it XKOS very soon.
SKOS stands for "Simple Knowledge Organization System (SKOS). It is a W3C recommendation designed for representation of thesauri, classification schemes, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. SKOS is part of the Semantic Web family of standards built upon RDF and RDFS, and its main objective is to enable easy publication and use of such vocabularies as linked data." (From Wikipedia).
SKOS defines classes and properties for representing:
- a "concept scheme" (a set of terms: a classification, a code list, a list of subject headings...)
- its terms / concepts (labels in different languages, definition, notation, editorial notes...),
- the relationships between concepts (generic or hierarchical)
- subsets of concepts (collections)
It is therefore suitable for representing classifications in a semantic, machine readable way.
SPARQL is the query language for RDF.
In everyday language, a vocabulary is a set of words, possibly used by a group, individual, or work, or in a field of knowledge (See the definitions given by Merriam-Webster dictionary). Vocabularies are then fundamental to shape the universe of discourse of people, and have a special role in the field of information management, especially in the form of controlled vocabularies, i.e., selected list of words used as "tag" or "classifier" of information unit - numeric or textual data. Because of their role in defining the entities to measure and codifying data, statistical classifications can be considered as special types of vocabularies.
Also in the area of information management and in the semantic web, vocabularies play a very important role. The World Wide Web Consortium (W3C) Vocabularies are defined in this broad sense by the W3C: "On the Semantic Web, vocabularies define the concepts and relationships (also referred to as “terms”) used to describe and represent an area of concern. Vocabularies are used to classify the terms that can be used in a particular application, characterize possible relationships, and define possible constraints on using those terms. In practice, vocabularies can be very complex (with several thousands of terms) or very simple (describing one or two concepts only)."
Moreover, the W3C usefully distinguishes two types of vocabularies:
- value vocabularies or sets of controlled values used to categorize and classify things. These are also known as Knowledge Organization Systems (KOS) and include classifications, code lists, thesauri, even certain types of ISO standards that prescribe controlled lists of values;
- metadata element sets that prescribe what features or properties should be used to describe things. They are also called schemas, or description vocabularies. XML schemas and RDF schema, formal languages to describe entities in XML and RDF respectively. Other example include ontologies, application profiles, and UML models.
The statistical classifications that are the focus of Caliper fall under the first type. SKOS, the formal language we used to express statistical classifications in a machine-readable format, is an example of the second type. Specifically, SKOS is a vocabulary for RDF, tailored to express thesauri on the web.
Caliper is not an official FAO website and the data it contains should be considered as experimental. For this reason, no license is explicitly given.