From 530b3cbbe9a5ed7be87f9bdae57c034ed4bab6bb Mon Sep 17 00:00:00 2001 From: stephanef Date: Wed, 17 Jan 2024 18:15:12 -0500 Subject: [PATCH] Updated resource classification usage guide --- docs/index.html | 131 ++++++++++++++++++++++++++---------------------- 1 file changed, 70 insertions(+), 61 deletions(-) diff --git a/docs/index.html b/docs/index.html index 8fd25ab..7be5c39 100644 --- a/docs/index.html +++ b/docs/index.html @@ -4726,7 +4726,7 @@

Concept

accessed, integrated with other resources, and reused across the DCAT-US ecosystem, promoting data interoperability and accessibility.
  • To enhance data interoperability and consistency, it is advisable to reuse established controlled - vocabularies such as GCMD, Agrovoc, and NAICS for data description.
    • + vocabularies such as Global Change Master Directory (GCMD) [[?GCMD]], Agrovoc, and NAICS for data description. @@ -5021,7 +5021,7 @@

    Concept Scheme

    using SKOS encoding and provided in Linked Data format (RDF/XML,TTL, JSON-LD, NTriples)
  • To enhance data interoperability and consistency, it is advisable to reuse established controlled - vocabularies such as GCMD, Agrovoc, and NAICS for data description.
    • + vocabularies such as Global Change Master Directory (GCMD) [[?GCMD]], Agrovoc, and NAICS for data description. @@ -19323,66 +19323,75 @@

    Extended Attributions and Diverse Roles

    Resource Classification

    +

    Controlled vocabularies, including taxonomies and thesauri, dramatically enhance data searchability. Utilizing + these vocabularies allows datasets to be systematically classified, tagged, and described with standardized + terms, aiding users in retrieving relevant datasets, even when using varied terms or synonyms.

    + +

    Employing controlled vocabularies enables semantic search, which comprehends the context and + relationships behind search terms. This approach enhances search results, for example, linking "automobiles" + with related terms like "cars" or "vehicles".

    + +

    This enriched search experience is crucial for navigating vast, diverse datasets, ensuring comprehensive and + relevant results, and bridging the gap between user intent and dataset content.

    + +

    The DCAT-US profile utilizes properties from the DCAT 3 framework for resource classification, providing + flexibility in the choice of + controlled vocabularies to meet the specific needs of various communities or agencies.

    + + -

    Controlled vocabularies, encompassing taxonomies, thesauri have a transformative impact on data searchability. - By using these vocabularies, datasets can be classified, tagged, and described with standardized terms and - phrases. This standardization ensures that users searching with different terms or synonyms can still retrieve - the most relevant datasets.

    -

    More than just keyword matching, the use of controlled vocabularies facilitates semantic - search. This means that the search process understands the context, relationships, and meanings - behind terms, rather than just the terms themselves. For instance, when using a thesaurus-based vocabulary, - searching for "automobiles" might also yield results for "cars" or "vehicles".

    -

    Such an enriched search experience becomes especially vital when dealing with vast and diverse datasets. It - ensures that users can find the most relevant and comprehensive results, even if the exact phrasing or - terminology varies between the user's query and the dataset's metadata. In essence, controlled vocabularies - bridge the gap between user intent and dataset content, leading to more accurate and meaningful search outcomes. -

    -

    The DCAT US profile uses a range of properties from the DCAT 3 framework to classify and categorize resources, - helping users and systems understand and navigate resources.

    -
    -

    Resource types

    -

    - The dcterms:type property specifies the nature or genre of content and is applicable to - dcat:Dataset, dcat:DataService, and dcat:DatasetSeries. For instance, types - might include "Geospatial Dataset", "Image", "Statistical Dataset", or "Map". The Dublin Core Type Vocabulary - is for example a popular vocabulary used to categorize datasets. -

    -
    -
    -

    Keywords

    -

    - Relevant for dcat:Dataset, dcat:DataService, dcat:Catalog, and dcat:DatasetSeries, the - dcat:keyword property allows datasets to be tagged with pertinent terms represented as literals. - Using keywords from AGROVOC, GCMD, or the North American Industry - Classification System (NAICS) can enhance consistency in the US context. -

    -
    -
    -

    Thematic Classification

    -

    - Applicable to dcat:Dataset and dcat:DatasetSeries, the - dcat:theme - property offers thematic categorization. The Data Theme Taxonomy from Data.gov (TBD) and the - Federal Geographic Data Committee (FGDC) Controlled Vocabularies such as ISO 19115 Topic - CodeList - and - Geoplatform NSDI Themes are widely used in the US to ensure a unified theming approach. -

    -
    -
    -

    Subject Classification

    -

    - Suitable for dcat:Dataset and dcat:DatasetSeries, the - dcterms:subject - property provides deeper insight into a dataset's primary subject. Adopting vocabularies like the - Global Change Master Directory (GCMD) FAO Agrovoc, the Integrated - Taxonomic Information System (ITIS), the North American Industry Classification System - (NAICS) or Library of Congress Subject Headings (LCSH), can optimize clarity and - searchability in US Governement datasets. -

    -
    + +

    Spatial Metadata

    @@ -21520,7 +21529,7 @@

    Other controlled vocabularies

    Profile, they may serve to increase interoperability across applications in the same region or domain. Examples are the full - set of concepts in GCMD [[GCMD]],and numerous other schemes.

    + set of concepts in Global Change Master Directory (GCMD) [[?GCMD]],and numerous other schemes.

    For geospatial metadata, the working group has identified the following additional vocabularies: