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Updated resource classification usage guide
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Expand Up @@ -4726,7 +4726,7 @@ <h2>Concept</h2>
accessed, integrated with other resources, and reused across the DCAT-US ecosystem, promoting data
interoperability and accessibility.</li>
<li>To enhance data interoperability and consistency, it is advisable to reuse established controlled
vocabularies such as GCMD, Agrovoc, and NAICS for data description.</li>
vocabularies such as Global Change Master Directory (GCMD) [[?GCMD]], Agrovoc, and NAICS for data description.</li>
</ul>
</td>
</tr>
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using SKOS encoding and provided in Linked Data format (RDF/XML,TTL, JSON-LD, NTriples)
</li>
<li>To enhance data interoperability and consistency, it is advisable to reuse established controlled
vocabularies such as GCMD, Agrovoc, and NAICS for data description.</li>
vocabularies such as Global Change Master Directory (GCMD) [[?GCMD]], Agrovoc, and NAICS for data description.</li>
</ul>
</td>
</tr>
Expand Down Expand Up @@ -19323,66 +19323,75 @@ <h4>Extended Attributions and Diverse Roles</h4>
<section id="resource-classification">
<h3>Resource Classification</h3>

<p>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.</p>

<p>Employing controlled vocabularies enables <strong>semantic search</strong>, 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".</p>

<p>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.</p>

<p>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.</p>

<ul>
<li>
<p>
<a href="#dataset-type"><strong>dcterms:type</strong></a>: This property specifies the category or genre ofgc
the content in a resource. It's applicable to <a href="#properties-for-dataset">dcat:Dataset</a>, <a
href="#properties-for-data-service">dcat:DataService</a>, and <a
href="#properties-for-dataset-series">dcat:DatasetSeries</a>. For <a
href="#properties-for-data-service">dcat:DataService</a>, types might include "Web Map Service" (WMS) for
services providing geographical data in a map format, "Web Feature Service" (WFS) for services allowing
users to access geospatial features, or "RESTful API" for services using REST API protocols. For datasets,
types can be "Geospatial Dataset", "Image", "Statistical Dataset", or "Map". The Dublin Core Type Vocabulary
is a popular choice for providing standardized descriptors.
</p>
</li>
<li>
<p>
<a href="#dataset-keyword"><strong>dcat:keyword</strong></a>: This property allows for the tagging of
datasets with relevant keywords, facilitating easier discovery and categorization. It is suitable for use
with <a href="#properties-for-dataset">dcat:Dataset</a>, <a
href="#properties-for-data-service">dcat:DataService</a>, <a
href="#properties-for-dataset-series">dcat:DatasetSeries</a>, and <a
href="#properties-for-catalog">dcat:Catalog</a>. Employing keywords from established vocabularies such as
AGROVOC (for agricultural terms), Global Change Master Directory (GCMD) [[?GCMD]] (for Earth science), or NAICS (for industry classifications) ensures
consistency and enhances the discoverability of datasets within the US context.
</p>
</li>
<li>
<p>
<a href="#dataset-theme-category"><strong>dcat:theme</strong></a>: This property provides thematic
categorization for resources, specifically for <a href="#properties-for-dataset">dcat:Dataset</a> and <a
href="#properties-for-dataset-series">dcat:DatasetSeries</a>. Utilizing a unified thematic taxonomy, such
as the Data Theme Taxonomy from Data.gov or the FGDC (Federal Geographic Data Committee) Controlled
Vocabularies like the ISO 19115 Topic CodeList, ensures a cohesive approach to categorizing datasets. This
thematic classification aids users in navigating and identifying datasets relevant to particular subjects or
sectors.
</p>
</li>
<li>
<p>
<a href="#dataset-subject"><strong>dcterms:subject</strong></a>: Aimed at providing detailed insight into
the primary subject matter of a dataset, this property is crucial for <a
href="#properties-for-dataset">dcat:Dataset</a> and <a
href="#properties-for-dataset-series">dcat:DatasetSeries</a>. Adoption of controlled vocabularies like
Global Change Master Directory (GCMD) [[?GCMD]] for Earth science topics, FAO Agrovoc for agricultural subjects, ITIS for taxonomic information, NAICS
for industry classifications, or LCSH (Library of Congress Subject Headings) enhances the clarity and
searchability of datasets, particularly in the context of US Government data. These vocabularies enable
precise and comprehensive subject classification, facilitating more effective data discovery and use.
</p>
</li>
</ul>

<p>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.</p>
<p>More than just keyword matching, the use of controlled vocabularies facilitates <strong>semantic
search</strong>. 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".</p>
<p>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.
</p>
<p>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.</p>
<section id="resource-types">
<h4>Resource types</h4>
<p>
The <code>dcterms:type</code> property specifies the nature or genre of content and is applicable to
<strong>dcat:Dataset, dcat:DataService</strong>, and <strong>dcat:DatasetSeries</strong>. For instance, types
might include "Geospatial Dataset", "Image", "Statistical Dataset", or "Map". The <a
data-cite="DCMI-TYPES#section-7"><code>Dublin Core Type Vocabulary</code></a>
is for example a popular vocabulary used to categorize datasets.
</p>
</section>
<section id="keywords">
<h4>Keywords</h4>
<p>
Relevant for <strong>dcat:Dataset, dcat:DataService, dcat:Catalog, and dcat:DatasetSeries</strong>, the
<code>dcat:keyword</code> property allows datasets to be tagged with pertinent terms represented as literals.
Using keywords from <strong>AGROVOC</strong>, <strong>GCMD</strong>, or the <strong>North American Industry
Classification System (NAICS)</strong> can enhance consistency in the US context.
</p>
</section>
<section id="thematic-classification">
<h4>Thematic Classification</h4>
<p>
Applicable to <strong>dcat:Dataset</strong> and <strong>dcat:DatasetSeries</strong>, the
<code>dcat:theme</code>
property offers thematic categorization. The <strong>Data Theme Taxonomy from Data.gov</strong> (TBD) and the
<strong>Federal Geographic Data Committee (FGDC) Controlled Vocabularies</strong> such as ISO 19115 Topic
CodeList
and
Geoplatform NSDI Themes are widely used in the US to ensure a unified theming approach.
</p>
</section>
<section id="subject-classification">
<h4>Subject Classification</h4>
<p>
Suitable for <strong>dcat:Dataset</strong> and <strong>dcat:DatasetSeries</strong>, the
<code>dcterms:subject</code>
property provides deeper insight into a dataset's primary subject. Adopting vocabularies like the
<strong>Global Change Master Directory (GCMD)</strong> <strong>FAO Agrovoc</strong>, the <strong>Integrated
Taxonomic Information System (ITIS)</strong>, the <strong>North American Industry Classification System
(NAICS)</strong> or <strong>Library of Congress Subject Headings (LCSH)</strong>, can optimize clarity and
searchability in US Governement datasets.
</p>
</section>
</section>


<!-- Spatial Metadata -->
<section id="spatial-metadata">
<h3>Spatial Metadata</h3>
Expand Down Expand Up @@ -21520,7 +21529,7 @@ <h4>Other controlled vocabularies</h4>
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.</p>
set of concepts in Global Change Master Directory (GCMD) [[?GCMD]],and numerous other schemes.</p>

<p>For geospatial metadata, the working group has identified the following additional vocabularies:
</p>
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