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Semantic Annotation Primer

Introduction

A semantic annotation is the attachment of semantic metadata to a resource - in this case, a dataset. It provides precise definitions of concepts and clarifies the relationships between concepts in a machine-readable way. The process of creating semantic annotations may seem tedious, but the payoff is enhanced discovery and reuse of your data.

The main differences between semantic annotation and simply adding keywords are:

  • annotations can be read and interpreted by computers
  • annotations describe the relationship between a specific part of the metadata and an external vocabulary

Benefits of annotation: Annotations vastly enhance data discovery and interpretation. Semantic annotations will make it easier for others to find and reuse data (and thus give proper credit), including the following cases:

  1. Equivalent concepts: Assume one dataset uses the phrase "carbon dioxide flux" and another dataset "CO2 flux". An information system is able to recognize that these datasets are about equivalent concepts, if the datasets were annotated with the same identifier for that measurement.
  2. Disambiguation: Assume you are searching for datasets about "litter" (as in "plant litter"). If datasets have been annotated, the system will be able to understand the difference between your meaning and other meanings (e.g., "garbage", a "group of animals born together", a "device for transporting the wounded", etc.). Each type of "litter" would be associated with a different identifier, and connected to related concepts.
  3. Hierarchical searches: If you search for datasets about "carbon flux", then datasets about "carbon dioxide flux" can also be returned because "carbon dioxide flux" is a type of "carbon flux". This is possible because the concepts came from a structured system where "carbon dioxide flux" is lower in the hierarchy than "carbon flux".

EML 2.2.0 now provides ways to embed references to external vocabularies using HTTP uniform resource identifiers (or URIs). The process is called semantic annotation, and provides a rigorous, expressive and consistent interpretation of the metadata. Usually the external reference (or annotation) is to a knowledge graph, sometimes called a controlled vocabulary or ontology. The annotation provides a computer-usable pointer (the HTTP URI) that resolves (and dereferences) to a useful description, definition or other relationships for that annotated resource.

Take-home messages

  • Semantic statements must be logically consistent, as they are not simply a set of loosely structured keywords.
  • EML 2.2.0 has five places or methods to add annotations.
  • The best place for advice and feedback on EML annotations is your data management community

Organization of this document

The purpose of this Primer is to provide an introduction to how semantic annotations are structured in EML documents. It is expected that you already have some familiarity with the EML schema, and the focus of this document then, is explanation and examples of annotations in EML. This Primer is divided into three major sections. You should be able to create EML annotations immediately, using only the main section Semantic Annotations in EML 2.2.0, referencing the Appendix when you would like a longer explanation.

  • A. Introduction: (this section)
  • B. Semantic Annotations in EML 2.2.0, with examples. Where used, EML elements are shown as inline code blocks (elementName).
  • C. Appendix additional information on specific related topics, linked from Sections A and B.
    1. Glossary: Glossary of terms, linked from text
    2. Semantic triples: details on their structure, and how that structure is leveraged by annotations with examples of their power
    3. URIs: defined, and as components of semantic triples
    4. RDF model: the W3C's RDF model with example graphs based on EML annotations
    5. Logical consistency: Common mistakes and how to check for them
    6. Vocabularies used in examples: Descriptions an links out to explore further
    7. Supplemental background information: The EML annotation approach here is compatible with recommendations by the World Wide Web Consortium (W3C) for construction of the Semantic Web. A wealth of material is available; a few selected ones are here.

Semantic Annotations in EML 2.2.0

In EML 2.2.0 there are 5 places where annotation elements can appear in an EML document:

  • top-level resource -- an annotation element is a child of the dataset, literature, software, or protocol elements
  • entity-level -- an annotation element is a child of a dataset's entity (e.g., dataTable )
  • attribute -- an annotation element is a child of a dataset entity's attribute element
  • eml/annotations -- a container for a group of annotation elements, using references
  • eml/additionalMetadata -- annotation elements that reference a main-body element by its id attribute

Annotation element structure

All annotation nodes are defined as an XML type, so they have the same structure anywhere they appear in the EML record. Here is the basic structure. Sections below have more examples.

    <annotation>
        <propertyURI label="property label here">property URI here</propertyURI>
        <valueURI label="value label here">value URI here</valueURI>
    </annotation>

An annotation element always has a parent-EML element, which is the 'thing' being annotated, or the subject. (e.g., dataset, attribute, see above). The annotation element has two required child elements, propertyURI and valueURI. Together, these three form a "semantic statement", that can become a "semantic triple". The concept of a triple is covered in more detail (see Semantic Triples, below). Here, we concentrate on the structure of an annotation within the EML doc itself:

  • propertyURI and valueURI elements
    • the element's text is the URI for the concept in an external vocabulary. The identifier represents a precise definition, relationships to other concepts, etc.
    • the XML attribute, label is required
      • it should be suitable for application interfaces to display to humans
      • should be populated by values from the referenced vocabulary's label field (e,g, rdfs:label or skos:prefLabel ). Note that this assumes the referenced vocabulary is stored as an RDF document, which is best practice for vocabularies.

When are IDs required in the EML doc? To be precise, all annotations must have an unambiguous subject. At the dataset, entity, or attribute level, the parent element is the subject. So, if an element has an annotation child, it must also have an id (i.e. the subject, or parent element must have an id attribute value). Annotations at eml/annotations or eml/additionalMetadata will have subjects defined with a references attribute or describes element. As for other internal EML references, an id is required. With EML 2.2.0, the parser will check that an id attribute is present on elements with annotation children. As a reminder, the id must be unique within an EML document. See examples below.

Top-level resource, entity-level, and attribute annotations

Annotations for top-level resources, entities, and attributes follow the same general pattern.

  • The subject of the semantic statement is the parent element of the annotation. It must have an id attribute.

Example 1: Top-level resource annotation (dataset)

In the following dataset annotation, the semantic statement can be read as "the dataset with the id 'dataset-01' is about grassland biome(s)".

<dataset id="dataset-01">
    <title>Data from Cedar Creek LTER on productivity and species richness for use in a workshop titled
    "An Analysis of the Relationship between Productivity and Diversity using Experimental Results from
    the Long-Term Ecological Research Network" held at NCEAS in September 1996.</title>
    <creator id="clarence.lehman">
        <individualName>
            <salutation>Mr.</salutation>
            <givenName>Clarence</givenName>
            <surName>Lehman</surName>
        </individualName>
    </creator>
    ...    
    <coverage> 
        ...
    </coverage>    
    <annotation>
        <propertyURI label="is about">http://purl.obolibrary.org/obo/IAO_0000136</propertyURI>
        <valueURI label="grassland biome">http://purl.obolibrary.org/obo/ENVO_01000177</valueURI>
    </annotation>
      ...    
</dataset>

Example 2: Entity-level annotation

In the following entity-level annotation, the semantic statement can be read as "the entity with the id 'urn:uuid:9f0eb128-aca8-4053-9dda-8e7b2c43a81b' is about Mammalia".

<otherEntity id="urn:uuid:9f0eb128-aca8-4053-9dda-8e7b2c43a81b" scope="document">
    <entityName>DBO_MMWatch_SWL2016_MooreGrebmeierVagle.xlsx</entityName>
    <entityDescription>Data contained in the file DBO_MMWatch_SWL2016_MooreGrebmeierVagle.xlsx are marine mammal observations and observation conditions from CCGS Sir Wilfrid Laurier July 10-20, 2016.  Data observations and locations are part of the Distributed Biological Observatory (DBO).</entityDescription>
    <physical scope="document">
        <objectName>DBO_MMWatch_SWL2016_MooreGrebmeierVagle.xlsx</objectName>
        <size unit="bytes">24635</size>
    </physical>
    <entityType>Other</entityType>
    <annotation>
        <propertyURI label="is about">http://purl.obolibrary.org/obo/IAO_0000136</propertyURI>
        <valueURI label="Mammalia">http://purl.obolibrary.org/obo/NCBITaxon_40674</valueURI>  
    <annotation>
</otherEntity>

Example 3: Attribute annotation

In the following attribute annotation, the semantic statement can be read as "the attribute with the id 'att.4' contains measurements of type plant cover percentage"

<attribute id="att.4">
    <attributeName>pctcov</attributeName>
    <attributeLabel>percent cover</attributeLabel>
    <attributeDefinition>The percent ground cover on the field</attributeDefinition>
    <annotation>
        <propertyURI label="contains measurements of type">http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType</propertyURI>
        <valueURI label="Plant Cover Percentage">http://purl.dataone.org/odo/ECSO_00001197</valueURI>
    </annotation>
</attribute>

Example 3 as an RDF graph

eml/annotations element annotation

An annotation in the annotations element differs from Examples 1-3 above, because the subject is directly referred to by a references attribute. Each annotation element has a references attribute that points to the id attribute of the element being annotated. Stated another way, what is listed in the references attribute is the id of the subject of the semantic annotation. Any of the EML modules may be referenced by the references attribute and because ids are unique within an EML document, this is a single subject.

  • The subject of the semantic statement is implictly the element containing the referenced id.

Example 4: annotations element annotation

All the annotations for a resource can be group together under an annotations element. If you use this construct, each annotation must have its subject specifically identified with a references attribute that points to the subject's id. The group of annotations must be placed TO DO< WHERE IN DOC?

Example 4 contains 3 different annotations. In the first, the subject is the dataTable element with the id attribute "CDF-biodiv-table". It's semantic statement can be read as "the dataTable with the id 'CDR-biodiv-table' is about grassland biome(s)". And the annotation components are analagous to Examples 2 above, again referencing terms in IAO and ENVO.

The second and third annotations both have an individual their subjects -- the creator element that has the id "adam.shepherd".

Respectively, their semantic statements can be read as

  • "'adam.shepherd', the creator (of the dataset), is a person".
  • "'adam.shepherd', the creator (of the dataset), is a member of BCO-DMO".

The ontologies used for adam.shepherd are

  • in the second annotation
    • propertyURI : an RDF built-in type, "is a" (as in, the subject is an instance of a class)
    • valueURI : schema.org's concept of a "person"
  • third annotation
    • propertyURI : another schema.org concept for a relationship, "is a member of"
    • valueURI : the DOI for the organization BCO-DMO, which is managed by re3data.org.
<eml>
   ...
    <dataset id="dataset-01">
        <title>Data from Cedar Creek LTER on productivity and species richness for use in a workshop titled "An Analysis of the Relationship between Productivity and Diversity using Experimental Results from the Long-Term Ecological Research Network" held at NCEAS in September 1996.</title>
        <creator id="adam.shepherd">
            <individualName>
                <salutation>Mr.</salutation>
                <givenName>Adam</givenName>
                <surName>Shepherd</surName>
            </individualName>
        </creator>
        <dataTable id="CDR-biodiv-table">
            <entityName>CDR LTER-patterns among communities.txt</entityName>  
        ...
       </dataTable>  
    </dataset>
    ...
    <annotations>
        <annotation references="CDR-biodiv-table">
            <propertyURI label="is about">http://purl.obolibrary.org/obo/IAO_0000136</propertyURI>
            <valueURI label="grassland biome">http://purl.obolibrary.org/obo/ENVO_01000177</valueURI>
        </annotation>
        <annotation references="adam.shepherd">
            <propertyURI label="is a">http://www.w3.org/1999/02/22-rdf-syntax-ns#type</propertyURI>
            <valueURI label="Person">https://schema.org/Person</valueURI>
        </annotation>
        <annotation references="adam.shepherd">
            <propertyURI label="member of">https://schema.org/memberOf</propertyURI>
            <valueURI label="BCO-DMO">https://doi.org/10.17616/R37P4C</valueURI>
        </annotation>
    </annotations>
   ...
</eml>

See Example 4 as an RDF graph

eml/additionalMetadata element annotation

If an additionalMetadata section holds a semantic annotation, it must have a describes element (to hold the subject) with a metadata element containing at least one annotation element.

  • The subject of the semantic statement has its id contained in the describes element.
  • The annotation itself is within the additionalMetadata metadata section
  • Multiple annotation elements may be embedded in the same metadata element to assert multiple semantic statements about the same subject.
  • To annotate different subjects it's best to use additional additionalMetadata sections, each with a single subject

Example 5: additionalMetadata element annotation

Example 5 shows one of the same annotations as Example 4, but this time, it is contained in an additionalMetadata section.

The semantic statements can be read as "'adam.shepherd', the creator (of the dataset), is a person".

<eml>
    ...
    <dataset id="dataset-01">
        <title>Data from Cedar Creek LTER on productivity and species richness for use in a workshop titled "An Analysis of the Relationship between Productivity and Diversity using Experimental Results from the Long-Term Ecological Research Network" held at NCEAS in September 1996.</title>
        <creator id="adam.shepherd">
            <individualName>
                <salutation>Mr.</salutation>
                <givenName>Adam</givenName>
                <surName>Shepherd</surName>
            </individualName>
        </creator>
        <dataTable id="CDR-biodiv-table">
            <entityName>CDR LTER-patterns among communities.txt</entityName>  
         ...
       </dataTable>  
    </dataset>
    ...
     <additionalMetadata>
         <describes>adam.shepherd</describes>
         <metadata>
             <annotation>
                 <propertyURI label="member of">https://schema.org/memberOf</propertyURI>
                 <valueURI label="BCO-DMO">https://doi.org/10.17616/R37P4C</valueURI>
             </annotation>
         </metadata>
     </additionalMetadata>
</eml>

Appendix

Semantic triples

Semantic annotations enable the creation of what are called triples, that are 3-part statements conforming to the W3C recommended RDF data model (learn more: https://www.w3.org/TR/rdf11-primer/).

A triple is composed of three parts: a subject, a predicate (object property or datatype property), and an object.

[subject] [predicate] [object]

These components are analogous to parts of a sentence: the subject and object can be thought of as nouns in the sentence and the predicate (object property or datatype property) is akin to a verb or relationship that connects the subject and object. The semantic triple expresses a statement about the associated resource, that is generally the subject.

There are (perhaps unfortunately) several other ways that the components of an RDF statement are sometimes described. One popular "synonymy" for subject-predicate-object is resource-property-value, i.e. the subject is referred to as the resource, the predicate a property, and the object a value. This can be confusing, since the usual definition of a resource is any identifiable 'thing' or object, especially one assigned a URI; and by this definition, resources can and often do occur in all three components of a triple. But thinking of a triple as a resource-property-value does provide an indication of the directionality of the semantics of an RDF statement. This latter terminology is also somewhat similar to how analogous components are named in JSON-LD. Note that JSON-LD is closely compatible with RDF, and one format can often be readily translated to the other (although there are some exceptions).

Semantic annotations added to an EML document can be extracted and processed into a semantic web format, such as RDF/XML. These"semantic" statements, i.e. RDF triples, are interpretable by any machines that can process the W3C standard of RDF. Those RDF statements contribute to the Semantic Web.

URIs

Ideally, the components of the semantic triple should be globally unique and persistent (unchanging), and consist of resolvable/dereferenceable HTTP uniform resource identifiers (URIs; or more formally, IRI's). The subjects of most EML semantic annotations will likely be HTTP URI's that identify the dataset resource itself, or specific attributes or other features within a dataset. The objects of EML semantic annotations, as well as the predicates that relate the subject to the object, will most typically be HTTP URI references to terms in controlled vocabularies (also called "knowledge graphs", or "ontologies") accessible through the Web, so that users (or computers) can dereference the URI's and look up precise definitions and relationships of these resources to other terms 1.

An example of a URI is "http://purl.obolibrary.org/obo/ENVO_00000097", when entered into the address bar of a web browser, resolves to the term with a label of "desert area" in the Environment Ontology (EnvO). Users can learn what this URI indicates and explore how the term is related to other terms in the ontology simply by dereferencing its URI in a web browser. All those other aspects you see on the Web page describing "http://purl.obolibrary.org/obo/ENVO_00000097" are from other RDF statements (triples) related to "ENVO_00000097", and that have been rendered into HTML. From here, you might decide, e.g. that "http://purl.obolibrary.org/obo/ENV0_00000172" ("sandy desert") is a better annotation for your object.

An RDF triple can be constructed as follows, with subject URI, predicate URI, and object URI:

<<https://doi.org/10.6073/pasta/06db7b16fe62bcce4c43fd9ddbe43575>> <<http://purl.obolibrary.org/obo/RO_0001025>>   <<http://purl.obolibrary.org/obo/ENVO_00000097>>

.

... indicating that the referenced dataset (subject/resource) was "located in" (predicate/property) a "desert area" (object/value). Note that a blank-space must separate the subject, from the predicate, from the object, and that a "period" completes the triple. This is a valid RDF triple, expressed in N-Triple syntax. RDF is most often serialized into XML, however, as Web browsers and many applications are good at parsing XML.

While our focus here is on the semantic annotation of EML documents, it is easy to see how the RDF statements can be used to describe and inter-relate any resources that have unique, persistent HTTP URIs!

Note that the above RDF triple consists of three HTTP URIs. While the exact distinction among what is a URI, a URN, and a URL can be debated, for our purposes, these HTTP URI's are can be considered both the name and web location of a resource. Content negotiation between a Web server and a client (which might be a browser, or a Python or R script) can enable an HTTP URI to dereference in ways optimized for the requesting client -- e.g. in one case, presenting a human-readable view of metadata for a dataset, and in another, activating a download of that dataset for import into a script. [SC: we should include a link to the FAQ here... can we point directly to the question about URIs vs. URNs?]

The software needed to extract semantic annotations out of EML, and convert these into valid RDF triples, is under development at NCEAS and EDI, and through the rOpenSci project. The RDF triple described above, however, hopefully gives an idea of how such triples, constructed of dereferenceable HTTP URI's, can be very useful.

The sections below describe the exact syntax for embedding annotations in EML 2.2.0 documents.

RDF Graphs

A graph consists of resources linked to other resources. Thus the simplest graph structure is when you specify how one resource (node) is linked to another resource (node).

The parts of a triple (subject, predicate, and object) become nodes and links in a graph. Below are examples of how annotations can be converted to RDF triples in RDF/XML, so that the RDF information is now computer-readable. Be aware that there are several formats for serializing RDF, including RDF/XML, Turtle, N-Triples, and N3, that vary in the level of how human-readable they are.

This process of converting a semantic annotation in EML into RDF, is done by parsing applications under development at EDI, NCEAS, rOpenSci, and other data repositories. Careful examination of the examples below also show references to "owl:Class", "owl:ObjectProperty", and other statements that may not be familiar. These are fundamental entities or building blocks in W3C-recommended Semantic Web languages, and are determined by the relationships that the triple component identifiers (HTTP URI's) have within their native knowledge graph/ontology.

Graph from Example 3 (attribute annotation): (back to Example 3 XML)

RDF example A

<rdf:RDF
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:owl="http://www.w3.org/2002/07/owl#">
    
    <rdf:Description rdf:about="att.4"> ### See note below
        <owl:ObjectProperty rdf:about="http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType">
            <owl:Class rdf:about="http://purl.dataone.org/odo/ECSO_00001197" />
        </owl:ObjectProperty> 
    </rdf>
</rdf:RDF>

Note: The subject described in the rdf:Description about attribute should actually be a globally unique HTTP URI for the attribute, rather than 'att.4'. The details of how this HTTP URI GUID is constructed are being developed by EDI, NCEAS, and others.

Graph from Example 4 (using annotations element): (back to Example 4 XML)

RDF example B

<rdf:RDF
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:owl="http://www.w3.org/2002/07/owl#">
    
    <rdf:Description rdf:about="adam.shepherd"> ### See note below 
        <owl:ObjectProperty rdf:about="http://www.w3.org/1999/02/22-rdf-syntax-ns#type">
            <owl:Class rdf:about="https://schema.org/Person" />
        </owl:ObjectProperty> 
        <owl:ObjectProperty rdf:about="https://schema.org/memberOf">
            <owl:Class rdf:about="https://doi.org/10.17616/R37P4C" />
        </owl:ObjectProperty> 
    </rdf>
    
</rdf:RDF>

Note: The subject described in the rdf:Description about attribute should actually be the globally unique URI issued for 'adam.shepherd'. The details of how this HTTP URI GUID is constructed are being developed by EDI, NCEAS, and others.

Check for Logical Consistency

With semantic annotation, you are adding precise definitions of concepts and relationships that can be traversed with computer logic. Annotations are not simply a set of loosely structured keywords! This is a really powerful addition to EML, and so it comes with some risk. The main thing you should ensure is that your annotations are logically consistent.

The simplest way to check your logic is to write out the RDF triple components and see if it makes sense as a sentence.

[subject (element-id)]  [predicate (propertyURI)]          [object (valueURI)]
[att.4]                 [contains measurements of type]    [plant cover percentage]

The graph examples (Example 3 RDF graph, Example 4 RDF graph) make 'true' statements that are logically consistent:

  • att.4 contains measurements of the type plant cover percentage
  • adam.shepherd is a person
  • adam.shepherd, member of BCO-DMO

However, below is the kind of statement you would NOT want to make:

[adam.shepherd] [is a type of] [measurement]

If you suspect your RDF triple might look like this, you should go back and examine the way you structured the annotation.

Things to check:

  1. Be sure you have used the right classes, properties, or vocabularies for your annotation components
  2. Become familiar with the vocabularies in your annotation, especially definitions and relationships
  3. Check with your community for specific recommendations on the best vocabularies to use for annotations at different levels
  4. In additionalMetadata, don't combine annotations with more than one describes element. EML allows 1:many describes elements in a single additionalMetadata section. So if you have 2 describes and 2 annotations, you will have 4 RDF statements. Make sure they are all true, and if not, break them up into multiple additionalMetadata sections.

Glossary

ontology: A knowledge graph representation of a set of terms, including their names, and descriptions of the categories, properties, and relationships among those terms.

Resource Description Framework (RDF):A family of World Wide Web Consortium (W3C) recommendations that enable the encoding, exchange, and reuse of structured metadata. The RDF data model employs semantic triples composed of a subject, predicate, and object to share and integrate data across different applications and communities through the Web.

uniform resource identifier (URI): A string of characters that unambiguously identifies a particular resource. For semantic annotations, the components of semantic triples are ideally HTTP URIs that resolve and describe precise definitions and relationships to other terms, using Web technology.

Pointer definition here

knowledge graph definition here

dereference definition here

subclasss definition here

semantic statement definition here

resolve definition here

Vocabularies used in Examples

Communities using EML annotation will develop recommendations for suitable vocabularies, based on their own requirements (e.g., domain coverage, structure, adaptability, reliabliity and maintenance model). The following ontologies are already widely used, and were employed in the examples above:

  • Environment Ontology (EnvO) definition, etc here

  • Information Artifact Ontology (IAO) definition, etc here

  • Ecosystem Ontology (EcsO) definition, etc here

  • schema.org definition, etc here

  • re3data.org definition, etc here

Additional background information

Following are tutorials and supplemental background reading

  • Tim Berners-Lee's article on the semantic web: Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american, 284(5), 34-43.

Footnotes

might not be needed.

parking area

Left over bits you might need. or not.

So be careful, and if you have questions, bring them up in your community for feedback. The examples in this primer should also make clear that inconsistent annotations could create confusion.

Footnotes

  1. this might be a footnote about URIs ↩