EML 2.2.0 now provides ways to embed HTTP URI's into several EML elements, thus serving as semantic annotations of those elements. These annotations, by referencing external knowledge graphs (aka controlled vocabularies or ontologies), will provide for much more rigorous, expressive, and consistent interpretation of the metadata contents.
The purpose of this primer is to provide an introduction to how these semantic annotations are structured in EML documents. It is expected that you have some familiarity with the EML schema prior to reading this document. It is important to note that our annotation approach is compatible with RDF, a specification recommended by the World Wide Web Consortium (W3C) for construction of the Semantic Web. If you want to read more about the W3C's RDF data model, knowledge graphs or the Semantic Web, there is supplemental material at the bottom of this primer.
A semantic annotation involves the attachment ("annotation") of semantic metadata to a resource -- which in this context would be an EML element. A semantic annotation provides a pointer (HTTP uniform resource identifier; URI) that should resolve (and dereference) to useful descriptions, definitions, or relationships that the annotated resource has with other terms or resources, and do so in a computer-usable way. The "pointers" reference terms within web-accessible knowledge graphs (i.e. "controlled vocabularies"). The process of creating semantic annotations may seem tedious, but the payoff is vastly enhanced information discovery and interpretation. Semantic annotations will make it easier for others to find and reuse your data (and thus give you credit).
For example, if a dataset is annotated as being about "carbon dioxide flux" and another dataset is annotated as being about "CO2 flux" the information system can recognize that these datasets are about equivalent concepts, because this equivalence can be indicated in a "computer-usable" way through the semantic annotation-- e.g. by sharing the same HTTP URI for their annotation.
As another example, if you perform a search for datasets about "litter" (as in "plant litter"), the system will be able to disambiguate the term from the many meanings of "litter" (as in garbage, the grouping of animals born at the same time to the same mother, etc.). These different types of litter could be disambiguated because they would be annotated with different identifiers (HTTP URI). Yet another example is if you search for datasets about "carbon flux", then datasets about "carbon dioxide flux" can also be returned because "carbon dioxide flux" is considered a type of "carbon flux". This is possible because the identifier (HTTP URI) for "carbon dioxide flux" would be organized into a hierarchy in its knowledge graph, and represented in this case as a subclass of "carbon flux".
Semantic statements must be logically consistent, as they are not simply a set of loosely structured keywords. The examples in this primer should also make clear that inconsistent annotations could create confusion. So be careful, and if you have questions, bring them up in your community for feedback.
If you already understand the basics of how RDF and the Semantic Web work, and are eager to simply learn how to implement semantic annotations in your EML documents, feel free to skip the next two sections, and go directly to the section on "Semantic Annotations in EML 2.2.0". If you want a refresher on how these semantic annotations will work, and why they are immensely powerful, the next two sections may be helpful.
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.
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.
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.
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.
In EML 2.2.0 there are 5 places where annotation elements can appear in an EML document:
- top-level resource -- an
annotationelement is a child of thedataset,literature,software, orprotocolelements - entity-level -- an
annotationelement is a child of a dataset's entity (e.g.,dataTable) - attribute -- an
annotationelement is a child of a dataset entity'sattributeelement - eml/annotations -- a container for a group of
annotationelements, using references - eml/additionalMetadata --
annotationelements that reference a main-body element by its id
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>propertyURIandvalueURIelements- must each have a
labelattribute that is suitable for application interfaces - are recommended to have labels populated by values from the label field (
rdfs:label) or preferred labels field (skos:prefLabel) from the referenced vocabulary - should contain URIs that point to terms in controlled vocabularies providing precise definitions, relationships to other terms, and multiple labels for display
- must each have a
When are IDs required?
Annotations at the dataset, entity or attribute level presume that the parent element is the subject. If an element has
an annotation child, an id is required (i.e. the subject 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. For other internal EML references, an id is required. The EML-2.2 parser checks for an id attribute if an annotation is present. As a reminder, the id must be unique within an EML document.
Labels: It is recommended that the label field of the annotation is populated by the value from the label field (rdfs:label: that should always be present) or preferred label field (skos:prefLabel: that sometimes are provided) from the referenced vocabulary. Note that this assumes the referenced vocabulary is stored as an RDF document, which is best practice.
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.
In the following dataset annotation, the subject of the semantic statement is the dataset element containing
the id attribute value "dataset-01". The predicate-- "http://purl.obolibrary.org/obo/IAO_0000136", is an object property explicating the relationship of the subject to the object, using a term from the Information Artifact Ontology, IAO (http://www.obofoundry.org/ontology/iao.html).
Finally, the object (value) in the semantic statement is "http://purl.obolibrary.org/obo/ENVO_01000177", which resolves
to the "grassland biome" term in the EnvO ontology (http://www.obofoundry.org/ontology/envo.html).
Taken together, 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> In the following entity-level annotation, the subject of the semantic statement refers to the otherEntity
element's id attribute value, "urn:uuid:9f0eb128-aca8-4053-9dda-8e7b2c43a81b". The object property of the
statement is "http://purl.obolibrary.org/obo/IAO_0000136". Finally, the object (value) in the semantic statement
is "http://purl.obolibrary.org/obo/NCBITaxon_40674", which resolves to the "Mammalia" term in the NCBITaxon
ontology (http://www.ontobee.org/ontology/NCBITaxon).
Taken together, the semantic statement indicates that "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>In the following attribute annotation, the subject of the semantic statement is the attribute element
containing the id attribute value "att.4". The predicate is of the statement is an object property
"http://ecoinformatics.org/oboe/oboe.1.2/oboe-core.owl#containsMeasurementsOfType". Note that the URI for the
object property resolves to a specific term in the OBOE ontology (https://github.com/NCEAS/oboe). Finally, the object (value)in the semantic statement is "http://purl.dataone.org/odo/ECSO_00001197", which resolves to the "Plant Cover Percentage" term in the ECSO Ontology (https://github.com/DataONEorg/sem-prov-ontologies/tree/master/observation).
Taken together, the semantic statement indicates that "the attribute with the id 'att.4' contains measurements of type plant cover percentage". Of course, this statement needs to be interpreted in the context of the entity within which 'att.4' occurs.
<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>An annotation in the annotations element differs from other types of annotations in that 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 contains 3 different annotations. For the first annotation,
the subject of the semantic triple is the dataTable element with the id attribute "CDF-biodiv-table". Notice
that the annotation has a references attribute that points to the subject id. The object property of the triple
is "http://purl.obolibrary.org/obo/IAO_0000136". Finally, the value (object) in the semantic triple
is "http://purl.obolibrary.org/obo/ENVO_01000177", which resolves to the "grassland biome" term in the
EnvO ontology (http://www.obofoundry.org/ontology/envo.html). Taken together, the first semantic statement could be
read as "the dataTable with the id 'CDR-biodiv-table' is about grassland biome(s)".
The second annotation has as its subject the creator element that has the id "adam.shepherd",
the object property "http://www.w3.org/1999/02/22-rdf-syntax-ns#type" and the value (object) "https://schema.org/Person".
This statement can be interpreted as "'adam.shepherd', the creator (of the dataset), is a person".
The third annotation also has as its subject the creator element that has the id "adam.shepherd".
The object property is "https://schema.org/memberOf" and the object (value) is "https://doi.org/10.17616/R37P4C".
This statement can be read as "'adam.shepherd', the creator (of the dataset), is a member of BCO-DMO".
<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>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
describeselement. - The annotation itself is within the
additionalMetadatametadatasection - Multiple
annotationelements may be embedded in the samemetadataelement to assert multiple semantic statements about the same subject. - To annotate different subjects it's best to use additional
additionalMetadatasections, each with a single subject
The following additionalMetadata annotation describes a semantic statement where the subject is the creator element with the id attribute "adam.shepherd". The object property of the statement is "https://schema.org/memberOf".
Finally, the object (value) in the semantic statement is the DOI for BCO-DMO, "https://doi.org/10.17616/R37P4C".
Taken together, the semantic statement could be read as "'adam.shepherd' (the creator of the dataset) is a member of BCO-DMO".
<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>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.
<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.
<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.
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 above 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:
- Be sure you have used the right classes, properties, or vocabularies for your annotation components
- Become familiar with the vocabularies in your annotation, especially definitions and relationships
- Check with your community for specific recommendations on annotations at different levels
- In
additionalMetadata, don't combineannotationswith more than onedescribeselement. EML allows 1:manydescribeselements in oneadditionalMetadatasection. So if you have 2describesand 2annotations, you will have 4 RDF statements. Make sure they are all true, and if not, break them up.
External resources:
- LinkedDataTools tutorial: http://www.linkeddatatools.com/introducing-rdf
- RDF data model: https://www.w3.org/TR/WD-rdf-syntax-971002/
- W3C RDF primer: https://www.w3.org/TR/rdf11-primer/
- A tidyverse lover’s intro to RDF https://ropensci.github.io/rdflib/articles/rdf_intro.html
- 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.
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.