Unfortunately, approaches to the production of semantic annotations in a standardized language are scarce or not available for non-expert users. A method is the building of semantic spreadsheet templates where classes, individuals, and properties are declared, with which an ontology is built. A good example is developed by Girón et al. 2021 found at https://github.com/insect-morphology/aism/blob/master/AISM_template_examples.tsv, where the description of cercus through axioms is made for various insects orders. Another application is proto.morphdbase.de, which allows the declaration of individuals, but currently not available. In this paper, the morphological descriptions are based on the building of a “descriptive template”, which is a .owl file to hold the anatomical terms (e. g. chaetotaxy) manually imported from CLAO. The import involves the search and the declaration within the template of terms related with anatomical entities described for Lepidocyrtus. Optional tools to import terms could be useful as ODK (https://github.com/INCATools/ontology-development-kit/) or Ontofox (http://ontofox.hegroup.org), increasing the performance in this step, but the terms curation must be done by an expert in the taxon. The “descriptive template” has classes and subclasses organized in a hierarchical structure, where class expressions or direct instances are the semantic annotations for each species. For instance, class expression for the absence of scales on antennae is: “antenna SubClassOf not (bearer_of some scaled)”, while the presence of scales is expressed as “antenna SubClassOf (bearer_of some scaled)”.

Continuing with the example above, the class antenna represents an individuals set that shares some property, e. g., part of the head capsule. The individuals are described object property or data property assertion could be made as:

“antenna has_individual some antenna123”, where the individual labeled as “antenna123” is the instance of the class antenna.

and

“antenna123 part_of headcapsule123”, where “part_of abdominalsegment1” is the object property assertion and “headcapsule123” is a provisional label for an individual that is an instance of the class head capsule.

Under the class-based method, the template has a “layer”, the class expressions, and under the instance-based method, the template has two “layers”, the class expressions, and individuals by class.

The morphological descriptions follow the HYA model proposed by Wirkner et al. 2017, including the phenotypic categories: 1. qualitative phenotypes, 2. presence/absence phenotypes, 3. count phenotypes, and 4. relative measurement phenotypes. Terminology related to the description of phenotypes were taken from Phenotype And Trait Ontology (PATO) (Table 1): anatomical side, color pattern, length, morphology (shape, size, and texture), and number; the spatial object properties are enlisted in the Table 2. Semantic annotations are declared through OWL class expressions and Manchester Syntax (http://www.w3.org/TR/owl2-manchester-syntax/) and built in Protégé 5.4.0 Musen 2015.

An RDF store was built with Apache Jena Fuseki, an HTTP interface for querying RDF graphs, which can be explored in a browser as http://localhost:3030//query.html, employing SPARQL according to W3C recommendations (The World Wide Web Consortium). Apache Jena Fuseki was chosen by the simplicity in the installation, in contrast with another web API with SPARQL endpoint, for instance, Openlink Virtuoso, Ontotext, or Neo4j. This created RDF store only holds class-based morphological descriptions for each described species.

Semantic-based morphological descriptions were made for 22 species, whose files in RDF/XML format are available at https://github.com/luis-gonzalez-m/Lepidocyrtus-RDF-Store. These descriptions have an average of 592 anatomical terms, of which 260 are referred for the chaetotaxy. The RDF triple (see below) expresses (a) part-whole relation between anatomical entities (Fig. 2) or (b) between anatomical entities and their qualities. The “absences” are represented as (c) negations, following the principle of Open Word Assumption (Antoniou and van Harmelen 2008). The importance of this assumption relies on the fact that it is not possible to make a full morphological description of an organism (Wirkner et al. 2017). The term “absence” usually contains evolutive assumptions in a phylogenetic context to explain changes of homologs or appearance of evolutive novelties (Sereno 2007, Willmann 2016), but at descriptive level it is considered as a quality. The above expressions are equivalent to the EQ format and have the logical structure for the character statements (Sereno 2007) and discussed by Göpel and Wirkner 2018.

chaeta am6.ab3 part_of some abdominal segment 3..........................................(a)

chaeta am6.ab3 bearer_of some macrochaeta.....................................................(b)

chaeta am6.ab3 not (part_of some abdominal segment 3)..................................(c)

Under the instance-based method, the above is most complex because a second “layer” must be added (Fig. 3), where each class instantiates an individual. For instance, the statement (d) is expressed as:

chaeta145 part_of abdominalsegment3……………………..…..…..……………....………(d)

An object property assertion with the provisional labels “chaeta145” and “abdominalsegment3” to name the individuals or parts of organisms perceived in the reality. Some examples of RDF triples and descriptive statements are present in the Table 3.

RDF store for biological data is oriented mainly to molecular data with Uniprot (https://uniprot.org) and Bio2RDF (https://bio2rdf.org), while for morphological data, RDF stores have not been developed. When the RDF is available, the next step is the creation of a semantic web service to put the semantic data on the web (Wollbrett et al. 2013). In SPARQL query the clauses WHERE and SELECT permit to extract information stored in RDF stores. The WHERE clause obtains data out of the dataset and SELECT names what parts of the dataset are pulled (DuCharme 2013). These clauses return RDF triples as the answer for the query.

A list of RDF triple, where the subclass relation is retrieved for the chaetae that composed the cephalic chaeta (Table 4) is obtained through the code in the example 1. Likewise, a list of RDF triple with the displayed relations bearer_of, and posterior_to, is showed in the Table 5 through the code in the example 2, being a descriptive block for the species L. americanus Cipola & Bellini, 2019:

Example 1 (Table 4)

PREFIX rdfs: http://www.w3.org/2000/01/rdf-schema#
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
SELECT DISTINCT ?s ?p ?o
WHERE { ?s rdfs:subClassOf ?o;
}

Example 2 (Table 5)

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
SELECT DISTINCT ?s ?p ?o
WHERE { ?s rdf:type owl:Class ;
     rdfs:subClassOf [ rdf:type owl:Restriction ;
               owl:onProperty ?p ;
               owl:someValuesFrom ?o; ] ;
}

<!-- http://purl.obolibrary.org/obo/L.americanus.owl#e.lb -->
    <owl:Class rdf:about=“http://purl.obolibrary.org/obo/L.americanus.owl#e.lb”>
        <rdfs:subClassOf rdf:resource=“http://purl.obolibrary.org/obo/L.americanus.owl#labial_chaeta”/>
        <rdfs:subClassOf>
            <owl:Restriction>
                <owl:onProperty rdf:resource=“http://purl.obolibrary.org/obo/L.americanus.owl#bearer_of”/>
                <owl:someValuesFrom rdf:resource=“http://purl.obolibrary.org/obo/L.americanus.owl#ciliated”/>
            </owl:Restriction>
        </rdfs:subClassOf>
        <rdfs:label>chaeta e.lb</rdfs:label>
    </owl:Class>

The querying of individuals is similar to querying ontology classes in Fuseki, but the main difference is the querying of individuals or objects in named graphs. For example, the SPARQL query to access named graph follows the general syntax (Vogt 2019):

SELECT*
WHERE {
GRAPH <123> # 123 is the named graph
}

Semantic-based methods for the documenting of morphological data have gained interest in recent years, motivated by the potential application in phylogenetics (Vogt et al. 2009, Balhoff et al. 2013, Tarasov 2019). This application involves the coding and extraction of the character and character states, and recognition of historical and serial homologies across anatomical entities employing computational reasoning (Cui 2012, Mabee et al. 2019). However, the development of computational tools for the generation of phenotypic annotations from RDF triple has received little attention. There are several limitations to its implementation: 1. these methods require datasets such as anatomic ontology, which are not available for most taxonomic groups, 2. these methods are unknown or less preferable than morphological descriptions in natural language, 3. the attention of character statement coding is relevant in comparison with the expression of descriptive statements, which are framed within taxonomic tradition or publication requirements, and 4. technical limitations. In this study, for example, the descriptive statements were generated manually, but other options such as the declaration of RDF triples in spreadsheets and importing to ontology editor may reduce the time-consuming demand. Also, the extraction of phenotypic annotations from texts has important advances (Wood et al. 2003, Thessen et al. 2012, Dahdul et al. 2015, Cui 2012). For instance, the incorporation of Natural Language Processing (NLP) during the extraction of phenotype data sets could increase the efficiency of this task (Dahdul et al. 2015). However, the generation of phenotypic annotations from RDF triple, employing techniques of Natural Language Generation (NLG) has not been explored or applied in morphological descriptions.

The instance-based method, “semantic anatomy instance”, is most complex when the Abox assertions are built with Protégé because individuals by class need to be specified, resulting in a template composed of two layers: ontology class and instanced objects. Recently proto.morphdbase.de incorporates instance-based methods, promising to increase the use of semantic-based tools during morphological descriptions. This application is complementary to other tools that integrate morphological data with multimedia representations, for instance, MorphoNet (Leggio et al. 2019), MorphoBank (O’Leary and Kaufman 2011), and Phenotools (Eliason et al. 2019). Proto.morphdbase.de has two principal components: semantic graph generated for each organism and accessibility to metadata supplied from scientific publication and regulated by FAIR Guiding Principles to manage stored data (Wilkinson et al. 2016, Vogt 2019).

Currently, there is an imperative need to document biological diversity, which implies the use of computational tools for the processing of different data generated in the biology domain. Unfortunately, this urgency is directed mostly to the storing and processing of molecular data, while the morphology is continually displaced. Morphological descriptions are a useful source of data but due to their nature and complexity requires “creative” solutions, new automatic or semi-automatic methods that permit the interchange between natural language employed commonly in published morphological descriptions and RDF triple syntax. The use of ontologies uncovers the subtle process between morphological data (expressed by RDF triple statement) to character statement, where the character state (properties) arises from the comparison between species and before the building of character matrix.

Initiatives about morphological descriptions that employ standardized languages are not new (Dallwitz 1984, Paterson et al. 2004, Cui 2008). Recently, Cui et al. 2020 developed an author-driven method, where the author or taxonomist defines what ontological classes are necessary for their descriptions. These classes are included by the software engineer in an ontology, this means, the author is immersed in the builiding of the ontology, expressing semantic relations and conflicts in the use of terminology. Although the ability on the management of RDF syntax during phenotypic annotation could be cumbersome, the use of structured syntax in morphological descriptions contributes to making them available to computational analysis.

However, the taxonomic tradition has an important weight in the language employed during morphological descriptions and is taxon-dependent. It is necessary to reconciliate the needs of taxonomists and friendly tools to incorporate these methods. It is not the goal to evaluate the multiple RDF store available, which differs in properties as storage size, querying time, and applicability (Frey et al. 2019). In this study, the building of descriptive templates is suggested, being an intuitive approach to manage RDF triplet and its storage. The semantic-based methods are a tool that increases the management, processing, and comparison of morphological data that could contribute to its integration in the taxonomic work with a friendly computational approach.