=Paper=
{{Paper
|id=Vol-1933/paper-4
|storemode=property
|title=Bottom-up Taxon Characterisations with Shared Knowledge: Describing Specimens in a Semantic Context
|pdfUrl=https://ceur-ws.org/Vol-1933/paper-4.pdf
|volume=Vol-1933
|authors=Patrick Plitzner,Tilo Henning,Andreas Müller,Anton Güntsch,Naouel Karam,Norbert Kilian
|dblpUrl=https://dblp.org/rec/conf/semweb/PlitznerHMGKK17
}}
==Bottom-up Taxon Characterisations with Shared Knowledge: Describing Specimens in a Semantic Context==
https://ceur-ws.org/Vol-1933/paper-4.pdf
Bottom-up taxon characterisations with shared
knowledge: describing specimens in a semantic context
Patrick Plitzner1[0000-0002-7740-5423], Tilo Henning1, Andreas Müller1, Anton Güntsch1,
Naouel Karam2 and Norbert Kilian1
1 Botanic Garden and Botanical Museum Berlin, Freie Universität Berlin, Germany
2
Department of Mathematics and Computer Science, Freie Universität Berlin, Germany
p.plitzner@bgbm.org
Abstract. Using the angiosperm order Caryophyllales, we will provide an ex-
emplar use case on optimizing the taxonomic research process with respect to
delimitation and characterisation (“description”) of taxa using the the European
Distributed Institute of Taxonomy (EDIT) Platform for Cybertaxonomy. The
workflow for sample data handling of the EDIT platform will be extended: Char-
acter data (data on genotypic and phenotypic characters of any type, here focus-
ing on morphology) will be captured and stored in structured form. The structure
consists of character and character state matrices for individual specimens instead
of taxa, which shall allow to generate taxon characterisations by aggregating the
data sets for the individual specimens included. To ensure data integrity, espe-
cially for the aggregation process, semantic web technologies will be used to es-
tablish and continuously elaborate expert community-coordinated exemplar vo-
cabularies with term ontologies and explanations for characters and states. In co-
operation with the "German Federation for Biological Data" (GFBio), the GFBio
Terminology Service is used for publishing the ontologies via a public API. The
EDIT platform will be extended to use and integrate the GFBio Terminology
Service in order to work with the latest version of the ontology used for specimen
respective taxon descriptions.
Keywords: descriptive data, e-taxonomy, terminology management
1 Pre-work and project goals
In a precursor project [1, 2], we have implemented a workflow for processing speci-
men-related metadata on the European Distributed Institute of Taxonomy (EDIT) Plat-
form for Cybertaxonomy [3], a comprehensive taxonomic data management and pub-
lication environment that offers a collection of tools and services and works as a service
provider to support taxonomic workflows, publishing, data storage and exchange, etc.
The aim was to organise the links between (a) samples of individual organisms col-
lected, (b) research data obtained from them, (c) specimens of these individuals depos-
ited in research collections, and (d) taxon assignments (“identifications”) of the inves-
tigated individuals.
On this basis, the current project will optimise the taxonomic research process with
respect to delimitation and characterisation (“description”) of taxa.
Working on the angiosperm order Caryophyllales [4], character data (mainly morpho-
logical data) of individual specimens will be recorded and stored in the underlying
“Common Data Model” (CDM) [5] compliant data store of the platform. For specimen
descriptions, a community-developed expert ontology backed by the GFBio terminol-
ogy service for ontology management is being developed and used to ensure data in-
tegrity. In a final step, data aggregation of the individual character data sets assisted by
the terminology service will generate automated descriptions on taxon level.
This project combines two major scientific areas, semantic descriptions and taxon
characterization both of which are crucial for sustainable scientific work. Taxon char-
acterizations on specimen level allow for generated taxonomic delimitation. However,
this is partly a subjective work leading to different definitions for certain features (leaf
colour is “reddish green” vs “greenish red”). To align different characterizations the
combination with semantically defined terms will relate existing definitions and also
unify newly created ones by proposing existing terms.
2 Terminology service
One of the project goals is to create an ontology for specimen descriptions which should
be used and developed collaboratively. This ontology should be made publicly availa-
ble to increase the reach and usage of the semantic concepts developed for it. The
GFBio terminology service [6], which is simultaneously being implemented, supports
working with formal ontologies, taxonomies or other Semantic Web compliant collec-
tions of terms. It will be used to store and publish the aforementioned ontology. The
service, as seen in Fig 1, provides a web service interface to support various requests
related to retrieving semantic information from the stored ontologies. Another im-
portant feature is the mapping of internal and external terminological resources which
promotes even more the collaborative work on ontologies.
Fig 1 Overview of the GFBio terminology service architecture
3 Specimen Description workflow
Ontologies backed by the terminology service will be created, managed, used and ex-
tended during the entire workflow for specimen based data acquisition and taxon de-
scriptions. Three main applications can be identified, all of which will be integrated
into the EDIT platform as part of the current project (see Fig 2)
Fig 2 The EDIT platform uses the API of the terminology service to integrate the
terminology services into three applications: 1) the term editor which allows editing on a
synced copy of the ontology, 2) the character editor where the user defines taxon specific term
hierarchies for structures, properties and their corresponding states and 3) the character
matrix which serves for the character-based description of single specimens.
3.1 Ontology Management
Ontology editing facilities are implemented into the EDIT platform using the API of
the terminology service. The platform itself provides a user and rights management
which will serve for collaborative work on the ontology preparation and maintenance.
Additionally, the CDM as the storage model adds more fine-grained meta information
to the development process. It allows tracking changes i.e. allowing a versioning mech-
anism and also an extended documentation via annotations and notes is possible.
Working on the ontology within the platform will be done on a synced copy of the
data. The CDM will be extended to support the linkage of terms and their relations as
well as their semantic concept in the remote ontology provided by the terminology ser-
vice.
A term editor based on the EDIT platform is used to visualise and edit the synced
copy.
3.2 Creating the descriptive data set/Character editor
For a comprehensive morphological analysis of a taxon in general as well as specimen-
wise, a well-defined, established terminology is essential that has already been widely
used in the respective plant group. The individual botanist must be able to choose the
necessary terms from a vocabulary that is persistently embedded in or linked to a stable
term-ontology (e.g. The Plant Ontology [7]).
To describe the morphological characters observed, composite terms are used fol-
lowing the tripartite principle proposed by Diederich [8] and realised in the Prometheus
model [9, 10]. That means that characters are composed of three single terms that be-
long to different categories: (1) plant structures, defining the morphological structure
of a plant organism from root to flower, (2) properties, describing the morphological
aspects of the plant structures, (3) states for setting the quantitative or categorical space
of the properties.
Structures and properties will be stored in tree structures into CDM-based data
stores. The tree structure allows for designing taxonomic group specific hierarchies and
dependencies between the single terms. The compilation of structure tree, property tree
and states connected to a taxon is called a descriptive data set.
3.3 Character matrix and aggregation
The first two steps dealt with the conceptual creation of the descriptive data set by
evaluating what terms of the ontology are needed, how they are ordered and how their
boundaries are defined. The final step is the actual description of specimens including
the creation of characters and measuring their states.
As pointed out in the previous chapter, data triplets based on the Prometheus model
are used. Every single character that describes a certain feature of the specimen is built
up from a structure term and a property term. The range of the property term itself is
limited by the states assigned to it.
The specimen descriptions are edited in a character matrix combining all specimens
associated with the taxonomic group of the current descriptive data set with the char-
acters created to describe the morphological features. The matrix can be seen as a table
with ordered rows which will be built up by the characters that were previously created
to describe the taxon. The columns will be the specimens belonging to that certain
taxon. The order of the characters also provides semantic information. There are, for
example, character that cannot exist because the overall structure to which they belong
does not exist as well as a more general character may already define the boundaries of
a sub character.
The editing process will be enriched with the semantic knowledge about the terms.
This enables rules for value hierarchies, data entry assistance through semantic docu-
mentation, data validation, etc.
The ordering of state information into a character matrix enables the procedure of
generating taxon descriptions via an aggregation algorithm. Specimen descriptions will
be comparable to each other because of structured character data organization. Single
characters and their states are semantically defined by the underlying ontology describ-
ing what they are and how to interpret their values. The semantic knowledge also assists
when comparing or merging character data from different sources.
4 Conclusion and Future Work
The EDIT platform in combination with the GFBio terminology service creates a capa-
ble environment for the process of a specimen-based and dynamic description of taxa
using character data. The descriptive data set as a data structure connects the “raw”
specimen character data to a taxonomic group, making data aggregation possible which
allows the generation of automated taxon descriptions. Each application of the work-
flow is based on the platform and the CDM so that the user rights and roles management
system can be set up specifically for each task by granting access only to those users
that are authorized.
In any step of the workflow it is common that requests to change or edit the ontology
will come up. The CDM provides the link to the synced copy of the ontology but any-
way, in a future step, change and versioning strategies should be discussed in more
detail as there are still no established solutions to this problem.
Another advantage of working with semantic technology is reasoning. This will espe-
cially be of interest during the aggregation process when dealing with conflicting data
or generated taxon descriptions vs. descriptions from literature.
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