Developing a Module for Generating Formalized Semantic Morphological Descriptions for Morph∙D∙Base

By

Meid, Sandra
Baum, Roman
Bhatty, Philipp
Grobe, Peter
Köhler, Christian
Quast, Björn
Vogt, Lars

Type

Article

Date of Publication

2017-7-25

Original Publication

Metabarcoding and metagenomics

Volume

1

Pages

1--2

Subjects

 morphology ,  triple store , content management system , formalized semantic morphological description , free-text descriptions , knowledge base , linked open data , Linked Open Data , metadata , Morph∙D∙Base , ontology , ontology-controlled application , ontology-terms , partonomy , semantic annotation

Contributed by

Pensoft Publishers

Language

English

Published by

Bulgaria Pensoft Publishers 2017-

Identifiers

DOI: https://doi.org/10.3897/tdwgproceedings.1.15141

 

Abstract

We demonstrate the early prototype of a new module for Morph∙D∙Base that allows the generation of highly formalized semantic morphological descriptions (http://escience.biowikifarm.net/wiki/EScience-Compliant_Standards_for_Morphology). The resulting morphological descriptions follow the individuals-based Instance Anatomy data scheme (as opposed to the class-based Semantic Phenotypes data scheme). The module allows the description of a specimen's anatomy by generating a granular representation of the parts of the specimen to be described, using ontology-terms from known ontologies. This results in a hierarchy of parts and subparts (partonomy), which serves as organizational backbone of the entire description, with each part representing a section of the description to which you can navigate using the partonomy. The module allows the description of each part from the partonomy using (1) a set of formalized input forms, which also allow the specification of metadata for each input field, (2) a text-widget for providing conventional free-text descriptions, which can be semantically enriched through annotating them with ontology-terms of (user-)selected ontologies, and (3) an image-widget for linking images, which allows semantically enriching each image by specifying regions of interest and annotating them with ontology-terms of (user-)selected ontologies. This new module is based on SOCCOMAS, an application for semantic ontology-controlled Web-Content-Management-Systems that we are currently developing (http://escience.biowikifarm.net/wiki/SOCCOMAS:_an_application_for_semantic_ontology-controlled_Web-Content-Management-Systems).