Supplementary Table 1

Relative proportions of different character types in datasets derived from monographs and matrices

Supplementary Table 2

Breakdown of EQ complexity scores across EQ statements from matrices (A) and ‘monographs’ (B)

Supplemental Materials

The following reports were generated for each taxa based on the curated matrices and ‘monographs’ from the Phenoscape Knowledgebase. These reports list the entities and qualities used in each EQ statement from these works. These data were used to (1) compare the overlap of entities (E) and phenotypes (EQs) for monographs and matrices for each of the four taxa, and (2) calculate the anatomical entities (E), qualities (Q), and phenotypes (EQ) that were unique to monographs and matrices

Phenoscape Guide to Character Annotation

<p>Archived versions (February 2, 2018 and October 21, 2014) of the Phenoscape project's Guide to Character Annotation. The updated version of the Guide is available on the Phenoscape wiki (http://phenoscape.org/wiki/Guide_to_Character_Annotation).</p

Ontology

Ontolog

Phenotype profiles

Phenotype profile

What is an anatomy ontology?

What is an anatomy ontology

Semantic Similarity Results

Semantic Similarity Result

Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes

<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote

An example of lexigraphically dissimilar phenotype descriptions from two publications [32], [33] that are semantically similar in that they pertain to the same anatomical structure.

<p>The ‘dorsal arrector’ and the ‘posterior pectoral-spine serrae’ are both parts of the pectoral fin, which is immediately apparent to both humans and computers from the structure of the anatomy ontology. Some of the data relationships shown, such as <i>PHENOSCAPE:exhibits</i> and those from CDAO (Comparative Data Analysis Ontology, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010500#pone.0010500-Prosdocimi1" target="_blank">[30]</a>), are not explicit in Phenex. Instead, these are generated by the interpretation of NeXML documents within the Phenoscape Knowledgebase data loading software.</p