## Sunday, September 27, 2015

### Coding an OWL ontology in HTML5 and RDFa

There are many fancy tools to edit ontologies. I like simple editors, like nano. And like any hacker, I can hack OWL ontologies in nano. The hacking implies OWL was never meant to be hacked on a simple text editor; I am not sure that is really true. Anyways, HTML5 and RDFa will do fine, and here is a brief write up. This post will not cover the basics of RDFa and does assume you already know how triples work. If not, read this RDFa primer first.

The BridgeDb DataSource Ontology
This example uses the BridgeDb DataSource Ontology, created by BridgeDb developers from Manchester University (Christian, Stian, and Alasdair). The ontology covers describing data sources of identifiers, a technology outlined in the BridgeDb paper by Martijn (see below) as well as terms from the Open PHACTS Dataset Descriptions for the Open Pharmacological Space by Alasdair et al.

Because I needed to put this online for Open PHACTS (BTW, the project won a big award!) and our previous solution did not work well enough anymore. You may also see the HTML of the result first. You may also want to verify it really is HTML: here is the HTML5 validation report. Also, you may be interested in what the ontology in RDF looks like: here is the extracted RDF for the ontology. Now follow the HTML+RDFa snippets. First, the ontology details (actually, I have it split up):

<div about="http://vocabularies.bridgedb.org/ops#"
typeof="owl:Ontology">
<h1>The <span property="rdfs:label">BridgeDb DataSource Ontology</span>
(version <span property="owl:versionInfo">2.1.0</span>)</h1>
<p>
<a property="rdfs:seeAlso" href="http://www.bridgedb.org/">homepage</a> too!
</p>
</div>
The OWL ontology can be extracted
<a property="owl:versionIRI"
href="http://www.w3.org/2012/pyRdfa/extract?uri=http://vocabularies.bridgedb.org/ops#">here</a>.
The Open PHACTS specification on
<a property="rdf:seeAlso"
>Dataset Descriptions</a> is also useful.
</p>


This is the last time I show the color coding, but for a first time it is useful. In red are basically the predicates, where @about indicates a new resource is started, @typeof defines the rdf:type, and @property indicates all other predicates. The blue and green blobs are literals and object resources, respectively. If you work this out, you get this OWL code (more or less):

bridgedb: a owl:Ontology;
rdfs:label "BridgeDb DataSource Ontology"@en;
rdf:seeAlso
rdfs:seeAlso <http://www.bridgedb.org/>;
owl:versionIRI
<http://www.w3.org/2012/pyRdfa/extract?uri=http://vocabularies.bridgedb.org/ops#>;
owl:versionInfo "2.1.0"@en .


An OWL class
Defining OWL classes are using the same approach: define the resource it is @about, define the @typeOf and giving is properties. BTW, note that I added a @id so that ontology terms can be looked up using the HTML # functionality. For example:

<div id="DataSource"
typeof="owl:Class">
<h3 property="rdfs:label">Data Source</h3>
<p property="dc:description">A resource that defines
identifiers for some biological entity, like a gene,
protein, or metabolite.</p>
</div>

An OWL object property
Defining an OWL data property is pretty much the same, but note that we can arbitrary add additional things, making use of <span>, <div>, and <p> elements. The following example also defines the rdfs:domain and rdfs:range:

<div id="aboutOrganism"
typeof="owl:ObjectProperty">
<p><span property="dc:description">Organism for all entities
with identifiers from this datasource.</span>
This property has
<a property="rdfs:domain"
href="http://vocabularies.bridgedb.org/ops#DataSource">DataSource</a>
as domain and
<a property="rdfs:range"
href="http://vocabularies.bridgedb.org/ops#Organism">Organism</a>
as range.</p>
</div>


So, now anyone can host an OWL ontology with dereferencable terms: to remove confusion, I have used the full URLs of the terms in @about attributes.

Van Iersel, M. P., Pico, A. R., Kelder, T., Gao, J., Ho, I., Hanspers, K., Conklin, B. R., Evelo, C. T., Jan. 2010. The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. BMC Bioinformatics 11 (1), 5+. http://dx.doi.org/10.1186/1471-2105-11-5

## Saturday, September 19, 2015

### #Altmetrics on CiteULike entries in R

I wanted to know when a set of publications I was aggregating on CiteULike was published. The number of publications per year, for example. I did a quick Google but could not find an R package to client to the CiteULike API, and because I wanted to play with JSON in R anyway, I created a citeuliker package. Because I'm a liker of CiteULike (see these posts). Well, to me that makes sense.

citeuliker uses jsonlite, plyr, and curl (and testthat for testing). The first converts the JSON returned by the API to a R data structure. The package unfolds the "published" field, so that I can more easily plot things by year. I use this code for that:
data[,"year"] <- laply(data[,"published"], function(x) {
if (length(x) < 1) return(NA) else return(x[1])
})
The laply() method comes from the plyr package. For example, if I want to see when the publications were published that I collected in my CiteULike library, I type:
barplot(table(citeuliker::getData(user="egonw")[,"year"]))
That then looks like the plot in the top-right of this post. And, yes, I have a publication from 1777 in my library :) See the reference at the bottom of this page.

Getting all the DOIs from my library is trivial too now:
data <- citeuliker::getData(user="egonw")
doi <- as.vector(na.omit(data[,"doi"]))
I guess the as.vector() to remove attributes can be done more efficiently; suggestions welcome.

Now, this makes it really easy to aggregate #altmetrics, because the rOpenSci people provide the rAltmetric package, and I can simply do (continuing from the above):
library(rAltmetric) acuna <- altmetrics(doi=dois[6]);
acuna_data <- altmetric_data(acuna);
plot(acuna)

And then I get something like this:

Following the tutorial, I can easily get #altmetrics for all my DOIs, and plot a histogram of my Altmetric scores (make sure you have the plyr library loaded):
raw_metrics <- lapply(dois, function(x) altmetrics(doi = x))
metric_data <- ldply(raw_metrics, altmetric_data
hist(metric_data$score, main="Altmetric scores", xlab="score") That gives me this follow distribution: The percentile statistics are also useful to me. After all, there is a clear pressure to have impact with your research. Getting your research known is a first step there. That's why we submit abstracts for orals and posters too. Advertisement. Anyway, there is enough to be said about how useful #altmetrics are, and my main interest is in using them to see what people say about that, but I don't have time now to do anything with that (it's about time for dinner and Dr. Who). But, as a last plot, and happy my online presence is useful for something, here a plot of the percentile of my papers in the journal it was published in and for the full Altmetric.com corpus: plot( as.vector(metric_data$context.all.pct),
as.vector(metric_data\$context.journal.pct),
xlab="pct all", ylab="pct journal"
)
abline(0,1)
This is the result:

This figure shows that my social campaign puts many of my publications in the top 10. That's a start. Of course, these do not link one-to-one to citations, which are valued more by many, even though it also does not reflect well the true impact. Sadly, scientists here commonly ignore that the citation count also includes cito:disagreesWith and cito:citesAsAuthority.

Anyways... I think I need other R packages for getting citation counts from Google Scholar, Web of Science, and Scopus.

Scheele, C. W., 1777. Chemische Abhandlung von der Luft und dem Feuer.
Mietchen, D., Others, M., Anonymous, Hagedorn, G., Jan. 2015. Enabling open science: Wikidata for research. http://dx.doi.org/10.5281/zenodo.13906