(chairman) So, let's welcome Egon,

who will describe how he is trying

to improve the coverage 
of chemical compounds in Wikidata.

(Egon) Yeah, thank you.

So, let's see...

Oh, this is not right.

Sorry.

They put the wrong slide deck.

(person 1) The one was better than--
(person 2) [inaudible]

(person 1) (laughs)

How about this?

This one actually says WikidataCon.

Slightly different slides.

Okay, so yeah, coverage and correctness,

accuracy, quality, if you like.

And the other thing here

is what makes it different 
from some of the other things

that we've seen at the WikidataCon

is how I do this quality,

and the coverage, actually.

So I'm actually taking advantage here
of my background,

which is in cheminformatics,

which is something 
that we use in our research.

And cheminformatics

is a way to understanding 
the chemical structures,

what we will see in a moment.

We can do things that we cannot do 
with the regular toolsets

that we have, like Shape Expressions,
quality constraints and sorts.

Now, one of the interesting
things of chemistry

is that chemical structures,

are sometimes the same,
sometimes not the same,

depending on what you want to know.

And this slide reflects that a bit,

and what we see here 
is biologically the same compounds

but chemically two different compounds.

But at biological levels 
with the [inaudible],

they are in equilibrium,

and you will not be able 
to really distinguish between them,

unless you're looking 
for a particular type of biology

like reaction mechanisms.

Another interesting thing 
about Wikidata and Wikipedia

is that we have things 
like long-chain fatty acids,

chemical concepts
which are not a specific compound

but actually a class of compounds.

Now, this class can be based 
on similar features in the molecules,

so like in the case 
of the long-chain fatty acids--

they all have a long-chain fatty
and an acid group.

In other cases, there are the classes

based more
on the biological functionality,

like a certain type of inhibitor,
like an ACE inhibitor.

And this introduces 
a lot of interesting things,

partly because of this close link 
with Wikipedias.

And one of the things that we see

is that Wikipedia may have a chembox 
for a particular compound

bur actually be about a compound class,

resulting to a slightly different concept

of what the two things 
are actually meaning

and the sitelink being more complicated.

We need this 
for understanding the biology.

So the research in our group 
is understanding the living cell.

The system's biology here 
described in the biological process

is we have a pathway database 
for that--WikiPathways,

and if we look at the chemistry in there
of the small molecules,

the chemistry is sometimes 
described in a lot of details,

sometimes in less detail,

pretty much like this Wikipedia
Wikidata link that we just had

resulting in basically links
to a lot of different databases

with slightly different focuses.

Some databases like LIPID MAPS
and the Human Metabolome Database,

they are very much focused on the biology,

whereas a database like ChEBI,

that's very much focused 
on the chemical entities.

So we try to breach that,

and that two-three years ago gave us
a very interesting insight

that if you look at the lines here

where in blue we have the total number 
of the small molecules

we have in these Pathways

and the numbers in red 
that we can match to that,

there is this gap, and this gap 
is complicated chemistry.

Also, poignantly, things
missing in Wikidata.

So therefore the need 
for date completeness

and the data quality.

And here we have an example.

This is actually 
a curation report of yesterday,

and these are still things 
that we have in Pathways

but that we do not know 
what the equivalent thing is in Wikidata.

And one of the things here
that I'm picking out here

is strigolactone.

And this is a class of compounds.

So we have that in one of our Pathways,
this particular Pathway over there.

So you start matching this 
to Wikidata and Wikipedia,

and to actually use
for this compound Wikipedia page

with these six structures--

images, name, no links, nothing.

Nothing in Wikipedia,

just this information, 
not machine-readable.

So based on the name, 
I can actually find three out of the six

of these compounds in Wikidata,

not linked, not classified.

So if we look at the class 
of strigolactones,

of which these six are examples,

Wikidata did not give us anything.

So that's the kind of curation
that I'm interested in.

On the right here--
that page is actually pretty much empty,

but it's exactly what Scholia is showing
for this class of chemical compounds.

So Scholia is one of the tools 
that I've been using to do this curation.

This missing classification
is a bit of information

missing in Wikidata,

but we can add this classification,

and we can retrieve that 
from some sources.

We will see with LIPID MAPS later,

we can automate 
adding these missing links,

if we understand the chemistry.

So this diagram over here--
we have fatty acid over there again

and the long-chain fatty acid over here

that we saw on one
of the previous slides--

very long-chain fatty acids 
and a number of other fatty acids.

This kind of information helps us see

a [inaudible] of the chemistry 
in Wikidata.

Scholia can visualize the 2D structure,

and this thing is actually 
automatically generated

from the chemical structure in Wikidata

on the fly creating 
in the Scalable Vector Graphics.

(coughing)

Sorry.

With the stereochemistry annotation there

to help the chemist see 
the completeness of the data

because also the stereochemistry 
might be missing.

We also get an overview on Scholia 
of related compounds

based on the InChIKey,

where the first block basically indicates
how the atoms are connected

and the second column 
indicates things like stereochemistry

and things like 
which isotopes are in there,

for example C11 instead of C12

or C13 instead of C12.

The last number 
of the last letter over here

actually indicates the charge,

so that's the example that we saw earlier
between the citric acid and the citrate,

or was it the acetic acid and the acetate?

By putting in a bit of the main knowledge,

we can do a lot more... making sense 
of what we have in Wikidata.

A bit more about Scholia

is that about data completeness 
with the physical and chemical properties,

the literature, those are whole things 
that we want to have access to.

But it only works if we can find 
the right chemical in Wikidata.

We started using Wikidata 
in a number of our projects,

so WikiPathways was one of them.

This is another project

in the area of the nanosafety, 
risk assessment,

where they use OECD testing guidelines

and using Wikidata here 
to make an overview of the experiments.

And this means that we can now
actually start annotating articles

where these protocols have been used.

And in this way, we get a better insight 
in the quality of literature as well.

We get to see which DDTs

are well tested, established 
experimental methods

and an indication of how good the data is 
that came out of that.

Another example--this is nanomaterials,

specific nanomaterials,

where there is a unique code-- 
we've added that--

with the same purpose of being able

to track down literature 
about these nanomaterials.

But, again, we need exact descriptions.

Now, this is 
the LIPID MAPS classification,

and here we see an interesting thing,

and this has shown up 
in some of the presentations,

elsewhere as well.

This idea that some of the things 
that we have in Wikidata

is not always matching the sources.

So different ontological models,

different ideas 
of what a particular thing means.

And so, if we look at the LIPID MAPS,
we have a lipid in the middle

and then a number of classes,

and many of these are in Wikidata.

But here, around actually 
fatty acids or fatty acyls,

that's where there is a mismatch

causing something that should be 
actually purely hierarchical,

actually it started to show 
some loops over there,

the mismatch of two representations 
of a lipid chemistry.

Now, the goal of this work
is not so much to reconcile this

but to visualize it

so that we can understand 
what is going on

and correct things 
that are actually clearly wrong.

The interesting about LIPID MAPS 
is actually that the classification

is indicated in the external identifier.

So one of the things 
that we've been using

is these external numbers
to make this automatic classification

because everything 
that starts with an LMFA05

is actually a fatty alcohol.

So I can translate that 
into Quickstatements,

push that into Quickstatements

and get that annotated in Wikidata.

This slide is just reflecting 
the advantage for LIPID MAPS here

which been collaborating with them

because they get a lot of data
out of Wikidata as well,

which we can cross-reference, 
which we can compare if it's correct.

LIPID MAPS is a quite curated database

but like everyone actually having trouble

with access to literature,

the demand of literature 
and filtering the literature,

getting to the right articles.

Shape Expressions is probably 
something that you've seen.

We have a few of them for chemistry now.

This is the example for racemic mixture.

In a case of racemic mixture,
you want to have two parts in there.

It's a mixture,

so at least two chemical entities 
need to be in there.

Moreover, each of the [inaudible] parts
has to be a chemical compound.

This is another level of a way
we can curate the content.

There have to be more of them.

We have quite a few 
different concepts in Wikidata

like groups of co-compounds.

There is a class that is 
of structurally similar compounds, etc.

If you run a query like this,
this case for the other one,

other schema that we have
for chemical elements,

you can do the same thing-- 
you can run it on a single item

or you can run that on everything 
that is a chemical element.

This is something 
that I can very much recommend

having a look at 
if you have not done so already.

Now, if we go to the automation of things,

here I'm using a tool called Bioclipse.

This is something that we worked on
some time ten years ago.

It's a platform 
for chemistry and biology,

or cheminformatics and bioinformatics.

aimed at automating things,

including visualizations and sorts.

But I've taken that now 
and developed a number of scripts

that I can actually run 
on the command line,

which makes it easier to automate things,
as we will see in a moment,

and doing all sort of things,

for example, the classification 
according to the LIPID MAP identifiers,

that's the scripts all available 
from the GitHub repository here.

And typically, I have them
create Quickstatements

because that gets me 
an additional check step

after I created the Quickstatements 
and see what does data actually look like.

Annotation of main subjects.

This one is my script too, 
starting from SMILES

to actually add chemical compounds
that are not in Wikidata yet,

which happens a lot.

So three or four weeks ago 
I added something like 500 compounds

which our project was looking into

because these are 
volatile compounds in oils.

This script adds the compounds.

They will later on add the annotation 
of which pieces that compound comes from

and what the properties are.

Bioclipse itself is based 
on the Chemistry Development Kit

and a few other libraries.

This allows me to do the chemistry.

And this is a very well-validated toolkit.

The SMILES part has been done 
by John Mayfield.

I have done a lot of validation 
against other tools.

And the quality 
is actually really high now,

comparable or in some cases even better 
of commercial cheminformatics tools.

It has given me a lot of reassurance

that the quality checking that we do 
with this tool on Wikidata

is giving interesting results.

This is the Quickstatements.

Quickstatements
is Magnus' work, of course.

What happens if we take the SMILES,
it calculates the InChI,

and the InChIKey, it even looks up 
based on the InChIKey,

if there is a PubChem identifier

that uses the InChIKey, 
the PubChem identifier,

to see if this compound 
already is in Wikidata.

And only if it's not already there,

then it will actually create
a CREATE statement.

A bit of automatic classification
here is an option.

So if I'm adding a class of compounds,

I can automatically indicate 
what these are all...

this type of compounds,

and I can also indicate, if needed,

if there is a particular article
where I got this information

from automatically adding references.

Well, this is what 
the Quickstatements output looks like

for the annotation of main subjects.

You've probably seen that as well.

A newer thing that I started doing

is actually doing reasoning 
on the data in Wikidata.

So if I have the SMILES, then I can check 
the molecular formula, for example.

I can check the InChIKey.

At some point, what we are going to do
is calculate physicochemical properties

and see if that matches 
what is in Wikidata.

This will highlight typos

or wrong units, for example.

At this moment... 
so this is a run of this morning.

What we see here 
is two tests actually failing,

and this is an example of it.

This is the InChIKey
that is computed from the isomeric SMILES

is different from the InChIKey
given in the entry.

This can result from data 
being pulled in from different resources.

So these are entries, about 300 of them,

in the 160,000 chemicals 
that we have in Wikidata.

So it's a very small amount, really,

where there is information, 
and someone needs to look at it.

Now, these are all organic compounds

and also quite a few inorganic compounds

where these things just work less well.

But I found in the other test 
that is failing

immediately a couple of things 
that are very clearly wrong.

PubChem is a huge database.

They do validation as well.

We are in the process 
of submitting Wikidata there,

which I'm really happy about.

It's in the last validation step 
at this moment.

And this will also mean that PubChem,

which has something 
like 100 million compounds

will actually link back to Wikidata.

It already does this, but via Wikipedia.

(laughing)

Do you recognize it?

With the aforementioned issues there 
of concept mismatches.

So this will give us a second thing.

And there, also, 
using the same Bioclipse scripts

or similar Bioclipse scripts,

we get validation reports,

again indicating things 
that chemists should look at.

That basically wraps it up.

This is still a work in progress,
the article is in preparation.

I've been working 
with Finn here in Scholia

to support this validation.

We're writing up the full work, 
but for now you can look up this poster.

The slides are on the program
of this session,

so you can look at the slides
and look at the details.

And a quick acknowledgment:

some of this work has been done 
by a number of grants that I received.

And thank you very much.

(applause)

(chairman) Are there any questions?

(person 3) Thank you so much for this.

I am [inaudible],

and so far, I've been reading articles

on the [inaudible] Quickipedia 
on different compounds.

I have a little bit more than 70 articles 
with different compounds--

just things I come across.

And my question to you is

if I want to move my chemistry activity 
from Wikipedia to Wikidata,

how can I help
in a way that is very friendly

to somebody who is a beginner 
in that field on Wikidata?

So, if that compound is in Wikipedia and..

Sometimes there is 
actually a Wikidata page.

I occasionally run into this as well,

in the last couple of months 
not so much anymore

but this morning, actually.

And what I typically do then

is I take the SMILES
from [inaudible] infobox

from that compound

or use PubChem to look up the SMILES,
check if the information is complete,

particularly the stereochemistry,

and then I use that 
that creates Wikidata item scripts

to create Quickstatements 
for that compound.

If there already is a Wikidata item,

I basically just update these scripts,

but rather than say, "Create Last,"
I replace the last with the Q-codes

that that item already has.

And then it complements 
or it adds this information

based on the information we had.

This is [manuable],

so you can copy-paste 
a number of SMILES, put it in a file,

and take that.

Extracting that information to Wikidata 
is not something I've automated yet,

but this helps me... 
it's a pretty fast process.

I can show you later 
how to use that software.

(chairman) Are there other questions?

So, I have one.

Do you make an effort 
to, in fact, make this more visible

in this bioinformatics community

so that they can start using
this structured data?

Yeah, I'm actively doing that.

So what I did not mention 
in this presentation so much,

but we saw that in...
I'd have somewhere to start here--

this is an overview 
of different databases.

A similar plot, which actually 
I do not have on this slide deck

is the number of different identifiers
that chemical compounds have,

and I've been working 
with a number of databases,

like MassBank, 
the Environmental Protection Agency,

CompTox Dashboard.

I've added links to the BDB database.

So I'm working with a number of projects

for pulling in additional information,

identifies our links out
to other databases.

Regarding outreach, yes,

so that wrong slide deck
that I was showing at the start,

there was actually 
a presentation two weeks ago

at an Open Science Meeting 
around chemistry.

I'm very much pushing this and...

I see a big future here.

There's a lot of interest.

And making people aware 
of the CC0 license,

that's typically the larger problem.

So we have to pull in
the information carefully.

(chairman) Other questions?

- Okay. 
- Thank you very much.

(chairman) Can we thank the speaker.

(applause)