[Ginny Pannabecker] Good morning everyone, thank you for
coming today. My name is Ginny Pannabecker. I'm here with the VT
University Libraries. So I'd like to welcome our guest
speakers here today from ContentMine, Stefan Kasberger and Thomas Arrow.
So in the back on the table where you signed in, there's also a flyer
that tells more about other sessions we have today and
tomorrow. One of those is a text and data mining forum where we
have a panel of researchers and also Tom joining us to talk about
text and data mining for research. That's today at 2:30. Tomorrow is a
in-depth workshop to try out the ContentMine tools. We still have
a few spaces left, so if you're interested please feel free to RSVP for
that. Those are all at this link here, so
thank you very much and i'll let you get started.
[Stefan Kasberger] Thank you very much Ginny, we're very happy to be here
at Virginia Tech during the Open Data Week which i find
is a fantastic event series to focus on this very important issue
for a few days and to organize all kinds of different events around this.
My name is Stefan Kasberger, I'm coming from Austria and I'm part of the ContentMine
project where i'm responsible for the trainings.
My scientific background is I'm active in the field of computational social
science and i'm studying this
geographic focus in Austria. Next to me is Tom Arrow.
He's one of the developers of the software
we use and is coming from UK.
And the plan for now is that we give you a short introduction into what text data
mining in general is, how it is applied
specifically for scholarly literature because that's what we do, we
use scientific publications to do text data mining,
and then also show you a little bit about how our software works
and what we do besides developing a text data mining software.
so before we go we start with the introduction
a thing which is very important for me because openness is always about sharing.
It's always about making information accessible.
And there is the Virginia Tech open data week they use this
hashtag so if you would like to share your questions or your thoughts uh with
with other people in here but also out there
please use the #vtodw hashtag uh yeah and it exists for all
the formats um and we also have tomorrow
from nine to four uh from nine a.m to four p.m a
hands-on workshop a full day one where we won't explicitly focus
on your request your research questions on your interests so you are heartily
welcome to join us tomorrow and get a first hands-on experience of the
software we will show you and we also have a
panel discussion today with Tom on the panel which is about
text data mining in general so i think these are
very interesting events during this week so let's get into the uh
text data mining stuff and the basic question always is
who maybe let's start like this who of you knows
what text data mining is a little bit or who have heard of it
okay yeah that's that's the best audience so far
so uh then because it's a term used uh
and um often it's not really clear what is about uh what is behind it
and there is no real exact definition like that's
how it is but there are different approaches towards it but in general you
always can say it's always about text because there is
data mining which uses structured data but text data mining always has its
input data text um and it's all it always
gives you the um
uh yeah it always tries to look on hidden knowledge that that's
especially especially uh important for this because normally you try to find
knowledge which is obvious but it always tries to find out relations and
structural information uh and aggregated information behind the
obvious words of a text so like how do entities of words
co-occur with each other uh and to get some
aggregated statistics about stuff like this and it tries to
reveal patterns and relations uh behind the obvious and as you see there
are three very important parts who drive the whole
area which is the ubiquitous uh access to internet uh and also to
computational power uh there is moore's law with doubling the computational
power each year and as you also can see there is a huge
production of data except expected uh for 2020 with 60 trillion
gigabytes it will not get less so you see there is
a there is a lot of dynamic uh
into this market into this uh technique um yeah
into these methods it depends on where where you're
at home uh how you call this stuff so in the introduction now i want to give
you a general overview of the field not all the things i tell you now is
done by us that's just really the big idea of text
data mining so let's start with the goals uh people
expect to uh come out of text data mining
and the first one is um one of them most applied one is to find the
documents which are of relevance to your your research
question or to your business uh plan because you have for example one
million scientific publications and you want to find just 101 which are
interesting for you for your phd thesis or for your next publication you
want you want to find the needle in the
haystack and that's one of the most important applications of tdm
because you need to know what words what knowledge is hidden
inside the 1 million papers that's not a very trivial
thing to do without machines the second thing is you want to find parents and
relations and especially you want to find new uh
patterns which only can be found if you have a certain
degree a certain mass of data input it's
it's not working with 20 publications but if you have
1 000 or even like 100 000 publications you maybe find information uh especially
like frequencies which are not that obvious from reading
20 papers the third one is as always when it is
about machine learning data analytics and stuff like that you always want to
reduce complexity because if you can reduce the complexity of one million
publications uh to like a few factors uh
you have new ways how you can analyze and present the outcome so you
also can access new methods like you can print out a network
diagram if you print out one with one million nodes
you will not see anything but if you reduce it to 20 factors you can make a
network visualization and you can find relations between
topics entities a little bit easier so reducing
complexity is always easy because humans need to uh have less complexity like
ss is in a data set of one million publications
one of the thing we are heavily doing is extracting
specific knowledge especially like entities and facts
uh you very often want to have specific terms out of uh you want to have all the
species in a publication you want to have all names or
organization names out of their like funding bodies or city
names uh or like drugs so you want to have
uh entities extracted because that's what you're interested
uh in if you're making research in pandemics you want to have species
and drugs for example so it depends on your domain
which kind of entities you want but you want to have some entities
and now we're going to the biggest societal uh
issues of our big dream of this whole big data discussion
is always to reduce the workload for humans
and it is especially important with the next point
you don't want the machines that they decide everything for us that can be
very tricky and also very dangerous but
normally we discuss algorithms and models like
this in a way that is meant that it gives
support for humans it's called decision support systems
like here you have some data analysis but at the end someone must decide
uh do we take it serious the ranking or the
the the number which comes out or not so that's one of the actual discussions
going on uh one thing that is also heavily
used already is uh to use it as a preparational step for
further downstream analytics stuff so when you once you have
unstructured text input and you detect data mining you get out some
uh structured semi-structured numeric information
quite often so like a value of a topic or
some correlation stuff um and you then can use this to do
usual data mining you can apply the machine learning stuff you can do the
usual statistical uh analytics stuff you always can do with this so this is
always a very huge part uh in workload but sometimes it's not
seen because it's the preparational step before you apply
some other uh methods on it and the four at the eighth and last
point is capability because it's always about when you apply
algorithms uh that the dream is that its case very
well so you want to implement an algorithm or and process
a data pipeline and if it does not matter if you put in
1 000 documents or 100 million documents you just add a little bit more
course or or clusters but in general it scales very easy
so next thing is when we talk about text data mining already said it's always
about text that's a big difference and it's always unstructured or weakly
structured because text is always different think of for example
of scientific publications there is always
an abstract but what's in the abstract is always
different hopefully so uh that's that's weekly structure
unstructured information because if you have a database with a column
which is called age always there is agender and this will be a numeric
information about 42 years or stuff like that
and the second thing about data to know is
that you have to have some kind of like similar context
uh of your data so you can analyze it uh maybe the size think of like uh
one word in a tweet has a different meaning than one word in a book
the weight i would say is different or the same language of course because
if you use german with english you maybe mess up what the thing is all
about and also like the topic if you're doing uh research about your own field
of research for me example is uh social geography uh that's like the
context uh for it that it's maybe then different
to when business analytics does something in the
field of marketing so um to understand a little bit the usual
process when you apply a text data mining you always start with
the crawling so that this is basically means you download the publication
in our case or you download the text onto your local
computer or your server but you have to create the copy
on your pc to then be able to apply tdm techniques
that's very important because we will come later to the copyright issues
uh because that's a critical step with this then you
want to scrape it it means like you have html or you have xml or you have pdfs or
whatever format you download you want to get out the information uh
you need later on and put it into your structure which you
then can apply further on some other data mining
techniques so once you put it in an uh standardized
structure uh you then can take this and process it and do the text
data mining this that's the actual text the mining part is the text data mining
part where you extract the information relevant for you as said before
this could be for example uh the entities
like person names or drugs or genes or whatever
depending on your issue and then you do the usual science part then you start
with that analysis uh you can do network analysis
uh later semantic analysis text analysis
stuff and so on and then you have of course to evaluate
and validate your outcome and present it to others and
here are some actual applications about tdm and the first one is financial
markets for you you there is a very nice example of
which i got told just 10 15 years ago so it's not enough
something new for example when uh uh trading agencies in london or in
frankfurt uh think about their job they are
looking out for press releases of big companies
how their outcome of the next quarter will be and they get
in some rss feeds how is siemens doing in the
next quarter and if they say oh we are doing better than we expected to
they grab the text analyze it for words and say like okay
uh it seems like uh the the profit is growing
so automatically some shares from siemens get bought
that's happening for 10 20 years uh maybe on financial markets with amazon
is the same with marketing you get
recommendations on facebook or wherever which book you
would may like sometimes there is some text involved in behind
maybe some emails get scanned uh and stuff like that
spam filtering is pretty pretty obvious uh you want to
you recognize if there is spam by words inside the email
for example if you have to work viagra in an
email uh the chances are very high that it's spam uh
i don't have a better example because there's someone everyone knows i think
um and elections there was a heavy discussion about cambridge analytica
uh on the on the american elections the u.s elections the last one
and of course tax data mining is used because it's the same technique as with
marketing it's just a different goal you want to achieve it's uh but
it's also like a b testing and micro targeting individuals to do and or
to buy stuff you want them to and the last
slide of the general introduction is about
the market so you i want to give an idea of how big this whole
market of big data is about and especially what tdm is in about and you
see tdm is growing over proportionally to
the big data market so it's one of the main parts of this
whole bus and it will even grow more and there are
not as many people uh who can apply as there is demand from
companies from research agencies whatever
uh to do it so this will be a major driver
of economic policies and research policies i guess in the next
years all right um yeah so let's narrow it down a little
bit to the uh scholarly literature
which is basically the stuff we do you can do text data mining with all kind of
text like tweets wikipedia websites whatever but we use uh
scientific publications and that's the stuff we normally work
with uh you see a typical publication and
what's very important to know about the specifics is the publication is
mostly owned by publishers this is the copyright issue will come
back later on but that that's really the specific
about it and it's sadly still very often pdf
and pdf is good for our eyes good for our printers
but not good for our computers because it's not text
they just say it's like a d on x y and that is an a x y plus 10
so it's like on a coordinate system but it's not like a string of characters
as a computer normally works uh so this is very complicated a lot of
money goes into converting pdf into structured text
a lot of research project it's amazing but we still produce a lot of
very very unusual information every day and there are also all kinds of
different restrictions on the usage of scientific publications
you are often not allowed to do text data mining with the
pdf or the xml or whatever you get from the publishers you you're just allowed
to read it but not to mine it and the formats are different
some of html xml pdf whatever it makes everything very messy and very
complicated there is no standard uh procedure in this and you also have
different ways of access you have science direct maybe you have
archive you have kind of repository you maybe go
to also some like academia and stuff like
this but you see it's not just one channel you
make a request and then you get the relevant information back
and so uh i want to give now three examples uh how you
can apply text data mine in a scientific literature
and what you can get out of it the first one is phytogenetic trees
it's the tree of life you want to have the systematic
relation of species and all the yeah the relations of the families and
stuff like that i'm not uh into biology
as you may already recognize and the idea is
uh to create the world's largest super tree because normally you publish a
single small relation a small tree in a publication
and you have like thousands maybe millions of publications
where are small trees in and you want to extract them and combine
them and then you create a uh super tree and
that was one thing done from the of mexican
literature by ross montz and that's the idea because that's a
very important core data set of doing research in
phytogenetic tree because once you have a
complete or at least a very good uh tree you can then use this structural
information for further analytics the second one is pandemics
um and we did now some stuff about zika virus
uh do you know zika virus last year in
brazil or around the olympics it was heavily in media and the thing is when
you when a pandemic starts you cannot really
prepare for this because it's just event triggered so you find out there is ebola
there is usuto or in this case uh the zika virus
happening so then you need to start to solve the
problem and the first thing is you need to have an overview of your research
field you need to find out who has done
research about seeker what's the actual status quo of uh the
seeker research globally not just on my department you
want to solve the problem because people are dying every day from it
so it's really an important thing and so you try to dive into it and you need
to have access to scientific publications and you apply
text data mining because you want to find
drugs used or this is the aedes aegypti the transmitting mosquito on the picture
and you want to find which species are named similar
with with as a chip in the general uh scientific literature maybe you find out
there if there is some other transmitting animal
maybe you find out that there is a another animal
helping you that the transmitting animal uh
cannot do the transmission of the virus for example
or you want to find out drugs which are used in
familiar or similar viruses like uh dengue uh uh
to cure uh the virus sort of stop it and the third
application is systematic reveal which is especially
one of the core activities of librarians and you have to keep in mind that there
are over half a million uis created each month which is a massive production
of knowledge there is exponential growth of publications and it will stay
that way
and the job of uh librarians is often uh to really want to find out is a new
publication of relevance to the researchers at your
department or your university or for the student so they have to manually read
papers and say like okay this is relevant this is not relevant we need
this to send to them and stuff like this and
you can pretty easily imagine when humans have to do it
it's quite a lot of work when you publish half a million
uh papers it's not just all papers but there are a lot of papers in there
and algorithms can help you to filter out and to
support you in this in this massive process of
organizing knowledge
so sounds really great i mean everyone maybe can think of some amazing
applications now in your own field of interest
and how this could help to make the world a better place and to solve real
problems but as always there is some problem
behind there and it's very often the same problem
it's the question who owns uh the information in this case
and the it all comes down to the central problem
that one when you want to do text data mining you need to copy
uh the document onto your machine where you can
apply the algorithms and the documents are owned by publishers
so this is where the copyright issue comes into your work
and there are two ways how to resolve this problem
in a legal way the first one is to get allowance by the copyright owner
in most cases it's the publishers
and the second one is to have a national exception a copyright law so when the
publisher says we don't want to the copyright law in
the u.s is stronger than the terms of use of a publisher so that's
the better way to address but there are always problems uh
involved in this process and when you want to get allowance by the copyright
owner you have some uh already some issues
which makes it really difficult and we do not see it as
an uh reasonable way uh on the short run to
go this because they sometimes have unreasonable high
fees if you want to use their information
and to tax data mining they have some kind of log
in mechanics working like you have to log in
maybe you have to log in and sign some some some contract that you're not
allowed to do this or that sometimes they do not allow you to share
the results the outcomes which is not working together with
research because you have to make it reproducible
and you have to shadow results but if you just
are allowed to apply it then it's not uh scientific in some sense and
sometimes you just can are allowed to do it on their machines on their cloud
services not at your laptop so it's it's it's there is some
power asymmetry so like you make yourself a little bit dependent
of what the publishers of uh offer you that you allowed to do with the
information they have um and
it means also that you have a lot a lot a lot of legal work involved so you need
an army of lawyers and you need a lot of
time a lot of patience you have to write contract sometimes so this
is not really the way to go because as we always say
there is if you have to write to read you should have to write to mine and
that's really a very important central uh point of
everything we do because if you have legal access because
you've bought the publication or because your library has bought your
publication you should you are allowed to read it because you paid
for it you should also be allowed to copy it and do some text data mining
with it and so far there are only exceptions in
the uk and in japan as explicit tax data mining
exceptions in the copyright law uh in the u.s you have the fair use
which is a very i really like fair use as an open
knowledge person but the problem with tax data mining is
it wasn't meant for this uh it's so it's it's old it was before
tax data mining was used so there is no legal proof no one so far
uh was at court and uh run through the uh law system uh
to check out if this holds uh against uh yeah testing it out so there should be
some clarification in terms of tax data mining in the copyright laws in the u.s
maybe that's a that's a way to solve it and in the european union it's very bad
right now but it seems like maybe the actual
ongoing process of a copyright reform resolve it and it we are confident that
there will be a good tax data mining exception in the
future but let's see i wait until the end uh i
changed so much the last few years how to say what will happen next
so and there is also another way that's the way we go down
uh which means we only focus on open access literature
who of you knows what or have heard of open access
great amazing audience uh yeah so the idea is if you make
knowledge openly accessible for everyone as you are allowed to copy it
to download it to edit it and to re-share
the thing you can also do text data mining so this is also
works contributed by channels publishers whatever but it was
not exclusively owned by the publisher it was under the
terms that uh they publish it but it disallowed for
everyone to to do the stuff uh i've already
mentioned
and why this is important it's pretty easy and the first uh point
tells it uh very exact it's easy access for everyone it's every word is very
important it's easy because there are no legal barriers you
don't need armies of lawyers to help you with this you don't have to
wait for years or months and it's for everybody don't think a
little bit outside of your recent social environment think about
startups think about global south think about the
people who are affected by ebola and zika virus for example with
ebola one of the countries in central africa
had no access to a publication of 30 years old
uh still hid behind a paywall which was relevant for them because they
were not had no uh the library had no contract with
uh the publisher and stuff like that so it's really an important
thing and all the other points are just concrete
uh examples what this leads to uh and from no unnecessary legal
barriers you just know how to do it because it's a kind of
like standard you know when a publication is on a creative commons
license uh published you know that you're
allowed to do that so it's easy to work with you can
apply uh methods pretty fast and the final point practically it's the
only way because you as mentioned before you don't have the
time and when you have like 36 different
publishers involved in 200 channels and you have 16 000 publications
you will not write 36 different emails and
uh contracts to sign so practically most of the tax data mining
stuff is done with open access electrodes because it's the only
practical way to do it
so yeah and now we want to show you how we
tackle this problem because we already said
we use open access literature so we were we already tried to show the power of
text data mining and we are content mind we
are a not-for-profit company so we are not in the game to make a lot of
money our main goal is to support researchers
to do the job they want to do by extracting
information and giving it openly uh free for everyone so
it's not about it's ours so we want to extract it and
so that everyone in the world can use it and it was
founded by peter murray rust he's a professor in emeritus living in
cambridge uk and he had this idea that the right to
read is the right to mind and to use some copyright exception in the uk to
test out what you can do with text data mining to
support research and he then got supported by the
shuttlework foundation which is an amazing
uh fellowship program they have amazing fellowship program and he is now
shuttleworth fellow uh so that's where we got the kickoff
uh funding for the activities we have done for the last two and a half years
because shuttleworth is one of the guys who
supports ubuntu and it's an uh new economy
billionaire uh from south africa i think that's where he comes from but also
various activities are um and he supports all kind of uh
projects around open data open science of medication and
that's why he supported also peter and our team so now
tom will tell you a little bit about the software when we now go a little bit
more into the concrete heads on things
thank you very much um yeah so i'm tom i'm one of uh content mind software
developers um and as stefan was saying we're a
not-for-profit and we're a very open organization so all of our
software is open source it's mostly apache licensed
and it's available on github we're just github forward slash content mine
and we've built a few different tools to help researchers and other people who
are interested in text and data mining in getting started our tools don't cover
all the bases we certainly don't do everything and
we're very happy to recommend other tools to people that we didn't make if
they happen to do the job better and we're generally keen to see people
do text and data mining you know whether or not it's using
exactly what we wrote or whether or not they find their own way to do it
um so there are a couple of different tools we've written to address different
problems so for example uh the first two
on that list get papers in quick script these are tools to help you download
papers so at some point you've got to get hold
of the papers and i know some researchers maybe already have a library
of papers they want to use downloaded as pdfs they've
accrued over their phd or whatever but often
if you're maybe looking at zika for example you would want to be able to
download all of the the papers you you can get hold of
fairly quickly and so quick scrape and get papers are tools to do this
get papers is a tool that allows you to go to repositories and
the main one we we use is europe pubmed central you can see it's mentioned there
and that's a mirror of it's basically a mirror of pubmed which
is made by the ncbi it's a collection of biomedical papers
and it has a very good open access subset to all the things in pubmed
central and therefore we can download these we
can query for papers in bulk and say you know we want all the papers
on the zika virus and download them in a machine readable form they they
offer most of their open access papers in xml which makes it very easy to mine
rather than pdfs which as stefan was saying can be
tricky to mine and we can also use tools like quick
scrape to get hold of papers that are not open access or maybe they are
open access but they're not in a repository
so you can get metadata from places like crossraff
they're people who hand out doi's to get a list of urls or papers
and you probably know if you go and click on a diy link
you go to this like landing page on a publisher's site so you go to a nature
paper but instead of getting to the actual
paper you usually get to a page which says
click here for the paper and you know like us on facebook and
tweet about this article and would you like to cite it that kind of thing
and so quick scrape is a tool which you can pass a big list of urls
and we have uh basically templates for each uh different publisher for a
wide variety of publishers it's easy to make new ones from
your new publisher to uh to tell the machine where to look for the pdf or
look for the html version and also to extract from that page
metadata about it like who the author was
when it was published who their funders were that kind of thing
the other two tools are used for the actual
sort of processing of the documents once their own disc so
norma helps you um remove maybe the sort of crud still in the
papers that you've downloaded like again the facebook links or um
maybe there's lots of rubbish in the pdf that you want
to pull out and it lets you convert that pdf to a
you have downloaded a pdf into a form that's easy to read so you convert that
to what we call scholarly html which is a sort of it's
still in uh the committee stage of becoming a web
standard but it involves not having all the all
the other rubbish and then amy is uh well actually does
the text mining um so this can pick out what people in
the business call named entities so we have big lists of things like
all the human genes um all the diseases in the
i think it's a cdc dictionary of diseases
all of the species that are you know endangered that kind of thing
you can search for as well as uh searching for things that
can form certain patterns so you could search for all the identifiers that
match um stars something like that um and this this thing gives you this
the facts that we've taken out of the papers
and once you've got these facts you don't want to do something with them
because kind of the whole point was that you didn't want to
uh read all these pictures by hand so we we have a
python wrapper called pisces project which is a
um yeah it's a python library i don't know if any of you guys do any
programming but it it gives you a python interface to um
the files that you now have on disk including files containing the extracted
facts and you can then do pretty things with
them make network analysis graphs of them or just plot uh
publications against time stefan's got a really nice demo of that he's going to
show in a little bit um i just wanted to give
you a really quick flavor of downloading some papers um sorry
great yeah um i just want to give you a quick flavor of downloading some papers
using uh bit buggy that um using get papers
just press on the browser then the blue stuff should participate okay
yeah yeah i think just something to do with the projector here
um yeah so to download papers it's not a very pretty interface but it
you can get a lot quite quickly which is really nice so you literally type get
papers
and then you can put in a query so for example you might put in the word zika
if you're interested in zika virus but i don't know if i could maybe bring
a query from somebody in the audience is anyone doing
research in i don't know any kind of scientific field really
or are you all librarians or amino acids yeah so that's going to turn
back uh like loads of results so i can put that in
i'm probably well we can see how many results you get so um
i'll quote it right so this has a space in it uh so it's
a german keyboard and i'm not quite used to it
so amino acid so i reckon you're getting tons of results back for this so
i won't download all of the information about them now um
so as it says it's not going down listening so we're now searching on
europe pubmed central and you can you can build very complicated queries for
this so you could um search by then the
documentations available on their website so if you wanted to search by
all the papers that were published in you know 2000 2005 with author names
beginning with a b or an x and uh in the field of
biochemistry yeah so look tons of papers so way too many um for us to download
here so yeah 222 000 and that's just the open access results so there'll be even
more that are closed access um but if we search for maybe a more
specific amino acid um trying to think of something that's
quite rare but i don't know uh um glycine yeah glycine
glycine is really common i think but we find out right okay
okay
c-o-c-i
c-i reckon there'll be plenty they have found seven open access
results for a wrong sale maybe that's yeah that's something to
read for ah oh yeah so lots um still 50 56
000 so if i um i don't know did any of you happen to be
um uh you you were saying you management
performance management systems okay so euro permanent central as it sounds it's
slightly bent towards medical things it now has lots of open access um
stuff in general like i think it ingests the whole applause but yeah so
performance management might be a slightly tighter thing
click indicate or integration energy management system indicator
it will be two or two hundred and a few cool let's see what happens
i tried yesterday ah right awesome that's always good when somebody's done
a dry run for you before you do it live in front of an audience
thirteen thousand that's not bad um so it's gonna take a little while to
download um maybe i start it and we see how it
goes um in fact we can limit it so if we just
get the first i don't know let's say thousand papers
um so these will be ordered by what europe pubmed central thinks is
relevance towards this keyword so um you can this is kind of the same
results you would get if you browse their website and you searched um but
this gives you a way to just get all of the content in bulk
and you have to save it somewhere
you were saying that we may use a more complex
queries right yeah what's the pattern what's the pattern so the pattern is um
do you know lucine yeah so lucine is this uh search engine
um which is free and a lot of sites use it
and so the pattern is um basically the string we're putting in
there is like a combination of default fields of documents so it
usually includes the full text of an open access article maybe the abstract
of closed articles but it doesn't include for example the
author name so we wouldn't get uh papers published by somebody called
zika if we search for zika um the pattern is uh fairly logical so
you can do ands and alls and that kind of thing
um you can also search by specific field so you need to go to the europa
mentioned website to see each field but for
example a field like first so the first publication date is first
underscore p date and then a colon and then you can put in
a you know a date range of whatever date range you might like
does it have like uh near the nearer uh operator
that we have on some like scotland uh i'm not sure if it does so i think
if you put into this into the the query without a specific field then it does do
things like stemming so if you search for amino acids you'll
also get amino acid um but i don't think it uh
and there is some way to do um in lucine there is a way to do kind of similarity
uh searches but i don't know um quite how it works there's a very good
documentation for it so although obviously pubmed uh and pubmed central
exists one of the reasons we don't use it is
because we found that the uh the api that's provided by this
organization is slightly better um it's quite good for us to work with
um and we you know as you can see we've just got a thousand files which is quite
nice um so we can just give you a really
quick glimpse of what this looks like i appreciate it doesn't look very pretty
but um at the end of the day we thought
about trying to make sort of a really nice off-the-shelf bit of software
and it's something we'd love to do but we found that actually every researcher
who wants to work in this area has a very specific thing they end up looking
for they they don't really want to do something
generic so it's more useful at the moment for us to give people tools they
can combine combine articles combine other people's
tools um because you don't know quite what people
are going to do some people have their own library of papers some people
you know want to somehow dig out all the papers that are
contain the widest number of different amino acids for example
and that's not really something that we could provide that much flexibility in
so we have to rely on people to have a bit
of a technical background um yeah so if you have a little look at
what you've got here uh what do we call it
yeah so this is just a folder structure so just like normal directories you
might have in your my documents folder and so for each of these papers there's
a single folder with the id of the paper and you've got the method about it in in
this json file i don't know if you know it doesn't know what jason look like but
it's quite easy to read for humans so uh
so i'm still playing on the german keyboard
so yeah so you can see there's an id it comes from pubmed you've got the full
title and so if you were interested in doing a
little bit of um programming or you could browse this um
using some other tools you can you know sort by all the authors or some
of the authors have orchids that kind of thing you can see where
they this guy was affiliated with um the university of uh medicine in japan
cato university and you've also got the full text um
which is like the whole document which is really nice to have um
so once you've downloaded this you can then do some mining you can extract
things like species from it um and we're coming quite close to time
so i'm going to get stefan to quickly run you through
uh a nice little visual
thank analysis um yeah and it's a little bit hard to
so he's going to bring up a document um which is sort of an interactive notebook
um and you can script this notebook in various languages so
in this case it's written in python and this is being you know increasingly used
by researchers because it's very nice you can
um you can store the document online it's very reproducible so when somebody
else works their way through the documents and reruns all the code you
typed in they can see they get the same results they can change it a bit they
can put in their own data and say you know this is the age i collected does it
look any different this is the data on my my
topic not the other person's topic and so this is very good for open
science in general because you have kind of like your lab notebook but
published and it also includes doing the bench work itself which is the
code and the analysis of it yeah so documentation
and application is all in the same file that's really
a nice thing to have and there it is i just want to before i just
want to show you also about uh
the final outcome of the whole content mind tool chain because
after you applied norma and amy you've seen now we have the full text
but i have to apply the text data mining uh parts of the pipeline too
you all get the even bigger uh yeah that's one article and then you
have more files in there and then exactly you have
to follow with the results where the entities the extracted ones are in and
you have like the normal genos uh results where you then have the
facts in them so that's how it looks like and
we've written a python wrapper which accesses exactly
this kind of folder structure and files uh we create with the software and
that's what i use as an input for this jupyter notebook where i do
some network analysis and descriptive statistics
inside with python so you can maybe write an own wrapper if
you want to use it with java it's not that difficult and we started with
python because that's the software uh me and my colleague uses and this
should show you as a short i'll give you a short
yeah a glimpse of what's about to apply tax data mining with
uh the zika virus that's that's the data set we've used for this and the
first one is we just printed out from the metadata when
were publications uh uh published uh in terms of seeker the
first top one is about seeker the second one is about the term aedes egypt a
transmitting mosquito and the third one is the sutures which
is meant as maybe being the next global
pandemic after seeker so this is interesting to compare maybe
you find some patterns and you see zika is very recent it's
just after the big uh uh yeah
after the virus breakout but it was not researched before
in other cases it's different because the idiocy
transmits also other uh viruses so you get a first uh idea of the
development of research that seems a little bit basic but that's
important at the beginning to have an understanding
but then you dive in deeper and here you look on the most
uh frequent authors inside the 1200 publications that's the corpus we
use for this and it's downloaded in 20 seconds and
the whole analysis preparation part took 20 minutes
and now i can use this information to some uh
statistics with it and there is some oh faith person
25 times mentioned in this whole corpus so
maybe it's the right person to contact uh when you are
wh o or united nations uh department who wants to find out more about
how to solve the problem and then you also want to do the same with the
journals you want to find out the most relevant
channels who publish things about papers about
zika so we do the same with the frequency
here and that's pretty basic uh that's not uh
that's metadata stuff we used for there but here
we now use the extracted entities so here we now uh
uh give some value from the text data mining stuff we did and as you see
on the top is the idios i think it's the family
of the mosquito uh 290 time and the flower beers and flowers die
uh that's the virus family from zika so you if you have the main knowledge
you can say okay i knew this i knew this that's right or
maybe there's a new term i've never heard of it
uh let's look uh if there's maybe some uh
uh yeah it has some relation with the stuff i need or i'm looking out for but
as you see you need domain knowledge i did not understand anything right i've
computed this the first time as a sociologist
but there is for example will bache a bacteria
55 times mentioned uh who when it i think
when it infects the mosquito uh the transmission uh chance is lower so it's
hinders the transmission i have a question there's
some of those are single terms but then like uh 73 masculine bio
where is it picking up that whole uh that's the title of the paper i think
okay that's like the full title of a um of the publication yeah so the
one thing about this algorithms is always you have to
make a balance between precision and recall so you always have false
positives and that that's what what's happening
and the data structure it depends how good this is
it comes in uh but you always have to be uh how is uh yeah
uh flexible or like uh yeah don't take it exactly you always
have to think about it but it helps you to explore the things could it be
putting out something from the reference list
like if a paper is constantly referenced in
many of those publications and would it be pulling those
instead of like a name or a keyword yeah that that could be the case i think
in this case stefan's going to come on onto his um the analysis he's doing is
actually trying to look at not just individual words that appear but which
papers those words appear and how they connect and i think possibly we're just
getting a bit confused about that i think it also seems like a strange one
just because it's dealing with the arctic and there's not many
mosquitoes yeah yeah so that's uh typical or wrong
data in there but you have you have to filter this out
uh because as you said it's easy to find out arctic
maybe mosquitoes and ck is not relevant and it's it's always about the big
numbers you will find errors especially down the line uh uh
quite a lot but when you look on like 290 times that's a pretty
significant number so
that's that's how it works when you use explorative techniques uh
do you have to be
yeah uh it's not too sensitive that it's exact the way uh you want to get it out
and then at the end uh i just picked out one uh
one term so this is a network analysis where i was looking on
which species co-occurred which we've with with which other species in a
publication so which species are often mentioned
together so this maybe helps you if there is another transmitting species
to the aether's egyptian chipped so this is like to find out similar uh
species in the name in the same publication which may
be tempts you to it's used in the same study
so maybe it's just a same transmitting animal in another virus or
something like this but it's also a way how you can find similarities
and to explore the whole information in there because it's massive it's 1200
papers and it's as we said seen before we could
use this with 50 000 papers too uh that's even a small one because uh
it's at least a little bit uh easier to understand at the beginning
and then i was especially looking on wolbachia or the bacteria
i just printed out all the neighbors which means all the co-occurring
other species in this network so this is our all species
which co-occur with volbachia just just as an example i don't know if
this could lead somewhere that has to be done by some uh
pandemic researchers but you can do the same with
uh uh organizations with other entities as you wish
and then i also looked at all the publications where the volbachia was
mentioned in and here is just a list of the 55
uh publications in there so if you want to read
and dive more into volbachia and the whole field
you have a lot of opportunities how to find related information from
entities to uh publications to researchers doing their thing and also
similar uh entities yeah this is
and then at the end it's just a fancy plot of all the entities
in there but this should just give an and short glimpse about
what could be done if you're in the domain and if you want to apply it you
need to refine it you may you need to have a lot
of domain knowledge especially because you have also asked the same the
right questions you have to look out for the right terms
for the right entities uh which is not that trivial uh
for me as a sociologist but it's just the first tutorial
which should show the strength of uh text data mining for pandemics
yeah uh that's it so far from our side yeah we're very close to time
but we would love to take some questions if you haven't have any questions
just i mean obviously you're good at programming and
putting out stuff but for the average user how easy would it be to kind of
like use it effectively yeah so that's a very
good question um at the moment it's not so easy it's
one thing we really want to work on and we would love to be able to produce
like a simplified form of the software that
individual users could get a real benefit from
but as i was mentioning earlier it's very difficult to find
um what that common set of uh like problems that a user would want to
solve across all the many disciplines we talk
to so what we've found at the moment is we
we've had most success with people who have at least a small
technical background and they are maybe doing and this is
very common in you know statistical fields people who are doing
um systematic reviews people who maybe already have a year's
experience of programming in r to do their statistical analysis
then it's not so bad to add this step to the beginning of it
but we don't have like a single point and click thing yet and something we
would like to do and it would be really great for demos
and stuff um you know pile in a load of papers
highlight all their species and and do it all beautifully on the screen
but at the end of the day that's not so useful because we kind of
the output still has to be machine readable
and for it to be machine readable um it has to be um there's often
quite a complex structure and that structure you know then
has to be tweaked and altered depending on what the user is trying to get out of
it and stefan i don't think quite showed
this but there are there were two graphs there and one was for example the
co-occurrence of species and papers and one was the co-occurrence of papers with
species and so those are two things that you
could do um but one of those is very useful and one
of those actually turns out to not be useful at all you're not really very
interested in which papers co-occurring species but you are
interested with species cocaine papers and that's the kind of thing that really
can't be solved by a computer or by a computer scientist it's
something that you need somebody who knows what they're doing so you have to
um you have to have a bit of an input from the domain
specialist and stuff there's a couple more slides and that kind of goes into
one of the main things we do as an organization is we we run as many
workshops as we can and try and get early career researchers and people who
have a bit of technology experience um able to do this themselves and
give them the tools rather than give them a single one-up packet to be do
everything because we're not able to do that at the moment
and the thing is also we have a lot of online resources
where you see all these uh examples how you write one get papers
query and you can dive in a lot more into it
so the other materials i think are pretty good uh
thing to start even if you're not a programmer because it looks
maybe a little bit tacky and complicated at the beginning when you've never seen
a shell but once you've opened the shell and
just execute the line we uh show you the tutorials it's just
uh changing the query term you just change from sql
to performance review and that's it change the output folder
so it's i think it's not that difficult to understand but there is some barrier
when you've never done it at the beginning so the tutorials i think
are maybe a good starting point to experiment on your
pc and find out how it works
some more questions
to this the this page i can edit it with more graphics and so
on um just like uh with uh
python commons yeah totally i mean that's that's the whole idea about it
we will continuously improve the functions of the python wrapper
and also the the presentational stuff like this
but as as tom said before our basic goal is not to be the expert
in pandemics because no one is from our team is
pandemics researcher we want to offer pandemic researchers
the opportunity to do their work better and easier we want to educate them to do
it uh the open way uh because we cannot it's
it's not a reasonable plan that we fulfill all domain knowledge or relevant
to tax data mining with thousands that's a part where the
community comes in and maybe some people of you
come in uh if you're interested in doing this uh
just just apply it share it show it to others
we're happy to help you in all problems with with the
with the online resources i think that's a good way to start
uh but it must be done by researchers uh it's not a technical problem at some
point it is at the first point but at some point you need to read a lot of
books to understand the stuff you're doing
nice okay yeah yep so in the actual file there's still uh
like download the interactive content in the fake in the article such as like
the figures or tables so typically the graphical
content it's not that okay but the tabular
content is so tables are represented in it but the graphical
content isn't you can also there's another way to add
another flag to the end of that and you can download a zip file with all of the
figures um as graphics but they're not like in the xml file they come
separately as supplementary information in the zip file
yeah because the purpose is the text one yeah
so like what if i'm just like i'm analyzing the results
okay i'm interested in one of these papers so would it be possible like to
somehow like add a tool like a way to get the pdf or
yeah so you can get the pdf as well um yeah you can have the pdf as well as the
xml um yeah again it depends a little bit on
exactly what your end goal is so if your end goal is to at the end of the
day still provide a user with a view of the paper
after it's been mined then that's a very different goal to
um if you want to be able to just get an aggregate of all of them like the graph
where you at the end of it you're not expecting to show the user part of the
paper but yeah you can download the pdfs with
get papers as well um and then you can browse them you can
also do things like um we don't have time to shirt but
yeah you can annotate papers so for example um we did a
bit of a collaboration with a team called hypothesis who are
a web annotation um startup and we can mine through papers find all
the species we could then look up those species and do things like tell you are
they endangered or not and then we can highlight on the paper
when you go back and view it uh this species is endangered this species
is not endangered which is nice you know and that gives you you'll be able to
choose if you were a researcher maybe what matters to you maybe you don't care
about endangeredness because you know it but you would like to see
all of the cities which have a population over 10 000 highlighted
because you're worried those might be pandemic areas
something like that okay yeah uh then maybe just to finish
uh here is a short url where you can find a documentation of
the stay also all events we have we will put
the slides on after the after tomorrow
and you will also find information about the panel discussion about the workshop
tomorrow if you want to come just register there there are still some
places open and that's the contact information so
we're happy if you write us uh and if you're interested in the stuff
we are doing and we are happy for collaborations uh
and to work together on the further pro to make further progress
in this area because i think there is a lot of opportunities in there it cannot
be done by one organization by a few people i and i
think it's not a plan that we do everything
but as more people are involved is better the quality
go to contentminer.org and yeah i'm very thankful
and yeah can you please go back to this slide yeah
yeah it's content nine hyphen virginia tech open day to week
oh okay that's that's the mnemonic if you can remember
yeah yeah so thank you very much for your coming and hope to see you
thank you thanks guys
you