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I work as a teacher
at the University of Alicante,
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where I recently obtained my PhD
on data libraries and linked open data.
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And I'm also a software developer
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at the Biblioteca Virtual
Miguel de Cervantes.
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And today, I'm going to talk
about data quality.
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Well, those are my colleagues
at the university.
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And as you may know, many organizations
are publishing their data
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or linked open data--
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for example,
the National Library of France,
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the National Library of Spain,
us, which is Cervantes Virtual,
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the British National Bibliography,
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the Library of Congress and Europeana.
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All of them provide a SPARQL endpoint,
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which is useful in order
to retrieve the data.
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And if I'm not wrong,
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the Library of Congress only provide
the data as a dump that you can't use.
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When we publish our repository
as linked open data,
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my idea was to be reused
by other institutions.
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But what about if I'm an institution
who wants to enrich their data
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with any data from other data libraries.
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Which data set should I use?
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Which data set is better
in terms of quality?
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The benefits of the evaluation
of data quality in libraries are many.
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For example, methodologies can be improved
in order to include new criteria,
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in order to assess the quality.
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And also, organizations can benefit
from best practices and guidelines
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in order to publish their data
as linked open data.
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What do we need
in order to assess the quality?
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Well, obviously, a set of candidates
and a set of features.
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For example, do they have
a SPARQL endpoint,
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do they have a web interface,
how many publications do they have,
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how many vocabularies do they use,
how many Wikidata properties do they have,
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and where can I get those candidates?
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I use LOD Cloud--
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but when I was doing this slide,
I thought about using Wikidata
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in order to retrieve those candidates.
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For example, getting entities
of type data library,
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which has a SPARQL endpoint.
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You have here the link.
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And I come up with those data libraries.
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The first one uses bibliographic ontology
as main vocabulary,
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and the others are based,
more or less, on FRBR,
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which is a vocabulary published by IFLA.
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And this is just an example
of how we could compare
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data libraries using
bubble charts on Wikidata.
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And this is just an example comparing
how many Wikidata properties
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are per data library.
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Well, how can we measure quality?
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There are different methodologies,
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for example, FRBR 1,
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which provides a set of criteria
grouped by dimensions,
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and those in green
are the ones that I found--
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that I could assess by means of Wikidata.
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And we also find that we
could define new criteria,
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for example, a new one to evaluate
the number of duplications in Wikidata.
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We use those properties.
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And this is an example of SPARQL,
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in order to count the number
of duplicates property.
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And about the results,
while at the moment of doing this study,
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not the slides, there was no property
for the British National Bibliography.
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They don't provide provenance information,
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which could be useful
for metadata enrichment.
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And they don't allow
to edit the information.
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So, we've been talking
about Wikibase the whole weekend,
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and maybe we should try to adopt
Wikibase as an interface.
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And they are focused on their own content,
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and this is just the SPARQL query
based on Wikidata
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in order to assess the population.
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And the BnF provides labels
in multiple languages,
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and they all use self-describing URIs,
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which is that in the URI,
they have the type of entity,
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which allows the human reader
to understand what they are using.
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And more results, they provide
different output format,
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they use external vocabularies.
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Only the British National Bibliography
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provides machine-readable
licensing information.
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And up to one-third of the instances
are connected to external repositories,
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which is really nice.
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And while this study, this work
has been done in our Labs team,
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a lab in a GLAM is a group of people
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who want to explore new ways
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of reusing data collections.
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And there's a community
led by the British Library,
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and in particular, Mahendra Mahey,
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and we had a first event in London,
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and another one in Copenhagen,
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and we're going to have a new one in May
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at the Library of Congress in Washington.
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And we are now 250 people.
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And I'm so glad that I found
somebody here at the WikidataCon
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who has just joined us--
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Sylvia from [inaudible], Mexico.
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And I'd like to invite you
to our community,
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since you may be part
of a GLAM institution.
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So, we can talk later
if you want to know about this.
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And this--it's all about people.
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This is me, people
from the British Library,
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Library of Congress, Universities,
and National Libraries in Europe
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And there's a link here
in case you want to know more.
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And, well, last month,
we decided to meet in Doha
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in order to write a book
about how to create a lab in our GLAM.
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And they choose 15 people,
and I was so lucky to be there.
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And the book follows
the Booksprint methodology,
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which means that nothing
is prepared beforehand.
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All is done there in a week.
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And believe me, it was really hard work
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to have their whole book
done in this week.
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And I'd like to introduce you to the book,
which will be published--
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it was supposed to be published this week,
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but it will be next week.
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And it will be published open,
so you can have it,
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and I can show you
a little bit later if you want.
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And those are the authors.
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I'm here-- I'm so happy, too.
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And those are the institutions--
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Library of Congress, British Library--
and this is the title.
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And now, I'd like to show you--
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a map that I'm doing.
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We are launching a website
for our community,
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and I'm in charge of creating a map
with our institutions there.
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This is not finished.
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But this is just SPARQL, and below,
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we see the map.
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And we see here
the new people that I found, here,
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at the WikidataCon--
I'm so happy for this.
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And we have here my data library
of my university,
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and many other institutions.
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Also, from Australia--
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if I can do it.
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Well, here, we have some links.
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There you go.
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Okay, this is not finished.
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We are still working on this,
and that's all.
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Thank you very much for your attention.
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(applause)
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[inaudible]
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Good morning, everybody.
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I'm Olaf Janssen.
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I'm the Wikimedia coordinator
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at the National Library
of the Netherlands.
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And I would like to share my work,
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which I'm doing about creating
Linked Open Data
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for Dutch Public Libraries using Wikidata.
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And my story starts roughly a year ago
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when I was at the GLAM Wiki conference
in Tel Aviv, in Israel.
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And there are two men
with very similar shirts,
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and equally similar hairdos, [Matt]...
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(laughter)
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And on the left, that's me.
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And a year ago, I didn't have
any practical knowledge and skills
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about Wikidata.
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I looked at Wikidata,
and I looked at the items,
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and I played with it.
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But I wasn't able to make a SPARQL query
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or to do data modeling
with the right shape expression.
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That's a year ago.
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And on the lefthand side,
that's Simon Cobb, user: Sic19.
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And I was talking to him,
because, just before,
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he had given a presentation
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about improving the coverage
of public libraries in Wikidata.
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And I was very inspired by his talk.
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And basically, he was talking
about adding basic data
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about public libraries.
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So, the name of the library, if available,
the photo of the building,
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the address data of the library,
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the geo-coordinates
latitude and longitude,
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and some other things,
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including with all source references.
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And what I was very impressed
about a year ago was this map.
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This is a map about
public libraries in the U.K.
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with all the colors.
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And you can see that all the libraries
are layered by library organizations.
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And when he showed this,
I was really, "Wow, that's cool."
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So, then, one minute later, I thought,
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"Well, let's do it
for the country for that one."
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(laughter)
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And something about public libraries
in the Netherlands--
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there are about 1,300 library
branches in our country,
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grouped into 160 library organizations.
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And you might wonder why
do I want to do this project?
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Well, first of all, because
for the common good, for society,
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because I think using Wikidata,
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and from there,
creating Wikipedia articles,
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and opening it up
via the linked open data cloud--
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it's improving visibility and reusability
of public libraries in the Netherlands.
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And my second goal was actually
a more personal one,
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because a year ago, I had this
yearly evaluation with my manager,
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and we decided it was a good idea
that I got more practical skills
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on linked open data, data modeling,
and also on Wikidata.
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And of course, I wanted to be able to make
these kinds of maps myself.
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(laughter)
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Then you might wonder
why do I want to do this?
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Isn't there already enough basic
library data out there in the Netherlands
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to have a good coverage?
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So, let me show you some of the websites
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that are available to discover
address and location information
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about Dutch public libraries.
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And the first one is this one--
Gidsvoornederland.nl--
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and that's the official
public library inventory
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maintained by my library,
the National Library.
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And you can look up addresses
and geo-coordinates on that website.
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Then there is this site,
Bibliotheekinzicht--
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this is also an official website
maintained by my National Library.
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And this is about
public library statistics.
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Then there is another one,
debibliotheken.nl--
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as you can see there is also
address information
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about library organizations,
not about individual branches.
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And there's even this one,
which also has address information.
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And of course, there's something
like Google Maps,
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which also has all the names
and the locations and the addresses.
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And this one, the International
Library of Technology,
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which has a worldwide
inventory of libraries,
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including the Netherlands.
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And I even discovered there is a data set
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you can buy for 50 euros or so
to download it.
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And there is also--seems to be
I didn't download it,
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but there seems to be address
information available.
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You might wonder is this kind of data
good enough for the purposes I had?
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So, this is my birthday list
for my ideal public library data list.
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And what's on my list?
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First of all, the data I want to have
must be up-to-date-ish--
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it must be fairly up-to-date.
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So, doesn't have to be real time,
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but let's say, a couple
of months, or half a year,
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delayed with official publication,
that's okay for my purposes.
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And I want to have it both
library branches
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and the library organizations.
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Then I want my data to be structured,
because it has to be machine-readable.
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It has to be in open file format,
such as CSV or JSON or RDF.
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It has to be linked
to other resources preferably.
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And the uses--the license on the data
needs to be manifest public domain or CC0.
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Then, I would like my data to have an API,
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which must be public, free,
and preferably also anonymous
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so you don't have to use an API key,
or you have to register an account.
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And I also want to have
a SPARQL interface.
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So, now, these are all the sites
I just showed you.
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And I'm going to make a big grid.
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And then, this is about
the evaluation I did.
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I'm not going into it,
but there is no single column
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which has all green check marks.
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That's the important thing to take away.
242
00:14:58,967 --> 00:15:03,947
And so, in summary, there was no
linked public free linked open data
243
00:15:03,947 --> 00:15:08,937
for Dutch public libraries available
before I started my project.
244
00:15:09,237 --> 00:15:13,027
So, this was the ideal motivation
to actually work on it.
245
00:15:14,730 --> 00:15:17,427
So, that's what I've been doing
for a year now.
246
00:15:17,717 --> 00:15:22,977
And I've been adding libraries bit by bit,
organization by organization to Wikidata.
247
00:15:23,417 --> 00:15:26,387
I created also a project website on it.
248
00:15:26,727 --> 00:15:29,567
It's still rather messy,
but it has all the information,
249
00:15:29,567 --> 00:15:33,240
and I try to keep it
as up-to-date as possible.
250
00:15:33,240 --> 00:15:36,277
And also all the SPARQL queries
you can see are linked from here.
251
00:15:38,002 --> 00:15:40,235
And I'm just adding
really basic information.
252
00:15:40,235 --> 00:15:44,097
You see the instances,
images if available,
253
00:15:44,097 --> 00:15:47,229
addresses, locations, et cetera,
municipalities.
254
00:15:48,534 --> 00:15:53,276
And where possible, I also try to link
the libraries to external identifiers.
255
00:15:56,024 --> 00:15:58,415
And then, you can really easily--
we all know,
256
00:15:58,415 --> 00:16:03,050
generating some Listeria lists
with public libraries grouped
257
00:16:03,050 --> 00:16:05,060
by organizations, for instance.
258
00:16:05,060 --> 00:16:08,380
Or using SPARQL queries,
you can also do aggregation on data--
259
00:16:08,380 --> 00:16:11,060
let's say, give me all
the municipalities in the Netherlands
260
00:16:11,060 --> 00:16:15,115
and the number of library branches
in all the municipalities.
261
00:16:17,025 --> 00:16:20,228
With one click, you can make
these kinds of photo galleries.
262
00:16:22,092 --> 00:16:23,655
And what I set out to do first,
263
00:16:23,655 --> 00:16:26,036
you can really create these kinds of maps.
264
00:16:27,176 --> 00:16:30,425
And you might wonder,
"Are there any libraries here or there?"
265
00:16:30,555 --> 00:16:33,355
There are--they are not yet in Wikidata.
266
00:16:33,355 --> 00:16:35,055
We're still working on that.
267
00:16:35,135 --> 00:16:37,644
And actually, last week,
I spoke with a volunteer,
268
00:16:37,644 --> 00:16:40,864
who's helping now
with entering the libraries.
269
00:16:41,644 --> 00:16:45,394
You can really make cool--in Wikidata,
270
00:16:45,394 --> 00:16:47,914
and also with using
the Cartographer extension,
271
00:16:47,914 --> 00:16:50,244
you can use these kinds of maps.
272
00:16:51,724 --> 00:16:53,736
And I even took it one step further.
273
00:16:53,911 --> 00:16:57,399
I also have some Python skills,
and some Leaflet things skills--
274
00:16:57,399 --> 00:16:59,971
so, I created, and I'm quite
proud of it, actually.
275
00:16:59,971 --> 00:17:03,482
I created this library heat map,
which is fully interactive.
276
00:17:03,482 --> 00:17:05,956
You can zoom in to it,
and you can see all the libraries,
277
00:17:06,712 --> 00:17:08,726
and you can also run it off Wiki.
278
00:17:08,726 --> 00:17:10,552
So, you can just embed it
in your own website,
279
00:17:10,552 --> 00:17:13,412
and it fully runs interactively.
280
00:17:15,131 --> 00:17:17,592
So, now going back to my big scary table.
281
00:17:19,512 --> 00:17:22,970
There is one column
on the right, which is blank.
282
00:17:22,970 --> 00:17:24,940
And no surprise, it will be Wikidata.
283
00:17:24,940 --> 00:17:26,448
Let's see how it scores there.
284
00:17:26,448 --> 00:17:29,500
(cheering)
285
00:17:32,892 --> 00:17:35,191
So, I actually think
of printing this on a T-shirt.
286
00:17:35,301 --> 00:17:37,288
(laughter)
287
00:17:37,788 --> 00:17:39,700
So, just to summarize this in words,
288
00:17:39,700 --> 00:17:41,129
thanks to my project, now,
289
00:17:41,129 --> 00:17:45,879
there is public free linked open data
available for Dutch public libraries.
290
00:17:47,124 --> 00:17:49,686
And who can benefit from my effort?
291
00:17:50,333 --> 00:17:52,002
Well, all kinds of parties--
292
00:17:52,002 --> 00:17:54,274
you see Wikipedia,
because you can generate lists
293
00:17:54,274 --> 00:17:56,051
and overviews and articles,
294
00:17:56,051 --> 00:17:59,908
for instance, using this
and be able to from Wikidata
295
00:17:59,908 --> 00:18:01,976
for our National Library for--
296
00:18:02,850 --> 00:18:05,391
IFLA also has an inventory
of worldwide libraries,
297
00:18:05,391 --> 00:18:07,216
they can also reuse the data.
298
00:18:07,650 --> 00:18:09,497
And especially for Sandra,
299
00:18:09,549 --> 00:18:13,237
it's also important for the Ministry--
Dutch Ministry of Culture--
300
00:18:13,277 --> 00:18:15,667
because Sandra is going
to have a talk about Wikidata
301
00:18:15,667 --> 00:18:18,287
with the Ministry this Monday,
next Monday.
302
00:18:19,922 --> 00:18:22,277
And also, on the righthand side,
for instance,
303
00:18:23,891 --> 00:18:27,098
Amazon with Alexa, the assistant,
304
00:18:27,098 --> 00:18:28,961
they're also using Wikidata,
305
00:18:28,961 --> 00:18:30,995
so you can imagine that they also use,
306
00:18:30,995 --> 00:18:33,357
if you're looking for public
library information,
307
00:18:33,357 --> 00:18:36,580
they can also use Wikidata for that.
308
00:18:38,955 --> 00:18:41,680
Because one year ago,
Simon Cobb inspired me
309
00:18:41,680 --> 00:18:44,244
to do this project,
I would like to call upon you,
310
00:18:44,244 --> 00:18:45,664
if you have time available,
311
00:18:45,664 --> 00:18:49,532
and if you have data from your own country
about public libraries,
312
00:18:51,572 --> 00:18:54,422
make the coverage better,
add more red dots,
313
00:18:54,982 --> 00:18:56,982
and of course, I'm willing
to help you with that.
314
00:18:56,982 --> 00:18:59,227
And Simon is also willing
to help with this.
315
00:18:59,870 --> 00:19:01,471
And so, I hope next year, somebody else
316
00:19:01,471 --> 00:19:03,901
will be at this conference
or another conference
317
00:19:03,901 --> 00:19:06,291
and there will be more
red dots on the map.
318
00:19:07,551 --> 00:19:08,911
Thank you very much.
319
00:19:09,004 --> 00:19:12,740
(applause)
320
00:19:18,336 --> 00:19:20,086
Thank you, Olaf.
321
00:19:20,086 --> 00:19:23,554
Next we have Ursula Oberst
and Heleen Smits
322
00:19:23,613 --> 00:19:27,734
presenting how can a small
research library benefit from Wikidata:
323
00:19:27,734 --> 00:19:31,423
enhancing library products using Wikidata.
324
00:19:53,717 --> 00:19:57,637
Okay. Good morning.
My name is Heleen Smits.
325
00:19:58,680 --> 00:20:01,753
And my colleague,
Ursula Oberst--where are you?
326
00:20:01,753 --> 00:20:03,873
(laughter)
327
00:20:04,371 --> 00:20:09,220
And I work at the Library
of the African Studies Center
328
00:20:09,220 --> 00:20:11,086
in Leiden, in the Netherlands.
329
00:20:11,086 --> 00:20:15,038
And the African Studies Center
is a center devoted--
330
00:20:15,038 --> 00:20:21,464
is an academic institution
devoted entirely to the study of Africa,
331
00:20:21,464 --> 00:20:23,986
focusing on Humanities and Social Studies.
332
00:20:24,672 --> 00:20:28,123
We used to be an independent
research organization,
333
00:20:28,123 --> 00:20:33,064
but in 2016, we became part
of Leiden University,
334
00:20:33,064 --> 00:20:38,433
and our catalog was integrated
into the larger university catalog.
335
00:20:39,283 --> 00:20:43,593
Though it remained possible
to do a search in the part of the Leiden--
336
00:20:43,593 --> 00:20:45,894
of the African Studies Catalog, alone,
337
00:20:47,960 --> 00:20:50,505
we remained independent in some respects.
338
00:20:50,586 --> 00:20:53,262
For example, with respect
to our thesaurus.
339
00:20:54,921 --> 00:20:59,883
And also with respect
to the products we make for our users,
340
00:21:01,180 --> 00:21:04,378
such as acquisition lists
and work dossiers.
341
00:21:05,158 --> 00:21:11,975
And it is in the field of the web dossiers
342
00:21:11,975 --> 00:21:14,582
that we have been looking
343
00:21:14,582 --> 00:21:19,582
for possible ways to apply Wikidata,
344
00:21:19,582 --> 00:21:23,372
and that's the part where Ursula
will in the second part of this talk
345
00:21:24,212 --> 00:21:27,184
show you a bit
what we've been doing there.
346
00:21:31,250 --> 00:21:35,160
The web dossiers are our collections
347
00:21:35,160 --> 00:21:39,000
of titles from our catalog
that we compile
348
00:21:39,000 --> 00:21:45,591
around a theme usually connected
to, for example, a conference,
349
00:21:45,591 --> 00:21:51,227
or to a special event, and actually,
the most recent web dossier we made
350
00:21:51,227 --> 00:21:56,017
was connected to the year
of indigenous languages,
351
00:21:56,017 --> 00:21:59,547
and that was around proverbs
in African languages.
352
00:22:00,780 --> 00:22:02,327
Our first steps--
353
00:22:04,307 --> 00:22:09,287
next slide--our first steps
on the Wiki path as a library,
354
00:22:10,267 --> 00:22:15,046
were in 2013, when we were one
of 12 GLAM institutions
355
00:22:15,046 --> 00:22:16,472
in the Netherlands,
356
00:22:16,472 --> 00:22:20,952
part of the project
of Wikipedians in Residence,
357
00:22:20,952 --> 00:22:26,443
and we had for two months,
a Wikipedian in the house,
358
00:22:27,035 --> 00:22:32,527
and he gave us trainings
for adding articles to Wikipedia,
359
00:22:33,000 --> 00:22:37,720
and also, we made a start with uploading
photo collections to Commons,
360
00:22:38,530 --> 00:22:42,650
which always remained a little bit
dependent on funding, as well,
361
00:22:43,229 --> 00:22:45,702
whether we would be able to digitize them,
362
00:22:45,702 --> 00:22:50,350
and to mostly have
a student assistant to do this.
363
00:22:51,220 --> 00:22:55,440
But it was actually a great adding
to what we could offer
364
00:22:55,440 --> 00:22:57,560
as an academic library.
365
00:22:59,370 --> 00:23:04,742
In May 2018, so is that my Ursula,
my colleague Ursula--
366
00:23:04,742 --> 00:23:09,465
she started to really explore--
dive into Wikidata
367
00:23:09,465 --> 00:23:14,515
and see what we as a small
and not very much experienced library
368
00:23:14,515 --> 00:23:18,175
in these fields could do with that.
369
00:23:25,050 --> 00:23:26,995
So, I mentioned, we have
our own thesaurus.
370
00:23:28,210 --> 00:23:30,689
And this is where we started.
371
00:23:30,689 --> 00:23:34,502
This is a thesaurus of 13,000 terms,
372
00:23:34,502 --> 00:23:37,670
all in the field of African studies.
373
00:23:37,670 --> 00:23:41,457
It contains a lot of African languages,
374
00:23:43,417 --> 00:23:46,360
names of ethnic groups in Africa,
375
00:23:47,586 --> 00:23:49,431
and other proper names,
376
00:23:49,431 --> 00:23:55,509
which are perhaps especially
interesting for Wikidata.
377
00:23:58,604 --> 00:24:04,824
So, it is a real authority control
378
00:24:04,824 --> 00:24:08,370
to vocabulary
with 5,000 preferred terms.
379
00:24:08,554 --> 00:24:11,204
So, we submitted the request to Wikidata,
380
00:24:11,204 --> 00:24:17,135
and that was actually very quickly
met with a positive response,
381
00:24:17,214 --> 00:24:19,354
which was very encouraging for us.
382
00:24:22,884 --> 00:24:25,574
Our thesaurus was loaded into Mix-n-Match,
383
00:24:25,574 --> 00:24:31,691
and by now, 75% of the terms
384
00:24:31,691 --> 00:24:36,145
have been manually matched with Wikidata.
385
00:24:38,061 --> 00:24:42,081
So, it means, well, that we are now--
386
00:24:42,971 --> 00:24:47,687
we are added as an identifier--
387
00:24:48,387 --> 00:24:51,553
for example, if you click
on Swahili language,
388
00:24:52,463 --> 00:24:57,152
what happens then in Wikidata
on the number that--
389
00:24:59,004 --> 00:25:02,354
that connects our term--
is the Wikidata term--
390
00:25:02,560 --> 00:25:05,620
we enter into our thesaurus,
391
00:25:05,620 --> 00:25:10,000
and from there, you can do a search
directly in the catalog
392
00:25:10,000 --> 00:25:12,560
by clicking the button again.
393
00:25:12,560 --> 00:25:18,160
It means, also, that Wikidata
has not really integrated
394
00:25:18,160 --> 00:25:19,572
into our catalog.
395
00:25:19,572 --> 00:25:22,090
But that's also more difficult.
396
00:25:22,314 --> 00:25:26,053
Okay, we have to give the floor
397
00:25:26,053 --> 00:25:30,838
to Ursula for the next part.
398
00:25:30,838 --> 00:25:32,554
(Ursula) Thank you very much, Heleen.
399
00:25:32,554 --> 00:25:37,258
So, I will talk about our experiences
400
00:25:37,258 --> 00:25:39,677
with incorporating Wikidata elements
401
00:25:39,677 --> 00:25:41,356
to our web dossier.
402
00:25:41,356 --> 00:25:44,607
A web dossier is--oh, sorry, yeah, sorry.
403
00:25:45,447 --> 00:25:49,646
A web dossier, or a classical web dossier,
consists of three parts:
404
00:25:50,248 --> 00:25:53,320
an introduction to the subject,
405
00:25:53,320 --> 00:25:56,060
mostly written by one of our researchers;
406
00:25:56,060 --> 00:26:01,328
a selection of titles, both books
and articles from our collection;
407
00:26:01,328 --> 00:26:06,146
and the third part, an annotated list
408
00:26:06,146 --> 00:26:08,876
with links to electronic resources.
409
00:26:09,161 --> 00:26:15,815
And this year, we added a fourth part
to our web dossiers,
410
00:26:15,815 --> 00:26:18,276
which is the Wikidata elements.
411
00:26:19,008 --> 00:26:22,007
And it all started last year,
412
00:26:22,007 --> 00:26:25,206
and my story is similar
to the story of Olaf, actually.
413
00:26:25,352 --> 00:26:29,570
Last year, when I had no clue
about Wikidata,
414
00:26:29,570 --> 00:26:33,402
and I discovered this wonderful
article by Alex Stinson
415
00:26:33,402 --> 00:26:36,932
on how to write a query in Wikidata.
416
00:26:37,382 --> 00:26:41,592
And he chose a subject--
a very appealing subject to me.
417
00:26:41,592 --> 00:26:45,902
Namely, "Discovering Women Writers
from North Africa."
418
00:26:46,402 --> 00:26:51,162
I can really recommend this article,
419
00:26:51,162 --> 00:26:52,981
because it's very instructive.
420
00:26:52,981 --> 00:26:57,422
And I thought I will be--
I'm going to work on this query,
421
00:26:57,422 --> 00:27:02,662
and try to change it to:
"Southern African Women Writers,"
422
00:27:02,662 --> 00:27:07,034
and try to add a link
to their work in our catalog.
423
00:27:07,311 --> 00:27:10,861
And on the right-hand side,
you see the SPARQL query
424
00:27:11,592 --> 00:27:15,181
which searches for
"Southern African Women Writers."
425
00:27:15,181 --> 00:27:20,686
If you click on the button,
on the blue button on the lefthand side,
426
00:27:21,526 --> 00:27:23,971
the search result will appear beneath.
427
00:27:23,971 --> 00:27:26,448
The search result can have
different formats.
428
00:27:26,448 --> 00:27:29,871
In my case, the search result is a map.
429
00:27:29,871 --> 00:27:32,850
And the nice thing about Wikidata
430
00:27:32,850 --> 00:27:36,652
is that you can embed
to this search result
431
00:27:36,652 --> 00:27:38,682
into your own webpage,
432
00:27:38,682 --> 00:27:42,339
and that's what we are now doing
with our work dossiers.
433
00:27:42,339 --> 00:27:47,039
So, this was the very first one
on Southern African women writers,
434
00:27:47,039 --> 00:27:49,649
listed classical three elements,
435
00:27:49,649 --> 00:27:53,209
plus this map on the lefthand side,
436
00:27:53,209 --> 00:27:55,650
which gives extra information--
437
00:27:55,650 --> 00:27:58,219
a link to the Southern African
women writer--
438
00:27:58,219 --> 00:28:00,749
a link to her works in our catalog,
439
00:28:00,749 --> 00:28:07,252
and a link to the Wikidata record
of her birth place, and her name,
440
00:28:08,219 --> 00:28:13,099
her personal record, plus a photo,
if it's available on Wikidata.
441
00:28:16,231 --> 00:28:20,329
And you have to retrieve a nice map
442
00:28:20,329 --> 00:28:24,032
with a lot of red dots
on the African continent.
443
00:28:24,032 --> 00:28:28,662
You need nice data in Wikidata,
complete, sufficient data.
444
00:28:29,042 --> 00:28:33,442
So, with our second web dossier
on public art in Africa,
445
00:28:33,442 --> 00:28:38,420
we also started to enhance
the data in Wikidata.
446
00:28:38,420 --> 00:28:43,242
In this case, for a public art--
we edited geo-locations--
447
00:28:43,242 --> 00:28:46,919
geo-locations to Wikidata.
448
00:28:46,919 --> 00:28:51,139
And we also searched for works
of public art in commons,
449
00:28:51,139 --> 00:28:55,165
and if they don't have
a record on Wikidata yet,
450
00:28:55,165 --> 00:29:00,670
we edited the record to Wikidata.
451
00:29:00,855 --> 00:29:05,327
And the third thing we do,
452
00:29:05,327 --> 00:29:09,958
because when we prepare a web dossier,
453
00:29:09,958 --> 00:29:15,514
we download the titles from our catalog,
454
00:29:15,514 --> 00:29:17,584
and the tiles are in MARC 21,
455
00:29:17,584 --> 00:29:23,226
so we have to convert them to a format
that is presentable on the website,
456
00:29:23,226 --> 00:29:28,229
and it takes not much time and effort
to convert the same set of titles
457
00:29:28,229 --> 00:29:30,457
to Wikidata QuickStatements,
458
00:29:30,457 --> 00:29:36,999
and then, we also upload
a title set to Wikidata,
459
00:29:36,999 --> 00:29:41,254
and you can see the titles we uploaded
460
00:29:41,254 --> 00:29:44,124
from our latest web dossier
461
00:29:44,124 --> 00:29:47,514
on African proverbs in Scholia.
462
00:29:48,546 --> 00:29:52,294
A really nice tool
that visualizes Scholia publications
463
00:29:52,294 --> 00:29:54,674
being present in Wikidata.
464
00:29:54,674 --> 00:29:59,674
And, one second--when it is possible,
we add a Scholia template
465
00:29:59,674 --> 00:30:01,863
to our web dossier's topic.
466
00:30:01,863 --> 00:30:03,272
Thank you very much.
467
00:30:03,272 --> 00:30:08,079
(applause)
468
00:30:09,255 --> 00:30:11,724
Thank you, Heleen and Ursula.
469
00:30:12,010 --> 00:30:16,866
Next we have Adrian Pohl
presenting using Wikidata
470
00:30:16,866 --> 00:30:22,265
to improve spatial subject indexing
and regional bibliography.
471
00:30:45,181 --> 00:30:46,621
Okay, hello everybody.
472
00:30:46,621 --> 00:30:49,630
I'm going right into the topic.
473
00:30:49,630 --> 00:30:54,146
I only have ten minutes to present
a three-year project.
474
00:30:54,535 --> 00:30:57,044
It wasn't full time. (laughs)
475
00:30:57,044 --> 00:31:00,100
Okay, what's the NWBib?
476
00:31:00,100 --> 00:31:04,404
It's an acronym for North-Rhine
Westphalian Bibliography.
477
00:31:04,404 --> 00:31:07,944
It's a regional bibliography
that records literature
478
00:31:07,944 --> 00:31:11,441
about people and places
in North Rhine-Westphalia.
479
00:31:12,534 --> 00:31:14,103
And the monograph's in it--
480
00:31:15,162 --> 00:31:19,451
there are a lot of articles in it,
and most of them are quite unique,
481
00:31:19,451 --> 00:31:22,052
so, that's the interesting thing
about this bibliography--
482
00:31:22,052 --> 00:31:25,472
because it's often
less quite obscure stuff--
483
00:31:25,472 --> 00:31:28,188
local people writing
about that tradition,
484
00:31:28,188 --> 00:31:29,488
and something like this.
485
00:31:29,612 --> 00:31:33,428
And there's over 400,000 entries in there.
486
00:31:33,428 --> 00:31:37,689
And the bibliography started in 1983,
487
00:31:37,689 --> 00:31:42,718
and so we only have titles
from this publication year onwards.
488
00:31:44,744 --> 00:31:49,166
If you want to take a look at it,
it's at nwbib.de,
489
00:31:49,166 --> 00:31:50,859
that's the web application.
490
00:31:50,859 --> 00:31:55,389
It's based on our service,
lobid.org, the API.
491
00:31:57,148 --> 00:32:01,220
Because it's cataloged as part
of the hbz union catalog,
492
00:32:01,220 --> 00:32:04,988
which comprises around 20 million records,
493
00:32:04,988 --> 00:32:08,869
it's an [inaudible] Aleph system
we get the data out of there,
494
00:32:08,869 --> 00:32:11,308
and make RDF out of it,
495
00:32:11,308 --> 00:32:16,408
and provide it as via JSON
or the HTTP API.
496
00:32:17,129 --> 00:32:20,507
So, the initial status in 2017
497
00:32:20,507 --> 00:32:25,307
was we had nearly 9,000 distinct strings
498
00:32:25,307 --> 00:32:28,727
about places--referring to places,
in North Rhine-Westphalia.
499
00:32:28,727 --> 00:32:34,187
Mostly, those were administrative areas,
like towns and districts,
500
00:32:34,187 --> 00:32:38,458
but also monasteries, principalities,
or natural regions.
501
00:32:38,907 --> 00:32:43,517
And we already used Wikidata in 2017,
502
00:32:43,517 --> 00:32:48,496
and matched those strings
with Wikidata API to Wikidata entries
503
00:32:48,496 --> 00:32:51,907
quite naively to get
the geo-coordinates from there,
504
00:32:51,907 --> 00:32:57,210
and do some geo-based
discovery stuff with it.
505
00:32:57,326 --> 00:32:59,910
But this had some drawbacks.
506
00:32:59,910 --> 00:33:02,577
And so, the matching was really poor,
507
00:33:02,577 --> 00:33:05,197
and there were a lot of false positives,
508
00:33:05,197 --> 00:33:09,184
and we still had no hierarchy
in those places,
509
00:33:09,184 --> 00:33:13,201
and we still had a lot
of non-unique names.
510
00:33:13,505 --> 00:33:15,356
So, this is an example here.
511
00:33:16,616 --> 00:33:18,378
Does this work?
512
00:33:18,494 --> 00:33:22,314
Yeah, as you can see,
for one place, Brauweiler,
513
00:33:22,314 --> 00:33:24,615
there are four different strings in there.
514
00:33:24,820 --> 00:33:27,893
So, we all know how this happens.
515
00:33:27,893 --> 00:33:31,994
If there's no authority file,
you end up with this data.
516
00:33:31,994 --> 00:33:33,894
But we want to improve on that.
517
00:33:34,614 --> 00:33:38,211
And as you can also see,
that while the matching didn't work--
518
00:33:38,211 --> 00:33:40,382
so you have this name of the place
519
00:33:40,382 --> 00:33:45,170
and there's often the name
of the superior administrative area,
520
00:33:45,170 --> 00:33:50,532
and even on the second level,
a superior administrative area
521
00:33:50,532 --> 00:33:52,040
often in the name
522
00:33:52,040 --> 00:33:58,909
to identify the place successfully.
523
00:33:58,909 --> 00:34:04,679
So, the goal was to build a full-fledged
spatial classification based on this data,
524
00:34:04,679 --> 00:34:07,109
with a hierarchical view of places,
525
00:34:09,079 --> 00:34:11,389
with one entry or ID for each place.
526
00:34:11,518 --> 00:34:17,488
And we got this mock-up
by NWBib editors in 2016, made in Excel,
527
00:34:18,048 --> 00:34:23,116
to get a feeling of what
they would like to have.
528
00:34:25,006 --> 00:34:28,198
There you have the--
Regierungsbezirk--
529
00:34:28,198 --> 00:34:31,016
that's the most superior
administrative area--
530
00:34:31,016 --> 00:34:34,918
we have in there some towns
or districts--rural districts--
531
00:34:34,918 --> 00:34:39,861
and then, it's going down
to the parts of towns,
532
00:34:39,861 --> 00:34:42,011
even to this level.
533
00:34:43,225 --> 00:34:46,232
And we chose Wikidata for this task.
534
00:34:46,232 --> 00:34:50,087
We also looked at the GND,
the Integrated Authority File,
535
00:34:50,087 --> 00:34:54,918
and GeoNames--but Wikidata
had the best coverage,
536
00:34:54,918 --> 00:34:56,902
and the best infrastructure.
537
00:34:58,112 --> 00:35:02,072
The coverage for the places
and the geo-coordinates we need,
538
00:35:02,072 --> 00:35:04,512
and the hierarchical
information, for example.
539
00:35:04,512 --> 00:35:06,732
There were a lot of places,
also, in the GND,
540
00:35:06,732 --> 00:35:09,694
but there was no hierarchical
information in there.
541
00:35:11,170 --> 00:35:13,682
And also, Wikidata provides
the infrastructure
542
00:35:13,682 --> 00:35:15,343
for editing and versioning.
543
00:35:15,343 --> 00:35:20,022
And there's also a community
that helps maintaining the data,
544
00:35:20,022 --> 00:35:22,052
which was quite good.
545
00:35:22,950 --> 00:35:26,882
Okay, but there was a requirement
by the NWBib editors.
546
00:35:27,682 --> 00:35:31,447
They did not want to directly
rely on Wikidata,
547
00:35:31,447 --> 00:35:32,972
which was understandable.
548
00:35:32,972 --> 00:35:34,982
We don't have those servers
under our control,
549
00:35:34,982 --> 00:35:38,002
and we won't know what's going on there.
550
00:35:38,084 --> 00:35:41,944
There might be some unwelcome edits
that destroy the classification,
551
00:35:41,944 --> 00:35:44,159
or parts of it, or vandalism.
552
00:35:44,159 --> 00:35:50,794
So, we decide to put
an intermediate SKOS file in between,
553
00:35:50,794 --> 00:35:55,534
on which the application would--
which should be generated from Wikidata.
554
00:35:57,113 --> 00:35:59,462
And SKOS is the Simple Knowledge
Organization System--
555
00:35:59,462 --> 00:36:03,919
it's the standard way to model
556
00:36:03,919 --> 00:36:07,519
a classification in the linked data world.
557
00:36:07,603 --> 00:36:09,278
So, how we did it? Five steps.
558
00:36:09,278 --> 00:36:14,037
I will come to each
of the steps in more detail.
559
00:36:14,037 --> 00:36:18,460
We match the strings to Wikidata
with a better approach than before.
560
00:36:18,727 --> 00:36:23,131
Created classification based
on Wikidata, edit,
561
00:36:23,131 --> 00:36:26,255
then back the links
from Wikidata to NWBib
562
00:36:26,255 --> 00:36:27,590
with a custom property.
563
00:36:27,590 --> 00:36:32,659
And now, we are in the process
of establishing a good process
564
00:36:32,659 --> 00:36:36,559
for updating the classification
in Wikidata.
565
00:36:36,619 --> 00:36:38,888
Seeing--having a DIF
of the changes,
566
00:36:38,888 --> 00:36:41,158
and then publishing it to the SKOS file.
567
00:36:42,813 --> 00:36:44,646
I will come to the details.
568
00:36:44,646 --> 00:36:46,261
So, the matching approach--
569
00:36:46,261 --> 00:36:48,356
as the API wasn't very sufficient,
570
00:36:48,356 --> 00:36:53,585
and because we have those
different levels in the strings,
571
00:36:54,441 --> 00:36:59,036
we build a custom Elasticsearch
index for our task.
572
00:36:59,596 --> 00:37:04,378
I think by now, you could probably,
as well, use OpenRefine for doing this,
573
00:37:04,378 --> 00:37:09,306
but at that point in time,
it wasn't available for Wikidata.
574
00:37:10,186 --> 00:37:14,336
And we build this index base
on SPARQL query,
575
00:37:14,336 --> 00:37:20,484
and for entities in NRW,
and with a specific type.
576
00:37:20,484 --> 00:37:25,069
And the query evolved over time a lot.
577
00:37:25,148 --> 00:37:29,157
And we have a few entries
that you can see the history on GitHub.
578
00:37:29,727 --> 00:37:32,088
So, where we put in the matching index,
579
00:37:32,088 --> 00:37:36,337
in the spatial object,
is what we need in our data.
580
00:37:36,337 --> 00:37:39,662
It's the label and the ID
or the link to Wikidata,
581
00:37:40,222 --> 00:37:43,874
the geo-coordinates, and the type
from Wikidata [inaudible], as well.
582
00:37:44,194 --> 00:37:50,488
But also for the matching, very important
that aliases and the broader thing--
583
00:37:50,488 --> 00:37:54,138
and this is also an example where the name
of the broader entity
584
00:37:54,138 --> 00:37:57,875
and the district itself are very similar.
585
00:37:57,937 --> 00:38:03,096
So, it's important to have
some type information, as well,
586
00:38:03,096 --> 00:38:04,606
for the matching.
587
00:38:04,900 --> 00:38:07,900
So, the nationwide results
were very good.
588
00:38:07,900 --> 00:38:11,110
We could automatically match
more than 99% of records
589
00:38:11,110 --> 00:38:12,265
with this approach.
590
00:38:13,885 --> 00:38:16,356
These were only 92% of the strings.
591
00:38:16,540 --> 00:38:18,140
So, obviously, the results--
592
00:38:18,140 --> 00:38:20,610
those strings that only occurred
one or two times
593
00:38:20,610 --> 00:38:22,419
often didn't appear in Wikidata.
594
00:38:22,419 --> 00:38:26,309
And so, we had to do a lot of work
with those with the [long tail].
595
00:38:27,905 --> 00:38:32,039
And for around 1,000 strings,
the matching was incorrect.
596
00:38:32,114 --> 00:38:34,950
But the catalogers did a lot of work
in the Aleph catalog,
597
00:38:34,950 --> 00:38:39,869
but also in Wikidata, they made
more than 6,000 manual edits to Wikidata
598
00:38:39,869 --> 00:38:45,019
to reach 100% coverage by adding
aliases-type information,
599
00:38:45,085 --> 00:38:46,615
creating new entries.
600
00:38:46,615 --> 00:38:49,100
Okay, so, I have to speed up.
601
00:38:49,546 --> 00:38:54,295
We created classification based on this,
on the hierarchical statements.
602
00:38:54,295 --> 00:38:58,580
P131 is the main property there.
603
00:38:59,827 --> 00:39:02,495
We added the information to our data.
604
00:39:03,035 --> 00:39:06,525
So, we now have this
in our data spatial object--
605
00:39:06,525 --> 00:39:11,535
and we focus this--the link to Wikidata,
and the types are there,
606
00:39:12,625 --> 00:39:17,554
and here's the ID
from the SKOS classification
607
00:39:17,554 --> 00:39:19,234
we built based on Wikidata.
608
00:39:20,034 --> 00:39:23,555
And you can see there
are Q identifiers in there.
609
00:39:26,940 --> 00:39:29,286
Now, you can basically query our API
610
00:39:29,286 --> 00:39:34,051
with such a query using Wikidata URIs,
611
00:39:34,316 --> 00:39:38,627
and get literature, in this example,
about Cologne back.
612
00:39:39,724 --> 00:39:45,675
Then we created a Wikidata property
for NWBib and edit those links
613
00:39:45,675 --> 00:39:50,995
from Wikidata to the classification--
batch load them with QuickStatements.
614
00:39:52,105 --> 00:39:53,634
And there's also a nice--
615
00:39:53,634 --> 00:39:59,344
also a move to using a qualifier
on this property
616
00:39:59,344 --> 00:40:02,994
to add the broader information there.
617
00:40:02,994 --> 00:40:06,333
So, I think people won't mess around
that work with this,
618
00:40:06,333 --> 00:40:09,223
and as with the P131 statement.
619
00:40:10,094 --> 00:40:11,743
So, this is what it looks like.
620
00:40:12,563 --> 00:40:16,142
This will go to the classification
where you can then start a query.
621
00:40:18,670 --> 00:40:23,293
Now, we have to build this
update and review process,
622
00:40:23,293 --> 00:40:28,692
and we will add those data like this,
623
00:40:28,692 --> 00:40:32,452
with a zero sub-field to Aleph,
624
00:40:32,452 --> 00:40:36,962
and the catalogers will start
using those Wikidata based IDs,
625
00:40:36,962 --> 00:40:41,012
URIs, for cataloging for spatial indexing.
626
00:40:44,702 --> 00:40:50,082
So, by now, there are more than 400,000
NWBib entries with links to Wikidata,
627
00:40:50,082 --> 00:40:55,905
and more than 4,400 Wikidata entries
with links to NWBib.
628
00:40:56,617 --> 00:40:58,042
Thank you.
629
00:40:58,042 --> 00:41:03,182
(applause)
630
00:41:07,574 --> 00:41:09,682
Thank you, Adrian.
631
00:41:13,312 --> 00:41:15,472
I got it. Thank you.
632
00:41:31,122 --> 00:41:34,402
So, as you've seen me before,
I'm Hilary Thorsen.
633
00:41:34,402 --> 00:41:36,152
I'm Wikimedian in residence
634
00:41:36,152 --> 00:41:38,382
with the Linked Data
for Production Project.
635
00:41:38,382 --> 00:41:39,942
I am based at Stanford,
636
00:41:39,942 --> 00:41:42,590
and I'm here today
with my colleague, Lena Denis,
637
00:41:42,590 --> 00:41:45,581
who is Cartographic Assistant
at Harvard Library.
638
00:41:45,581 --> 00:41:50,041
And Christine Fernsebner Eslao
is here in spirit.
639
00:41:50,041 --> 00:41:53,530
She is currently back in Boston,
but supporting us from afar.
640
00:41:53,530 --> 00:41:56,240
So, we'll be talking
about Wikidata and Libraries
641
00:41:56,240 --> 00:42:00,350
as partners in data production,
organization, and project inspiration.
642
00:42:00,850 --> 00:42:04,300
And our work is part of the Linked Data
for Production Project.
643
00:42:05,450 --> 00:42:08,190
So, Linked Data for Production
is in its second phase,
644
00:42:08,190 --> 00:42:10,450
called Pathway for Implementation.
645
00:42:10,450 --> 00:42:13,291
And it's an Andrew W. Mellon
Foundation grant,
646
00:42:13,291 --> 00:42:16,120
involving the partnership
of several universities,
647
00:42:16,120 --> 00:42:20,280
with the goal of constructing a pathway
for shifting the catalog community
648
00:42:20,280 --> 00:42:24,860
to begin describing library
resources with linked data.
649
00:42:24,860 --> 00:42:26,919
And it builds upon a previous grant,
650
00:42:26,919 --> 00:42:30,369
but this iteration is focused
on the practical aspects
651
00:42:30,369 --> 00:42:32,009
of the transition.
652
00:42:33,559 --> 00:42:35,650
One of these pathways of investigation
653
00:42:35,650 --> 00:42:39,000
has been integrating
library metadata with Wikidata.
654
00:42:39,429 --> 00:42:41,054
We have a lot of questions,
655
00:42:41,054 --> 00:42:42,999
but some of the ones
we're most interested in
656
00:42:42,999 --> 00:42:46,180
are how we can integrate
library metadata with Wikidata,
657
00:42:46,180 --> 00:42:49,580
and make contribution
a part of our cataloging workflows,
658
00:42:49,580 --> 00:42:53,589
how Wikidata can help us improve
our library discovery environment,
659
00:42:53,589 --> 00:42:55,929
how it can help us reveal
more relationships
660
00:42:55,929 --> 00:42:59,629
and connections within our data
and with external data sets,
661
00:42:59,629 --> 00:43:04,370
and if we have connections in our own data
that can be added to Wikidata,
662
00:43:04,370 --> 00:43:07,480
how libraries can help
fill in gaps in Wikidata,
663
00:43:07,480 --> 00:43:09,969
and how libraries can work
with local communities
664
00:43:09,969 --> 00:43:13,070
to describe library
and archival resources.
665
00:43:14,010 --> 00:43:17,129
Finding answers to these questions
has focused on the mutual benefit
666
00:43:17,129 --> 00:43:19,649
for the library and Wikidata communities.
667
00:43:19,649 --> 00:43:22,949
We've learned through starting to work
on our different Wikidata projects,
668
00:43:22,949 --> 00:43:25,279
that many of the issues
libraries grapple with,
669
00:43:25,279 --> 00:43:29,451
like data modeling, identity management,
data maintenance, documentation,
670
00:43:29,451 --> 00:43:31,289
and instruction on linked data,
671
00:43:31,289 --> 00:43:33,970
are ones the Wikidata
community works on too.
672
00:43:34,370 --> 00:43:36,099
I'm going to turn things over to Lena
673
00:43:36,099 --> 00:43:39,640
to talk about what
she's been working on now.
674
00:43:46,550 --> 00:43:51,040
Hi, so, as Hilary briefly mentioned,
I work as a map librarian at Harvard,
675
00:43:51,040 --> 00:43:54,180
where I process maps, atlases,
and archives for our online catalog.
676
00:43:54,180 --> 00:43:56,580
And while processing two-dimensional
cartographic works
677
00:43:56,580 --> 00:43:59,572
is relatively straighforward,
cataloging archival collections
678
00:43:59,572 --> 00:44:02,429
so that their cartographic resources
can be made discoverable,
679
00:44:02,429 --> 00:44:04,119
has always been more difficult.
680
00:44:04,119 --> 00:44:06,989
So, my use case for Wikidata
is visually modeling relationships
681
00:44:06,989 --> 00:44:10,389
between archival collections
and the individual items within them,
682
00:44:10,389 --> 00:44:13,210
as well as between archival drafts
in published works.
683
00:44:13,359 --> 00:44:17,329
So, I used Wikidata to highlight the work
of our cartographer named Erwin Raisz,
684
00:44:17,329 --> 00:44:19,890
who worked at Harvard
in the early 20th-century.
685
00:44:19,890 --> 00:44:22,539
He was known for his vividly detailed
and artistic land forms,
686
00:44:22,539 --> 00:44:23,939
like this one on the screen--
687
00:44:23,939 --> 00:44:26,294
but also for inventing
the armadillo projection,
688
00:44:26,294 --> 00:44:29,020
writing the first cartography
textbook in English
689
00:44:29,020 --> 00:44:31,318
and other various
important contributions
690
00:44:31,318 --> 00:44:32,919
to the field of geography.
691
00:44:32,919 --> 00:44:34,609
And at the Harvard Map Collection,
692
00:44:34,609 --> 00:44:38,509
we have a 66-item collection
of Raisz's field notebooks,
693
00:44:38,509 --> 00:44:41,359
which begin when he was a student
and end just before his death.
694
00:44:43,679 --> 00:44:46,229
So, this is the collection-level record
that I made for them,
695
00:44:46,229 --> 00:44:47,994
which merely gives an overview,
696
00:44:47,994 --> 00:44:50,513
but his notebooks are full of information
697
00:44:50,513 --> 00:44:53,351
that he used in later atlases,
maps, and textbooks.
698
00:44:53,351 --> 00:44:56,313
But researchers don't know how to find
that trajectory information,
699
00:44:56,313 --> 00:44:58,665
and the system
is not designed to show them.
700
00:45:01,030 --> 00:45:03,734
So, I felt that with Wikidata,
and other Wikimedia platforms,
701
00:45:03,734 --> 00:45:05,154
I'd be able to take advantage
702
00:45:05,154 --> 00:45:08,075
of information that already exists
about him on the open web,
703
00:45:08,075 --> 00:45:10,629
along with library records
and a notebook inventory
704
00:45:10,629 --> 00:45:12,574
that I had made in an Excel spreadsheet
705
00:45:12,574 --> 00:45:15,416
to show relationships and influences
between his works.
706
00:45:15,574 --> 00:45:18,594
So here, you can see how I edited
and reconciled library data
707
00:45:18,594 --> 00:45:20,165
in OpenRefine.
708
00:45:20,165 --> 00:45:23,164
And then, I used QuickStatements
to batch import my results.
709
00:45:23,304 --> 00:45:25,244
So, now, I was ready
to create knowledge graphs
710
00:45:25,244 --> 00:45:27,864
with SPARQL queries
to show patterns of influence.
711
00:45:30,084 --> 00:45:33,304
The examples here show
how I leveraged Wikimedia Commons images
712
00:45:33,304 --> 00:45:34,664
that I connected to him.
713
00:45:34,664 --> 00:45:36,459
And the hierarchy of some of his works
714
00:45:36,459 --> 00:45:38,604
that were contributing
factors to other works.
715
00:45:38,604 --> 00:45:42,354
So, modeling Raisz's works on Wikidata
allowed me to encompass in a single image,
716
00:45:42,354 --> 00:45:45,890
or in this case, in two images,
the connections that require many pages
717
00:45:45,890 --> 00:45:47,864
of bibliographic data to reveal.
718
00:45:51,684 --> 00:45:55,544
So, this video is going to load.
719
00:45:55,563 --> 00:45:57,233
Yes! Alright.
720
00:45:57,233 --> 00:46:00,113
This video is a minute and a half long
screencast I made,
721
00:46:00,113 --> 00:46:02,033
that I'm going to narrate as you watch.
722
00:46:02,033 --> 00:46:05,423
It shows the process of inputting
and then running a SPARQL query,
723
00:46:05,423 --> 00:46:09,283
showing hierarchical relationships
between notebooks, an atlas, and a map
724
00:46:09,283 --> 00:46:11,033
that Raisz created about Cuba.
725
00:46:11,033 --> 00:46:12,603
He worked there before the revolution,
726
00:46:12,603 --> 00:46:14,633
so he had the unique position
of having support
727
00:46:14,633 --> 00:46:17,013
from both the American
and the Cuban governments.
728
00:46:17,334 --> 00:46:20,583
So, I made this query as an example
to show people who work on Raisz,
729
00:46:20,583 --> 00:46:24,134
and who are interested in narrowing down
what materials they'd like to request
730
00:46:24,134 --> 00:46:26,154
when they come to us for research.
731
00:46:26,154 --> 00:46:29,684
To make the approach replicable
for other archival collections,
732
00:46:29,684 --> 00:46:33,105
I hope that Harvard and other institutions
will prioritize Wikidata look-ups
733
00:46:33,105 --> 00:46:35,414
as they move to linked data
cataloging production,
734
00:46:35,414 --> 00:46:37,520
which my co-presenters
can speak to the progress on
735
00:46:37,520 --> 00:46:38,854
better than I can.
736
00:46:38,854 --> 00:46:41,543
But my work has brought me--
has brought to mind a particular issue
737
00:46:41,543 --> 00:46:46,580
that I see as a future opportunity,
which is that of archival modeling.
738
00:46:47,369 --> 00:46:52,302
So, to an archivist, an item
is a discrete archival material
739
00:46:52,302 --> 00:46:55,000
within a larger collection
of archival materials
740
00:46:55,000 --> 00:46:56,884
that is not a physical location.
741
00:46:56,884 --> 00:47:00,663
So an archivist from the American National
Archives and Records Administration,
742
00:47:00,663 --> 00:47:02,943
who is also a Wikidata enthusiast,
743
00:47:02,943 --> 00:47:05,742
advised me when I was trying
to determine how to express this
744
00:47:05,742 --> 00:47:07,734
using an example item,
745
00:47:07,734 --> 00:47:10,456
that I'm going to show
as soon as this video is finally over.
746
00:47:11,433 --> 00:47:14,391
Alright. Great.
747
00:47:20,437 --> 00:47:22,100
Nope, that's not what I wanted.
748
00:47:22,135 --> 00:47:23,536
Here we go.
749
00:47:31,190 --> 00:47:32,280
It's doing that.
750
00:47:32,280 --> 00:47:34,154
(humming)
751
00:47:34,208 --> 00:47:37,418
Nope. Sorry. Sorry.
752
00:47:40,444 --> 00:47:43,045
Alright, I don't know why
it's not going full screen again.
753
00:47:43,045 --> 00:47:44,329
I can't get it to do anything.
754
00:47:44,329 --> 00:47:46,880
But this is the-- oh, my gosh.
755
00:47:46,880 --> 00:47:48,235
Stop that. Alright.
756
00:47:48,235 --> 00:47:51,195
So, this is the item that I mentioned.
757
00:47:51,575 --> 00:47:53,655
So, this was what the archivist
758
00:47:53,655 --> 00:47:55,964
from the National Archives
and Records Administration
759
00:47:55,964 --> 00:47:57,414
showed me as an example.
760
00:47:57,414 --> 00:48:02,414
And he recommended this compromise,
which is to use the part of property
761
00:48:02,414 --> 00:48:05,614
to connect a lower level description
to a higher level of description,
762
00:48:05,614 --> 00:48:08,534
which allows the relationships
between different hierarchical levels
763
00:48:08,534 --> 00:48:10,840
to be asserted as statements
and qualifiers.
764
00:48:10,840 --> 00:48:12,884
So, in this example that's on screen,
765
00:48:12,884 --> 00:48:16,294
the relationship between an item,
a series, a collection, and a record group
766
00:48:16,294 --> 00:48:19,655
are thus contained and described
within a Wikidata item entity.
767
00:48:19,655 --> 00:48:22,024
So, I followed this model
in my work on Raisz.
768
00:48:22,704 --> 00:48:26,024
And one of my images is missing.
769
00:48:26,024 --> 00:48:27,971
No, it's not. It's right there. I'm sorry.
770
00:48:28,210 --> 00:48:30,613
And so, I followed this model
on my work on Raisz,
771
00:48:30,613 --> 00:48:33,103
but I look forward
to further standardization.
772
00:48:38,983 --> 00:48:41,352
So, another archival project
Harvard is working on
773
00:48:41,352 --> 00:48:44,632
is the Arthur Freedman collection
of more than 2,000 hours
774
00:48:44,632 --> 00:48:48,702
of punk rock performances
from the 1970s to early 2000s
775
00:48:48,702 --> 00:48:51,970
in the Boston and Cambridge,
Massachussets areas.
776
00:48:51,970 --> 00:48:55,145
It includes many bands and venues
that no longer exist.
777
00:48:55,604 --> 00:48:59,505
So far, work has been done in OpenRefine
on reconciliation of the bands and venues
778
00:48:59,505 --> 00:49:02,324
to see which need an item
created in Wikidata.
779
00:49:02,886 --> 00:49:05,964
A basic item will be created
via batch process next spring,
780
00:49:05,964 --> 00:49:08,697
and then, an edit-a-thon will be
held in conjunction
781
00:49:08,697 --> 00:49:12,254
with the New England Music Library
Association's meeting in Boston
782
00:49:12,254 --> 00:49:15,866
to focus on adding more statements
to the batch-created items,
783
00:49:15,866 --> 00:49:18,937
by drawing on local music
community knowledge.
784
00:49:18,937 --> 00:49:22,086
We're interested in learning more
about models for pairing librarians
785
00:49:22,086 --> 00:49:26,310
and Wiki enthusiasts with new contributors
who have domain knowledge.
786
00:49:26,297 --> 00:49:29,293
Items will eventually be linked
to digitized video
787
00:49:29,293 --> 00:49:31,387
in Harvard's digital collection platform
788
00:49:31,387 --> 00:49:33,167
once rights have
been cleared with artists,
789
00:49:33,167 --> 00:49:35,147
which will likely be a slow process.
790
00:49:36,327 --> 00:49:38,030
There's also a great amount of interest
791
00:49:38,030 --> 00:49:41,680
in moving away from manual cataloging
and creation of authority data
792
00:49:41,680 --> 00:49:43,247
towards identity management,
793
00:49:43,247 --> 00:49:45,667
where descriptions
can be created in batches.
794
00:49:45,667 --> 00:49:48,057
An additional project that focused on
795
00:49:48,057 --> 00:49:51,297
creating international standard
name identifiers, or ISNIs,
796
00:49:51,297 --> 00:49:53,477
for avant-garde and women filmmakers
797
00:49:53,477 --> 00:49:57,657
can be adapted for creating Wikidata items
for these filmmakers, as well.
798
00:49:57,657 --> 00:50:01,076
Spreadsheets with the ISNIs,
filmmaker names, and other details
799
00:50:01,076 --> 00:50:04,697
can be reconciled in OpenRefine,
and uploaded with QuickStatements.
800
00:50:04,910 --> 00:50:06,940
Once people in organizations
have been described,
801
00:50:06,940 --> 00:50:09,316
we'll move toward describing
the films in Wikidata,
802
00:50:09,316 --> 00:50:12,526
which will likely present
some additional modeling challenges.
803
00:50:13,446 --> 00:50:15,486
A library presentation
wouldn't be complete
804
00:50:15,486 --> 00:50:16,882
without a MARC record.
805
00:50:16,882 --> 00:50:19,916
Here, you can see the record
for Karen Aqua's taxonomy film,
806
00:50:19,916 --> 00:50:22,096
where her ISNI and Wikidata Q number
807
00:50:22,096 --> 00:50:24,176
have been added to the 100 field.
808
00:50:24,176 --> 00:50:26,636
The ISNIs and Wikidata Q numbers
that have been created
809
00:50:26,636 --> 00:50:30,066
can then be batch added
back into MARC records via MarcEdit.
810
00:50:30,066 --> 00:50:33,236
You might be asking why I'm showing you
this ugly MARC record,
811
00:50:33,236 --> 00:50:35,596
instead of some beautiful
linked data statements.
812
00:50:35,596 --> 00:50:38,576
And that's because our libraries
will be working in a hybrid environment
813
00:50:38,576 --> 00:50:39,896
for some time.
814
00:50:39,896 --> 00:50:42,326
Our library catalogs still relies
on MARC records,
815
00:50:42,326 --> 00:50:44,076
so by adding in these URIs,
816
00:50:44,076 --> 00:50:46,366
we can try to take advantage
of linked data,
817
00:50:46,366 --> 00:50:48,346
while our systems still use MARC.
818
00:50:49,496 --> 00:50:52,950
Adding URIs into MARC records
makes an additional aspect
819
00:50:52,950 --> 00:50:54,335
of our project possible.
820
00:50:54,335 --> 00:50:56,894
Work has been done at Stanford
and Cornell to bring data
821
00:50:56,894 --> 00:51:01,873
from Wikidata into our library catalog
using URIs already in our MARC records.
822
00:51:02,334 --> 00:51:05,090
You can see an example
of a knowledge panel,
823
00:51:05,090 --> 00:51:06,984
where all the data is sourced
from Wikidata,
824
00:51:06,984 --> 00:51:11,004
and links back to the item itself,
along with an invitation to contribute.
825
00:51:11,403 --> 00:51:15,130
This is currently in a test environment,
not in production in our catalog.
826
00:51:15,130 --> 00:51:17,444
Ideally, eventually,
these will be generated
827
00:51:17,444 --> 00:51:19,916
from linked data descriptions
of library resources
828
00:51:19,916 --> 00:51:22,954
created using Sinopia,
our linked data editor
829
00:51:22,954 --> 00:51:24,563
developed for cataloging.
830
00:51:24,563 --> 00:51:27,994
We found that adding a look-up
to Wikidata in Sinopia is difficult.
831
00:51:27,994 --> 00:51:31,514
The scale and modeling of Wikidata
makes it hard to partition the data
832
00:51:31,514 --> 00:51:33,544
to be able to look up typed entities,
833
00:51:33,544 --> 00:51:34,900
and we've run into the problem
834
00:51:34,900 --> 00:51:37,493
of SPARQL not being good
for keyword search,
835
00:51:37,493 --> 00:51:41,883
but wanting our keyword APIs
to return SPARQL-like RDF descriptions.
836
00:51:41,883 --> 00:51:45,043
So, as you can see, we still have
quite a bit of work to do.
837
00:51:45,043 --> 00:51:47,937
This round of the grant
runs until June 2020,
838
00:51:47,937 --> 00:51:50,163
so, we'll be continuing our exploration.
839
00:51:50,163 --> 00:51:53,113
And I just wanted to invite anyone
840
00:51:53,113 --> 00:51:57,573
who's continued an interest in talking
about Wikidata and libraries,
841
00:51:57,573 --> 00:52:01,454
I lead a Wikidata Affinity Group
that's open to anyone to join.
842
00:52:01,454 --> 00:52:03,013
We meet every two weeks,
843
00:52:03,013 --> 00:52:05,513
and our next call is Tuesday,
November the 5th,
844
00:52:05,513 --> 00:52:08,073
so if you're interested
in continuing discussions,
845
00:52:08,073 --> 00:52:10,393
I would love to talk with you further.
846
00:52:10,393 --> 00:52:11,890
Thank you, everyone.
847
00:52:11,890 --> 00:52:13,623
And thank you to the other presenters
848
00:52:13,623 --> 00:52:16,893
for talking about all
of their wonderful projects.
849
00:52:16,893 --> 00:52:21,283
(applause)