Notes on my work with (cultural heritage) FAIR data. Topics cover Programming, Open Science, Digital Humanities, Knowledge Graphs, Linked Data, Metadata, data catalogs, Wikidata, RDF, SPARQL and the Semantic Web as well as personal interests.

Royal Library of Belgium

Sven Lieber

Hi everyone,  today’s summer-break edition is for anyone who likes free and open knowledge. There is a short video that explains the intriguing new
<strong>
 Wikifunctions
</strong>
project. Besides that I will talk about my experiences of creating a Wikidata page about a company, hopefully it inspires you to also contribute to free and open knowledge.

FAIR Data Digest #11

Hi everyone,  last week I told you about
<em>
 European Research Infrastructures Consortia
</em>
or ERIC in short. Today we gonna look into one of these ERICs that is active in the Cultural Heritage field. I also have to celebrate a tiny anniversary related to Wikidata and I will share a resource about FAIR Jupyter notebooks.

FAIR Data Digest #7

Dear subscriber,  Welcome to the first edition of my new weekly newsletter! Within different recurring categories I will provide updates of my work, my personal journey, and topics such as Knowledge Graphs, Linked Data, FAIR data, Open Science and many more. It is conference season!

FAIR Data Digest #1

Hi everyone,  today’s edition is about FAIR data that is not necessarily related to cultural heritage, but related to cars 🚗. We will have a look into an Open Data dataset of the Dutch government that provides data which hopefully helps me to answer some questions about
<strong>
 child safety seats
</strong>
. Statistics shown in this post come from this GitHub repository.

FAIR Data Digest #23

Hi everyone,  I hope you had a great summer. The academic year is about to start and I hope you are hungry for some knowledge :-) In today’s edition I want to introduce you to the European Open Science Cloud (EOSC).  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #14

Hi everyone,  this last summer-break edition ties in with the previous edition. Last week I’ve wrote over research to make
<strong>
 publication metadata
</strong>
from the free publishing service CEUR-WS
<strong>
 available in a FAIR fashion
</strong>
, such that the data can be explored.

FAIR Data Digest #13

Hi everyone,  welcome to the 3rd edition of my newsletter! Besides the fact that we have suffered the first official heat wave in Belgium this year, I have other hot information to share.

FAIR Data Digest #3

Dear subscriber,  welcome to the second edition of the newsletter and also a warm welcome to all new subscribers. It has been an interesting week. In this edition I will talk about some work updates, a one-day workshop I’ve attended last week and I have a video recommendation.

FAIR Data Digest #2

Hi everyone,  welcome to today’s edition on the the
<em>
 European Day of Languages
</em>
(Q496423).  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest! Subscribe for free to receive new posts and support my work.

FAIR Data Digest #17

Hi everyone,  in today’s slightly more technical edition I will focus on my recent encounters with the Linked Data query language SPARQL. Many of you may know it as a language to retrieve data (possibly from Wikidata). I mainly use it for a project where I recently found some performance issues that could be resolved by either rewriting the query or changing the approach.

FAIR Data Digest #22

Hello everyone,  welcome to today’s Halloween edition about
<em>
 data anti-patterns
</em>
that hopefully will scare you so much you don’t do them :-) Things you better avoid when aiming for FAIR data. Anti patterns?

FAIR Data Digest #20

Hi everyone,  greetings from Karlsruhe in Germany where I am attending a conference about research data infrastructure at the moment.

FAIR Data Digest #15

Hi everyone,  I’m on vacation now, but thanks to pre-scheduling you won’t miss out FAIR data updates! Today I want to share with you a research paper in which the authors report on the creation of large amounts of FAIR data which they host on Wikidata.

FAIR Data Digest #12

Hi everyone,  it has been a while since we talked about research infrastructures. Many of them might sound abstract or just like super specialized app stores. But today we have a brief look into something very tangible: heritage objects that are analyzed by instruments or are digitized in other forms. There is lots of diverse data in Heritage Science. ❓ FAIR Buzzword Bingo: what is E-RIHS?

FAIR Data Digest #21

Hi everyone,  welcome back to today’s edition in which I share a work update related to bibliographic data and provide information to an upcoming interesting talk. ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest! Subscribe for free to receive new posts and support my work.

FAIR Data Digest #19

Hi everyone,  this edition starts with two announcements before it dives into the topic of software engineering skills for data science. This newsletter started as a
<strong>
 weekly newsletter
</strong>
in which I shared topics around FAIR data and updates about my work. Selecting one or more topics each week is fun and helps myself to
<em>
 summarize
</em>
and
<em>
 understand
</em>
topics better.

FAIR Data Digest #18

Hi everyone,  welcome to another summer-break edition. This time with a webinar recording about persistent identifiers for FAIR data and news about data cataloging with DCAT both in Europe and the US.  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #10

Hi everyone,  greetings to one of our summer editions! Whether you're on vacation, just back, or about to head off, I've got some interesting content to share with you.

FAIR Data Digest #9

Hi everyone,  in today’s edition I will try to explain what DARIAH / CLARIAH is and what they do in the context of FAIR data. I am also sharing a video of some recent presentations about Wikipedia from the Wikimedia Research Showcase. ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #8

Hi everyone,  if you are a researcher and you want to follow the FAIR data principles you are not left alone. In today’s edition I will try to explain what ERIC is and what they have to do with FAIR data. As for work updates: I was on a small camping vacation in France, so there are no work updates, but I came back with many ideas for the future!

FAIR Data Digest #6

Hi everyone,  did you ever wonder how FAIR data is put into practice? This week I went down the rabbit hole and what I brought back is the start of a new series to
<strong>
 unravel the many FAIR data initiatives
</strong>
. There are also some good news for (Belgian) FAIR data in my work update!

FAIR Data Digest #5

Hi everyone,  this week it’s all about hands-on! I will talk about a webinar on trying to fix quality issues in Wikidata by using its ontology. Additionally I will share a few resources around basics and advanced uses of the query language SPARQL that is used to query Linked Data, for example from Wikidata.

FAIR Data Digest #4

Hi everyone,  as promised, today you will hear more about the research data infrastructure conference where I and around
<strong>
 680 other people
</strong>
attended last week in Karlsruhe, Germany. Grab a cup of coffee of tea and enjoy today’s edition!

FAIR Data Digest #16

FAIR Data Digest

Hi everyone,

welcome back to today's edition in which I share a work update related
to bibliographic data and provide information to an upcoming interesting
talk.

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🏢 Books exist in many different versions. The same book might be
published as paperback or as ebook and that even in different
translations. Both for user experience and data quality it is
interesting to cluster the bibliographic records of such editions of a
book together. Algorithms to do that were described in several research
papers in the past. But I couldn't find any easy to use open source
tool. In today's work update I talk about the clustering and **present a
fast implementation in Python that I developed recently** that hopefully
can easily be reused. \#FAIRsoftware.

📅 Are you interested in Digital Humanities or are you curious about
Belgian journalism? Join us **next Monday** for the next online session
of The **DH virtual discussion group for early career researchers**
where [Sergio Alonso
Mislata](https://www.linkedin.com/in/sergio-alonso-mislata-24646113b/)
will present behind the scenes of his work for the *Centre for Archives
on the Media and Information
([CAMille](https://www.kbr.be/en/projects/camille/))*, a joint lab
between the Royal Library of Belgium and the Université Libre de
Bruxelles (ULB).

[DH Virtual Discussion
Group](https://bib.kuleuven.be/english/artes/artesresearch/dh-virtual-discussion-group){.button
.primary}

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</div>

# 🏢 Work update: FRBR clustering {#work-update-frbr-clustering}

<div>

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</div>

What do the books \"*The invention of Nature*\" and \"*De uitvinder van
de natuur*\" have in common? Well, they are all different versions of
the same *work* \"*The invention of nature*\" by Andrea Wulf
([Q24908385](https://www.wikidata.org/wiki/Q24908385)), whether it is in
a different format or a different language. In technical terms, these
records are on the so-called *manifestation* level. But for simplicity I
will refer to them simply as book versions.

Library catalogs usually contain records of all the different versions.
However, identifying the higher *work* level has advantages for both the
librarians and the users of the catalog. In today's work update (no pun
intended) I want to talk about how I recently did this for the BELTRANS
project in a performant way using a so-called *inverted index*.

A few research papers were published in the past decade about how one
can cluster manifestation-level book descriptions in library catalogs to
work-level records. I collected a few relevant references in [a
list](https://orkg.org/list/R608176) at the Open Research Knowledge
Graph (ORKG) ([Q108127463](https://www.wikidata.org/wiki/Q108127463)).

::: {.captioned-image-container}
<figure>
<a
href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg"
class="image-link is-viewable-img image2" target="_blank"
data-component-name="Image2ToDOM"></a>
<div class="image2-inset">
<img
src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg"
class="sizing-normal"
data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:478,&quot;width&quot;:1399,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:88986,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/jpeg&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:null}"
srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F634bd2ba-8094-484c-86eb-42c74db841ea_1399x478.jpeg 1456w"
sizes="100vw" loading="lazy" width="1399" height="478" />
<div class="image-link-expand">
<img
src="data:image/svg+xml;base64,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"
class="lucide lucide-maximize2" />
</div>
</div>
<figcaption>An illustration of how the clustering algorithm finds
matches: for each book version (red) multiple descriptive keys are
created (blue). If at least one such key overlaps the two book versions
should be clustered together.</figcaption>
</figure>
:::

What I missed so far is a practical modern implementation. Something
that does not require to install a whole XML-based software stack and a
Java application. I implemented such a **clustering algorithm in
Python** and released it in a GitHub repository from the Royal Library
of Belgium (DOI:
[10.5281/zenodo.10011416)](https://zenodo.org/doi/10.5281/zenodo.10011416).
Check out my blog where I provide some more details and
behind-the-scenes.

[Read the full blog post for
details](https://doi.org/10.59350/4hd4r-1tk44){.button .primary}

<div>

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</div>

That's it for this week of the FAIR Data Digest. If you found the
content interesting, please share and subscribe. See you *in two weeks*!

Sven

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Hi everyone,

this week it's all about hands-on! I will talk about a webinar on trying
to fix quality issues in Wikidata by using its ontology. Additionally I
will share a few resources around basics and advanced uses of the query
language SPARQL that is used to query Linked Data, for example from
Wikidata.

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📅 Wikidata does *not* have a *formal* ontology, yet there are thousands
of classes and properties that are used by the community to describe
data. Last week I attended a very interesting webinar about the ontology
of Wikidata. The webinar covered, h**ow the ontology works, why it is
important and how to find and try to solve data quality issues.**

🖥️ Besides Wikidata, you may store your RDF data in your own (RDF)
database. Then you even have more possibilities to **use some advanced
data management features** like we do in the
[BELTRANS](https://github.com/kbrbe/beltrans-data-integration) project.
On the recent DHBenelux conference, [Daria
Kondakova](https://twitter.com/dareiadareia) and Jakob Kohler were
looking for solutions to describe personas of Greek Mythology in
different contexts. I think that this is a nice use case for
named-graphs. **I will briefly introduce what named graphs are and how
they can be used with SPARQL**. Additionally I will link some resources
to SPARQL basics.

🏢 Last but not least, in my work update I will talk about the
generalization of a Python script to enrich incomplete tabular data in
an automated way. And also how you could do the same, more manually,
with the Excel-like tool **OpenRefine**.

<div>

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</div>

# 📅 Events {#events}

<div>

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</div>

## Webinar about the Ontology of Wikidata

Each entity (page) in Wikidata should have a type or represent a type!
There are usually entities that describe types such as Human
([Q5](https://www.wikidata.org/wiki/Q5)) and entities that represent
*instances* of these types, such as *Paul Otlet*
([Q1868](https://www.wikidata.org/wiki/Q1868)) who is an "instanceOf" a
Human.

Unfortunately there are millions of pages that **miss such basic type
information**. This makes the data **less FAIR and more difficult to
query and analyze**. These kind of issues and potential ways of trying
to fix it was part of the Europeana Data Spaces webinar *The Ontology of
Wikidata, how to interact with it for a better quality* that I have
attended last Tuesday.

In this very hands-on webinar, [Camillo
Pellizzari](https://www.wikidata.org/wiki/Q74862511) presented the
usefulness of Wikidata's ontology (the types mentioned above, but also
the properties used to create subject-property-object triples). For
example, if you query for people with the occupation engineers you only
get a partial result. But if you tweak the SPARQL query a bit to also
include subclasses of Engineers
([Q81096](https://www.wikidata.org/wiki/Q81096)) **you get a more
complete picture**. Subclasses are for example System Engineers
([Q1572123](https://www.wikidata.org/wiki/Q1572123)) or Geological
Engineers ([Q2887387](https://www.wikidata.org/wiki/Q2887387)) that by
the way have subclasses as well. Check yourself:
[Query](https://query.wikidata.org/#SELECT%20%3Fperson%0AWHERE%20%7B%0A%20%20%3Fperson%20wdt%3AP106%20wd%3AQ81096%20.%0A%7D)
to get engineers (45,072 results) vs
[query](https://query.wikidata.org/#SELECT%20%3Fperson%0AWHERE%20%7B%0A%20%20%3Fperson%20wdt%3AP106%2Fwdt%3AP279%2a%20wd%3AQ81096%20.%0A%7D)
to get engineers and subclasses of it (111,316 results).

Camillo presented the amazing tool [Wikidata Graph
Builder](https://angryloki.github.io/wikidata-graph-builder/) that can
be used to visualize content from Wikidata. For example, the following
little visualization shows the engineer class in the type hierarchy (on
the top right you can choose different visualizations).

::: {.captioned-image-container}
<figure>
<a
href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png"
class="image-link is-viewable-img image2" target="_blank"
data-component-name="Image2ToDOM"></a>
<div class="image2-inset">
<img
src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png"
class="sizing-normal"
data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:679,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:83750,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:null}"
srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F751c5123-a4a9-4e50-8df9-4b4355cca15c_1893x883.png 1456w"
sizes="100vw" loading="lazy" width="1456" height="679" />
<div class="image-link-expand">
<img
src="data:image/svg+xml;base64,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"
class="lucide lucide-maximize2" />
</div>
</div>
</figure>
:::

But when clicking on the top left on Mode and select Reverse, you will
see the many sub and sub sub classes of engineers (shown below)! Don't
worry, you can zoom in within the tool. [Try it
out](https://angryloki.github.io/wikidata-graph-builder/?item=Q81096&property=P279&mode=reverse&graph_direction=down)
yourself!

::: {.captioned-image-container}
<figure>
<a
href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png"
class="image-link image2" target="_blank"
data-component-name="Image2ToDOM"></a>
<div class="image2-inset">
<img
src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png"
class="sizing-normal"
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srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F202f5ba2-08e2-4167-a35d-cbbac2b70132_1512x287.png 1456w"
sizes="100vw" loading="lazy" width="1456" height="276" />
<div>

</div>
</div>
</figure>
:::

Furthermore, Camillo demonstrated how one, based on SPARQL queries **can
identify problematic entity pages**. For example the ones without a
type. And how one can try to manually try to fix it by assigning a
correct type or adding new hierarchy levels in the ontology based on
trustworthy information from other thesauri such as RAMEAU from the
National Library of France.

He stressed that you can only ***try*** to fix it. Sometimes it is very
complex to fully understand what an existing entity represents and how
it relates to other existing entities. **This is also my main takeaway:
humans remain necessary to correctly curate Knowledge Graphs!**

[Read more about the
webinar](https://www.wikidata.org/wiki/User:Ep%C3%ACdosis/Wikidata-intro#Ontology){.button
.primary}

<div>

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</div>

# 🖥️ Coding corner {#coding-corner}

<div>

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</div>

## Advanced Linked Data management with named graphs

With the Resource Description Framework (RDF) you can represent Linked
Data as subject-predicate-object triples. **But what if you have to
represent additional context of those triples?** You can do this with
*Quads, s*tatements with *four* components: subject, predicate, object
and *context*.

You can imagine those named graph context as a statement saying in which
"box" the triple belongs! The following figure illustrates different
named graphs using different colors. It also shows that with the query
language SPARQL you have different ways to query data in those named
graphs.

::: {.captioned-image-container}
<figure>
<a
href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp"
class="image-link is-viewable-img image2" target="_blank"
data-component-name="Image2ToDOM"></a>
<div class="image2-inset">
<img
src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp"
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data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/82161223-69a0-4e84-bb65-877d04800a79_960x373.webp&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:373,&quot;width&quot;:960,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:null,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:null}"
srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F82161223-69a0-4e84-bb65-877d04800a79_960x373.webp 1456w"
sizes="100vw" loading="lazy" width="960" height="373" />
<div class="image-link-expand">
<img
src="data:image/svg+xml;base64,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"
class="lucide lucide-maximize2" />
</div>
</div>
</figure>
:::

Named graphs are a very powerful technique to manage RDF triples.
However, different implementations of triple stores and SPARQL engines
treat named graphs differently, **leading to unexpected and
non-interoperable results!**

**How can you use named graphs and how do these different SELECT
statements work?** Check out my new Blog post to find the answer! I
explain and visually illustrate examples inspired by a discussion on the
recent DHBenelux conference and by my work on the BELTRANS project.

[Read the full blog
post](https://sven-lieber.org/en/2023/06/26/rdf-named-graphs/){.button
.primary}

<div>

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</div>

## SPARQL basics at the ISE lecture

While talking about SPARQL, are you interested in learning (or looking
up) some basics? Just last week the slides of the the *Information
Service Engineering (ISE)* lecture on SPARQL from prof. [Harald
Sack](https://sigmoid.social/@lysander07)
([Q30078997](https://www.wikidata.org/wiki/Q30078997)) were made
accessible on
[GoogleDrive](https://drive.google.com/file/d/13gLES1BYGi9E__-tBsuWno6rPffFkR5G/view).

If you are interested in more RDF or simply are more of a video person,
you can also check out a previous edition of the ISE lecture series on
YouTube!

[Watch the 2021 ISE lectures on
YouTube](https://www.youtube.com/@ISEFIZKarlsruhe/playlists?view=50&sort=dd&shelf_id=2){.button
.primary}

<div>

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</div>

# 🏢 Work updates {#work-updates}

<div>

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</div>

Last week I talked about a newly implemented Python script we used to
enrich incomplete tabular data with data from the ISNI database.

I generalized the script such that you can configure also **other
Application Programming Interfaces (APIs)** besides the one from the
ISNI database. The first release of this command line tool is also
available using the following identifier [DOI:
10.5281/zenodo.8075058](https://doi.org/10.5281/zenodo.8075058) (thanks
to the GitHub integration of Zenodo).

However, I have to say that after implementing it, I realized that the
tool OpenRefine can do the same! Thus if you prefer a user interface and
if you are not afraid of adding a few snippets of parsing code to
OpenRefine yourself, you could use it instead of our command line
script.

The added value of our command line script remains: without much
configuration you can call the command line script. Among others, this
allows to **perform the enrichment in an automated fashion** **without
manually interacting** with a user interface.

[How to use OpenRefine to query SRU
APIs](https://www.oclc.org/developer/news/2018/using-oclc-apis-in-open-refine.en.html){.button
.primary}

<div>

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</div>

That's it for this week of the FAIR Data Digest. I hope you found the
content interesting. Don't forget to share or subscribe. See you next
week!

Sven

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