Wikidata

Coming Scholia, WikiCite, Wikidata and Wikipedia sessions

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In the coming months I will have three different talks on Scholia, WikiCite, Wikidata and Wikipedia at al.:

  • 3. October 2018 in DGI-byen, Copenhagen, Denmark as part of Visuals and Analytics that Matter conference, – the concluding conference for the DEFF-sponsored project Research Output & Impact Analyzed and Visualized (ROIAV).
  • 7. November 2018 in Mannheim as part of the Linked Open Citation Database (LOC-DB) 2018 workshop.
  • 13. december 2018 at the library of the Technical University of Denmark as part of Wikipedia – a media for sharing knowledge and research, an event for researchers and students (and still in the planning phase).

In september I presented Scholia as part of the Workshop on Open Citations. The slides with title Scholia as of September 2018 is available here.

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Scholia is more than scholarly profiles

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Scholia, a website originally started as service to show scholarly profiles from data in Wikidata, is actually not just for scholarly data.

Scholia can also show bibliographic information for “literary” authors and journalists.

An example that I have begun on Wikidata is for the Danish writer Johannes V. Jensen whose works pose a very interesting test case for Wikidata, because the interrelation between the works and editions can be quite complicated, e.g., news paper articles being merged into a poem that is then published in an edition that are then expanded and re-printed… Also the scholarly and journalistic work about Johannes V. Jensen can be recorded in Wikidata. Scholia currently records 30 entries about Johannes V. Jensen, – and that does not necessarily includes works about works written by Johannes V. Jensen.

An example of a bibliography of a journalist is that of Kim Wall. Her works are almost always addressing very unique topics, – fairly relevant as sources in Wikipedia articles. Examples include an article on a special modern Chinese wedding tradition in Fairy Tale Romances, Real and Staged and an article on furries It’s not about sex, it’s about identity: why furries are unique among fan cultures.

An interesting feature about most of Wall’s articles, is that she let the interviewee have the final word by adding a quotation as the very final paragraph. That is also the case with the two examples linked above. I suppose that say something of Wall’s generous journalistic approach.

 

 

Hyppige elementer blandt bedste danske film

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Bo Green Jensen har skrevet bogen De 25 bedste danske film, hvor man blandt andet finder Vredens Dag, Kundskabens træ, Babettes gæstebud og Den eneste ene. Denne korte liste på 25 film, der blev udgivet i 2002, har jeg lige indtastet i Wikidata via “katalog”-egenskaben. Når det er gjort, kan man benytte Wikidata Query Service til, med en SPARQL-databaseforespørgsel, at finde elementer der går igen blandt filmene. En sådan SPARQL-forespørgsel kunne se sådan ud:

SELECT (COUNT(?item) AS ?count) ?value ?valueLabel WHERE {
  ?item wdt:P972 wd:Q12307844 .
  ?item ?property ?value .
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],da,en". }
}
GROUP BY ?value ?valueLabel
HAVING (COUNT(?item) > 1)
ORDER BY DESC(?count)

Denne version tæller film og ordner elementerne efter hvor mange film de enkelte elementer indgår i. Informationen i Wikidata er nok ikke helt komplet. Med Magnus Manskes Listeria-værktøj kan man dog få en tabel konstrueret der viser at hver enkelt film er rimeligt godt dækket ind.

SPARQL’en findes her og resultatet ses her.

Det er ikke overraskende at et af de elementer der findes ved alle de 25 film er at de er oplistet i De 25 bedste danske film. Det er lissom en tautologi… Hvis vi går videre ned i hyppighed finder vi at Bodil Kjer og Anne Marie Helger er de højest placerede personer.

Bodil Kjer forbindes nok mest med gråtonede film fra 1940’erne og 1950’erne – i listen finder man hende som skuespiller i Otte akkorder, John og Irene og Mød mig på Cassiopeia – men i sin senere karriere gjorde hun sig også bemærket, dels som skrøbelig frue i Strømer, dels i den første danske Oscarvindende spillefilm. Hun er ikke en overraskelse.

Hvad jeg finder overraskende er at Anne Marie Helger ligger med 5 elementer, og dermed den næsthøjeste person på listen. Hun er skuespiller i Strømer, Johnny Larsen, selvfølgelig Koks i kulissen, og Erik Clausens De frigjorte. Hun figurerer også som manuskriptforfatter på Christian Braad Thomsens film.

En tak længere nede kommer Erik Balling, Ebbe Rode, Ib Schønberg og Anders Refn. Balling er producent på to film på listen og stod for både instruktion og manuskript på Poeten og Lillemor. Anders Refn er filmklipper på to og var tillige i en dobbeltrolle med instruktion og manuskript til Strømer.

Min navnebror Finn Nielsen er med på listen i forbindelse med tre film: Strømer, Johnny Larsen og Babettes gæstebud. Han gjorde forøvrigt også en fin(n) præstation i Kærlighedens smerte, som ikke kom på listen da instruktøren allerede er repræsenteret med Kundskabens træ.

Sverige står som samproduktionsland på fire film. Det er særligt i de senere års film, men den første film er faktisk Sult som jo er fra 1960’erne.

Og så iøvrigt mangler Bodil Kjer at blive talt med en ekstra gang: Som ekstra 26. emne lister Bo Green Jensen Far til fire-serien. I denne serie indgår der en legetøjselefant ved navn Bodil Kjer…

“Og så er der fra 2018 og frem øremærket 0,5 mio. kr. til Dansk Sprognævn til at frikøbe Retskrivningsordbogen.”

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From Peter Brodersen I hear that the budget of the Danish government for next year allocates funds to Dansk Sprognævn for the release of the Retskrivningsordbogen – the Danish official dictionary for word spelling.

It is mentioned briefly in an announcement from the Ministry of Culture: “Og så er der fra 2018 og frem øremærket 0,5 mio. kr. til Dansk Sprognævn til at frikøbe Retskrivningsordbogen.”: 500.000 DKK allocated for the release of the dataset.

It is not clear under which conditions it is released. An announcement from Dansk Sprognævn writes “til sprogteknologiske formål” (to natural language processing purposes). I trust it is not just for natural language processing purposes, – but for every purpose!?

If it is to be used in free software/databases then a CC0 or better license is a good idea. We are still waiting for Wikidata for Wiktionary, the yet waporware with a multilingual, collaborative and structured dictionary. This ressource is CC0-based. The “old” Wiktionary has surprisingly not been used that much by natural language processing researcher. Perhaps because of the anarchistic structure of Wiktionary. Wikidata for Wiktionary could hopefully help with us with structuring lexical data and improve the size and the utility of lexical information. With Retskrivningsordbogen as CC0 it could be imported into Wikidata for Wiktionary and extended with multilingual links and semantic markup.

The problem with Andreas Krause

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I first seem to have run into the name “Andreas Krause” in connection with NIPS 2017. Statistics with the Wikidata Query Service shows “Andreas Krause” to be one of the most prolific authors for that particular conference.

But who is “Andreas Krause”?

Google Scholar lists five “Andreas Krause”. An ETH Zürich machine learning researcher, a pharmacometrics researcher, a wood researcher, a economish networkish researcher  working from Bath and a Dresden-based battery/nano researcher. All the NIPS Krause works should likely be attributed to the machine learning researcher, and a read of the works reveal the affiliation to be to ETH Zürich.

An ORCID search reveals six “Andrea Krause”. Three of the Andreas Krause have no or almost no further information about them beyond the name and the ORCID identifier.

There is a Immanuel Krankenhaus Berlin rheumatologist which does not seem to be in Google Scholar.

There may even be more than these six “Andreas Krause”. For instance, the article Emotional Exhaustion and Job Satisfaction in Airport Security Officers – Work–Family Conflict as Mediator in the Job Demands–Resources Model has affiliation with “School of Applied Psychology, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland”, thus topic and affiliation do not quite fit in with any of the previously mentioned “Andreas Krause”.

One interesting ambiguity is for Classification of rheumatoid joint inflammation based on laser imaging – which obviously is a rheumatology work but also has some machine learning aspects. There is computer scientist/machine learner Volker Tresp as co-author and the work is published in an IEEE venue. There is no affiliation on the “Andreas Krause” on the paper. It is likely the work of the rheumatologist, but you could also guess on the machine learner.

Yet another ambiguity is Biomarker-guided clinical development of the first-in-class anti-inflammatory FPR2/ALX agonist ACT-389949. The topic somewhat overlap between the pharmacokinetics and the domain of the Berlin researcher. The affiliation is to “Clinical Pharmacology, Actelion”, but interestingly, Google Scholar does not associate this paper with the pharmacokinetics researcher.

In conclusion, author disambiguation may be very difficult.

Scholia will can show the six Andreas Krause. But I am not sure that helps us very much.

Female GitHubbers

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In Wikidata, we can record the GitHub user name with the P2037 property. As we typically also has the gender of the person we can make a SPARQL query that yields all female GitHub users recorded in Wikidata. There ain’t no many. Currently just 27.

The Python code below gets the SPARQL results into a Python Pandas DataFrame and queries the GitHub API for followers count and adds the information to a dataframe column. Then we can rank the female GitHub users according to follower count and format the results in a HTML table

Code

 

import re
import requests
import pandas as pd

query = """
SELECT ?researcher ?researcherLabel ?github ?github_url WHERE {
  ?researcher wdt:P21 wd:Q6581072 .
  ?researcher wdt:P2037 ?github .
  BIND(URI(CONCAT("https://github.com/", ?github)) AS ?github_url)
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
"""
response = requests.get("https://query.wikidata.org/sparql",
                        params={'query': query, 'format': 'json'})
researchers = pd.io.json.json_normalize(response.json()['results']['bindings'])

URL = "https://api.github.com/users/"
followers = []
for github in researchers['github.value']:
    if not re.match('^[a-zA-Z0-9]+$', github):
        followers.append(0)
        continue
    url = URL + github
    try:
        response = requests.get(url,
                                headers={'Accept':'application/vnd.github.v3+json'})
        user_followers = response.json()['followers']
    except: 
        user_followers = 0
    followers.append(user_followers)
    print("{} {}".format(github, followers))
    sleep(5)

researchers['followers'] = followers

columns = ['followers', 'github.value', 'researcherLabel.value',
           'researcher.value']
print(researchers.sort(columns=['followers'], ascending=False)[columns].to_html(index=False))

Results

The top one is Jennifer Bryan, a Vancouver statistician, that I do not know much about, but she seems to be involved in R-studio.

Number two is Jessica McKellar is a well-known figure in the Python community. Number four and five, Olga Botvinnik and Vanessa Sochat, are bioinformatician and neuroinformatician, respectively (or was: Sochat has apparently left the Poldrack lab in 2016 according to her CV). Further down the list we have people from the wikiworld, Sumana Harihareswara, Lydia Pintscher and Lucie-Aimée Kaffee.

I was surprised to see that Isis Agora Lovecruft is not there, but there is no Wikidata item representing her. She would have been number three.

Jennifer Bryan and Vanessa Sochat are almost “all-greeners”. Sochat has just a single non-green day.

I suppose the Wikidata GitHub information is far from complete, so this analysis is quite limited.

followers github.value researcherLabel.value researcher.value
1675 jennybc Jennifer Bryan http://www.wikidata.org/entity/Q40579104
1299 jesstess Jessica McKellar http://www.wikidata.org/entity/Q19667922
475 triketora Tracy Chou http://www.wikidata.org/entity/Q24238925
347 olgabot Olga B. Botvinnik http://www.wikidata.org/entity/Q44163048
124 vsoch Vanessa V. Sochat http://www.wikidata.org/entity/Q30133235
84 brainwane Sumana Harihareswara http://www.wikidata.org/entity/Q18912181
75 lydiapintscher Lydia Pintscher http://www.wikidata.org/entity/Q18016466
56 agbeltran Alejandra González-Beltrán http://www.wikidata.org/entity/Q27824575
22 frimelle Lucie-Aimée Kaffee http://www.wikidata.org/entity/Q37860261
21 isabelleaugenstein Isabelle Augenstein http://www.wikidata.org/entity/Q30338957
20 cnap Courtney Napoles http://www.wikidata.org/entity/Q42797251
15 tudorache Tania Tudorache http://www.wikidata.org/entity/Q29053249
13 vedina Nina Jeliazkova http://www.wikidata.org/entity/Q27061849
11 mkutmon Martina Summer-Kutmon http://www.wikidata.org/entity/Q27987764
7 caoyler Catalina Wilmers http://www.wikidata.org/entity/Q38915853
7 esterpantaleo Ester Pantaleo http://www.wikidata.org/entity/Q28949490
6 NuriaQueralt Núria Queralt Rosinach http://www.wikidata.org/entity/Q29644228
2 rongwangnu Rong Wang http://www.wikidata.org/entity/Q35178434
2 lschiff Lisa Schiff http://www.wikidata.org/entity/Q38916007
1 SigridK Sigrid Klerke http://www.wikidata.org/entity/Q28152723
1 amrapalijz Amrapali Zaveri http://www.wikidata.org/entity/Q34315853
1 mesbahs Sepideh Mesbah http://www.wikidata.org/entity/Q30098458
1 ChristineChichester Christine Chichester http://www.wikidata.org/entity/Q19845665
1 BinaryStars Shima Dastgheib http://www.wikidata.org/entity/Q42091042
1 mollymking Molly M. King http://www.wikidata.org/entity/Q40705344
0 jannahastings Janna Hastings http://www.wikidata.org/entity/Q27902110
0 nmjakobsen Nina Munkholt Jakobsen http://www.wikidata.org/entity/Q38674430

Find titles of all works published by DTU Cognitive Systems in 2017

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Find titles of all works published by DTU Cognitive Systems in 2017! How difficult can that be? To identify all titles of works from a research organization? With Wikidata and the Wikidata Query Service (WDQS) at hand it shouldn’t be that difficult to do? Nevertheless, I ran into a few hatches:

  1. There is what we can call the “Nathan Churchill Problem”: Nathan Churchill was at one point affiliated with our research section Cognitive Systems and wrote papers, e.g., together with our Morten Mørup. One paper clearly identifies him as affiliated with our section. Searching the DTU website yields no homepage for him though. He is now at St. Michael’s Hospital, Toronto according to a newer paper. So is he no longer affiliated with the Cognitive Systems section? That’s somewhat difficult to establish with credible and citable sources. If he is not, then any simple SPARQL query on the WDQS for Cognitive Systems papers will yield his new papers which shouldn’t be counted as Cognitive Systems section papers. If we could point to a source that indicates whether his affiliation at our section is stopped we could add a qualifier to the P1416 property in his Wikidata entry and extend the SPARQL query. What I ended up doing, was to explicitly filter out two of Churchill’s publications with the ugly line “FILTER(?work != wd:Q42595201 && ?work != wd:Q36384548)“. The problem is of course not just confined to Churchill. For instance, Scholia currently lists new publications by our Søren Hauberg at the Scholia page for DIKU, – a department where he has previously been affiliated. We discussed the affiliation problem a bit in the Scholia paper, see page 253 (page 17).
  2. Datetime datatype conversion with xsd:dateTime. The filter on date is with this line: “FILTER(?publication_datetime >= "2017-01-01"^^xsd:dateTime)“. Something like “FILTER(?publication_datetime >= xsd:dateTime(2017))” does not work.
  3. Missing data. It is difficult to establish how complete the Wikidata listing is for our section with respect to publications. Scraping Google Scholar, PubMed and our local university database of publications could be a possibility, but this is far from streamlined with the tools I have developed.

The full query is listed below and the result is available from this link. Currently, 48 results are returned.

#defaultView:Table
SELECT ?workLabel 
WITH {
  SELECT 
    ?work (MIN(?publication_datetime) AS ?datetime)
  WHERE {
    # Find CogSys work
    ?researcher wdt:P108 | wdt:P463 | wdt:P1416/wdt:P361* wd:Q24283660 .
    ?work wdt:P50 ?researcher .
    ?work wdt:P31 wd:Q13442814 .
    
    # Nathan Churchill seems not longer to be affiliated!?
    FILTER(?work != wd:Q42595201 && ?work != wd:Q36384548)
    
    # Filter to year 2017
    ?work wdt:P577 ?publication_datetime .
    FILTER(?publication_datetime >= "2017-01-01"^^xsd:dateTime)
  }
  GROUP BY ?work 
} AS %results
WHERE {
  INCLUDE %results
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en,da,de,es,fr,jp,nl,nl,ru,zh". }
}