Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientiﬁc articles, only 8.9% are entries describing a person and the number of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata. To ﬁll this gap and to improve the content of Wikidata in general, we established a workﬂow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be transferred to other enrichments based on ORCID data. This is extension is beneﬁcial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes

Förstner, K.

Galke, L.

Melnychuk, T.

Schultz, C.

Seidlmayer, E.

Tochtermann, K.

Voß, J.

English

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ORCID for Wikidata – Data enrichment forscientometric applications ?Eva Seidlmayer1[0000−0001−7258−0532], Jakob Voß2[0000−0002−7613−4123], TetyanaMelnychuk3[0000−0002−7258−2842], Lukas Galke4[0000−0001−6124−1092], KlausTochtermann4, Carsten Schultz3[0000−0002−5984−9872], and Konrad U.Förstner1,5[0000−0002−1481−2996]1 ZB MED – Information Centre for Life Sciences, Cologne and Bonn, Germany2 Verbundzentrale GBV, Göttingen, Germany3 Institute for Innovation Research, Kiel University, Germany4 ZBW – Leibniz Information Centre for Economics, Kiel and Hamburg, Germany5 University for Applied Sciences, Cologne, GermanyAbstract. Due to its numerous bibliometric entries of scholarly arti-cles and connected information Wikidata can serve as an open and richsource for deep scientometrical analyses. However, there are currentlycertain limitations: While 31.5% of all Wikidata entries represent scien-tific articles, only 8.9% are entries describing a person and the numberof entries researcher is accordingly even lower. Another issue is the fre-quent absence of established relations between the scholarly article itemand the author item although the author is already listed in Wikidata.To fill this gap and to improve the content of Wikidata in general, weestablished a workflow for matching authors and scholarly publicationsby integrating data from the ORCID (Open Researcher and Contribu-tor ID) database. By this approach we were able to extend Wikidataby more than 12k author-publication relations and the method can betransferred to other enrichments based on ORCID data. This is exten-sion is beneficial for Wikidata users performing bibliometrical analysesor using such metadata for other purposes.Keywords: Wikidata · ORCID · data enrichment · bibliometrics · sci-entometrics1 IntroductionThe open knowledge base Wikidata is increasingly used also to integrate anduse bibliographic data [16]. Scholarly articles account for almost a third of allWikidata items, far beyond items about humans with roughly 9% [15] of entries.During the bibliometric research project Q-Aktiv, we retrieved social con-text information on authors of scientific publications from Wikidata in order to? Copyright c© 2020 for this paper by its authors. Use permitted under Creative Com-mons License Attribution 4.0 International (CC BY 4.0)2 E. Seidlmayer et al.integrate the information into our data set. Of more than 8M scholarly publi-cations published by 39.5M authors from the 1953 till May 2019 related to thetopic of cholesterol we were able to find about 95% in Wikidata. However, metadata for only 3% of the authors could be retrieved and used downstream for theanalysis of social context. In order to avoid the common problems of author dis-ambiguation, we used the relation between author items and publication itemsfor identification of authors. There we determined two major reasons for thisobservation: Missing Wikidata items for authors and the absence of representedrelations publications and their authors in Wikidata.To overcome this shortcoming and to improve the foundation for biblio-graphic analysis in general, we established a workflow for matching authors andingesting ORCID data [9]. ORCID provides a authors centric, self-curated collec-tion of scholarly metadata linked to an unique identifier. As of 2020, the ORCIDdatabase contains more than 9.6M researcher profiles and includes almost 62Mscientific publications linked to these profiles [10]. The public information ofeach user’s record is published under an open license and can contain details onpublications, education, employments, funding as well as further information.Here we will first present the context of the bibliometric research project inwhich the need for an improvement of Wikidata information appeared. After-wards, we will describe, how we harvest ORCID for information on publicationsand their authors, how we query Wikidata for existing items that are also listedin ORCID and how we perform the matching to items. The benefits of such anORCID based extension of Wikidata’s corpus of scholarly inform especially theinclusion of further relations will become obvious.2 Context and application: bibliometric project Q-AktivQ-Aktiv is a collaborative project of ZB MED - Information Centre for LifeSciences, ZBW – Leibniz information Centre for Economics and Kiel University.The project aims for a better understanding of developing research areas apply-ing a bibliometric analysis. We used scientific publication data annotated withMedical Subject Headings (MeSH) vocabulary by National Library of Medicine(NLM) hosted in ZB MED database ”Knowledge Environment” to map topicsand relevant documents. By doing so, we were able to observe if publications oftwo or more distinct research fields that did not share keywords, start doing itat a later time stage. This moving of research topics towards each other can bedescribed as convergence. The opposing phenomenon of research fields is termeddivergence. As a third option, a new research topic can evolve in the intersectionof converging research area.In the Q-Aktiv project, we first analyzed the use case cholesterol [3]. Wefound that among other observations the concept of cardiovascular diseases -as time goes by - enters the field of cholesterol. Analyzing the keywords allo-cated to the publications, we developed a learning based similarity measure forconcepts of evolving research fields. In Q-Aktiv, we calculated the similarity ascosine distance between keywords [2]. A low cosine distance represents a closeORCID for Wikidata 3relationship between topics that are annotated to the same papers, while a highcosine distance indicates a low similarity of topics.The learning based network analysis is based on a knowledge graph con-taining publications, author names and keywords. However, apart from the de-scriptive observation, we were so far unable to identify reasons for the observedconvergence. Still, we could speculate and developed new working hypothesis forthis observation: We know that the development of scientific fields is driven bypeople and that people actively decide address certain rearch questions. Scien-tists decide to take up topics, and initiate or join collaborative projects. Based onthese consideration, the questions emerged who those researchers are who lead tochanges of research fields? Does a change in the researchers social environmentresult in a change of research questions and influence convergence and divergenceof scientific fields? Is science – not completely but partially – influenced by ashifting social composition of the group of researchers? Investigations regardingnetworks, citation behaviour, or social conditions of publication, in particular,would benefit from more information related to the authors and research groups[8]. One main difficulty is generating a solid data foundation for substantiatedanalysis [4]. The inclusion of additional data sources would broaden the basis ofinfometric analyses and contribute to a consolidation of knowledge. However, wecurrently face the limitations of existing tools and accessible sources. To obtaina deep understanding of the research fields development, the integration of socialcontext information such as affiliation, gender or education into the analysis canbe beneficial. Luckily, such information can be found in Wikidata.3 Wikidata for bibliographic data enrichmentIn order to introduce social context information, we haved developed a librarywritten in Python. The library which named ”Take it personally” (T.I.P.) sup-ports enrichment of author information based on Wikidata [11,13]. We choseWikidata as a source since it provides numerous links via publication identi-fiers such as DOI (Digital Object identifier), PMID (PubMed ID) and PMC(PubMed Central). In addition, Wikidata’s query API support an easy retrievalof information [7]. Apart from the scholarly article (Q13442814, [5]) items, au-thors are represented by the author property (P50, [6]) items, which allows toswitch from the publication centric view in our bibliographic data source to anauthor centered view. Wikidata’s community approach enables individuals tocorrect their own entries. With regards to sensible information such as gender,Wikidata introduces queer gender declarations in addition to male and female.Applying our T.I.P. library, we enriched several data collections based onWikidata. Overall, we experienced a low coverage of identified authors of publi-cations. For several data sets complied for the topic cholesterol consisting of morethan 14M, 8M, and 99k publications, we were able to detect authors for less than5% of the publications. As comparision: The coverage for a recent COVID-19data set (23k publications with 138k authors) was with 13% significantly higher.4 E. Seidlmayer et al.We assume that there are two reasons for the low coverage: First, the choles-terol set contains older publications which are not as well covered and curatedas current digitally available publications. The very recent COVID-19 set con-firms this assumption with an almost tripled coverage. A second reason are thatseveral conditions are needed to be fulfilled for the identification of authors: Onthe one hand, the publication itself needs to be registered, on the other hand, theauthor needs to be registered in Wikidata as well. Furthermore, a link betweenboth items has to be established for the retrieval of information on authors orig-inating from the publication. While tools for manually creating these links exist[12], an automatic process would be preferable.4 Establishing publication-author matches with ORCIDTo improve the Wikidata based retrieval of social context information on sci-entific authors, we tried to establish a general approach that is beneficial for abroader community and does not only solve our issue.ORCID was determined by us as data source that can help to fill the infor-mation gap. It contains a large number of researchers with connections to theirpublications and provides a persistent identifier for these researchers. It canonly be created by the researchers themselves and is curated by the person or itsinstitution. The entry may include information regarding the professional CVcontaining details on publications, education, funding, employment and mem-berships. Since those statements are made by the researchers themselves thedata is highly trustworthy – though the list of literature does not has to becomplete. The ORCID organisation publishes the public information under aCreative Commons Zero license (CC0) which allows as frictionless reuse.The available data snapshop of 2019 contains 673k individuals who have anORCID account and associated information. 134k of the ORCID ID could bemapped to available Wikidata items.The rich collection of ORCID could be used to extend Wikidata by numerousfurther publications.However, we decided, also after discussion with other members of the Wiki-data community, to only use the data set for improving available items. Themain argument was to avoid producing a large number records with minimalmetadata and no links to or from other items. Bearing also the finit capacity ofWikidata in mind, we therefore focus on matching existing items in order to im-prove and condense the existing data by favouring to introduce rather relationsbetween items than more thin items themselves. Linked data sets live from theconnections, not from the actual number of items.5 Estimating the potential of enrichmentsFor the purpose to estimate the potential of ORCID for matching authors andpublications we extracted all publications listed in ORCID-archive-occurrencein Wikidata. The ORCID public data set for 2019 consists of a meta file calledORCID for Wikidata 5summaries and eleven activities files [1]. In the first of the eleven activities files,we had been able to identify 3.8M publications by DOI, PMID, PMC, DNB(Deutsche National Bibliothek ID) and eid (Scopus ID).For this subset 457k associated publications could be found in Wikidata butonly 32k of those had author item linked to them. This means by far the largershare of 425k articles items were not connected to any author item. It is possiblethat the authors are recorded in the publication item but only with a plain string(Property author name string (P2093, [17]), not author (P50, [6]). Though, suchinformation cannot be automatically translated into link to author items. At thispoint it would be easy to introduce the researcher who recorded the publicationto ORCID as author to the Wikidata publication item. As mentioned before,the discussion with other members of the Wikidata community revealed that nonew items should be created. This is why we limited the approach to authorswith existing Wikidata items.For the 32k publication items recorded with author items in Wikidata, wehad to find out if the associated author information contains the known author.Since the publication is extracted from the ORCID file it is still linked to theresearcher who declared it as her own or his own. This connection can be takeninto account by checking whether the initial author is correctly entered to thepublication item.All together the eleven activities files bear the following content: More than34M publications registered identified by one or more identifier such as DOI,PubMed id, PubMedCentral id, Scopus id, or DNB id and claimed by the au-thor as own work. Based on this, it was possible to identify more than 2.6Mpublications registered in Wikidata. For more than 820k of those publicationsthe authors could be detected. Unfortunately, we were facing issues due to thelimitation of the Wikidata API which was returning inconsistent numbers of re-sult items for the same requests. We tried to fix this by requesting small chunksof data and multiple passes but still could not solve the issue. This shortcomingof the Wikidata API is also reported by others.6 Data preparation and submissionAs a result of the analysis, we created CSV files containing the following identi-fiers: author item identifier, ORCID ID, given name, family name, article itemidentifier and multiple item identifiers of all publication-authors. Based on this,we created JSON templates containing the required properties. Wikidata imple-ments a publication centered view of the semantic data. There is no propertysuch as is author of but the conection starting from the publication implementedby the has author propert (has author). The property can be qualified with theauthor item identifier. In addition, also P1932, has author string, can be addedand qualified with the string of the author.Applying the tool Wikibase CLI [14] for creating a bot, we submitted the dataas JSON to introduce the information to the publication entries into Wikidata.Afterwards the matching was performed. In total it was possible to includes6 E. Seidlmayer et al.more than 948k articles and detect more than 792k authors which did not havean item in Wikidata yet. We found more that 47k authors were listed correctly.Using our bot we had been able to introduce more than 12k authors to the paperitems as their originators.The code for implementing the workflow as Shell and Python Scripts is de-posited at Zenodo and can be retrieved at https://doi.org/10.5281/zenodo.4088048.7 Conclusion and outlookWe have developed an efficient approach to improve the author publication linksin Wikidata base on data from the ORCID database. As the ORCID researchersenter statements on their own publications, the information is trustworthy andproblems of author disambiguation are avoided. Based on this established work-flow we can easily introduce any other information included in the ORCIDdatabase. This can be direct statements on researcher items regarding aspects ofthe scientific biography and current research activity. Analogue to the approachof interweaving existing items presented here we could also introduce for exam-ple relations between organizations and researchers. It needs to be discussed inthe community weather this is the kind of information that Wikidata carriesfurther.Soon there will be the release of the new ORCID data set for 2020 and wewill continue our integration of such data into Wikibase.AcknowledgementsThis work was supported by BMBF of Germany within the program QuantitativeWissenschaftsforschung under grant numbers 01PU17013A, 01PU17013B, and01PU17013C.The work was worked out in major parts within the fellowship program FreiesWissen (Free Knowledge) 2019 / 2020 by Wikimedia Germany, Stifterverband,and Volkswagenstiftung.References1. Figshare: ORCID Public Data File 2019, https://doi.org/https://doi.org/10.23640/07243.9988322.v22. Galke, L., Melnychuk, T., Seidlmayer, E., Trog, S., Förstner, K. U., Schultz, C.& Tochtermann, K., (2019). Inductive Learning of Concept Representations fromLibrary-Scale Bibliographic Corpora. In: David, K., Geihs, K., Lange, M. & Stumme,G. (Hrsg.), INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Infor-matik für Gesellschaft. Bonn: Gesellschaft für Informatik e.V.. (S. 219-232). DOI:10.18420/inf2019 263. Melnychuk, T., Galke, L., Seidlmayer, E., Förstner, K., Tochtermann, K., Schultz,C.: Development of Similarity Measures from Graph-Structured Bibliographic Meta-data: An Application to Identify Scientific Convergence. Manuscript submitted forpublication for Scientometrics (2020)ORCID for Wikidata 74. Mihaljević, H., Tullney, M., Santamaŕıa, L., Steinfeldt, C.: Reflections on Gen-der Analyses of Bibliographic Corpora. Frontiers in Big Data 2(29) (2019),from https://www.frontiersin.org/article/10.3389/fdata.2019.00029/full,https://doi.org/10.3389/fdata.2019.000295. https://www.wikidata.org/wiki/Q134428146. https://www.wikidata.org/wiki/Property:P507. Mitraka, E., Waagmeester, A., Burgstaller-Muehlbacher, S., Schriml, L. M., Su, A.I., Good, B. M.: Wikidata: A platform for data integration and dissemination for thelife sciences and beyond. BioRxiv (2015), from https://doi.org/10.1101/031971,https://doi.org/10.1101/0319718. Nielsen, F. A., Mietchen, D., Willighagen, E.:Scholia and scientometrics withwiki-data. https://zenodo.org/record/1036595.XThTQvyxU5k (2017)9. ORCID for Wikidata GitHub Repository, https://github.com/EvaSeidlmayer/orcid-for-wikidata. Last accessed 29 Jul 202010. ORCID: ORCID Statistics, https://orcid.org/statistics. Last acccessed 08Oct 202011. Seidlmayer, E., Galke, L., Melnychuk, T., Schultz, C., Tochtermann, K., Förstner,K. U.: Take it Personally - A Python library for data enrichment in infometricalapplications. In: SEMANTICS Posters & Demos 2019, Karlsruhe (2019)12. Smith, A.: Author Disambiguator. 2020 https://author-disambiguator.toolforge.org/13. TIPlib, Github Repository, https://github.com/foerstner-lab/TIPlib. Lastacccessed 12 Aug 202014. Wikibase-CLI, GitHub Repository, https://github.com/maxlath/wikibase-cli.Last accessed 29 Jul 202015. Wikidata Statistics 2020, https://www.wikidata.org/wiki/Wikidata:Statistics. Last accessed 29 Jul 202016. Wyatt, L., Ayers, P., Proffitt, M., Mietchen, D., Seiver, E., Stinson, A., Tara-borelli, D., Virtue, C., Tud, J., Curiel, J.: WikiCite Annual Report, 2019–20 (2020)https://doi.org/10.5281/zenodo.386981017. https://www.wikidata.org/wiki/Property:P2093

ORCID for Wikidata. Data enrichment for scientometric applications

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ORCID for Wikidata. Data enrichment for scientometric applications

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