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426:" a systematic mapping study in order to identify the current topical coverage of existing research as well as the white spots which need further investigation. 67 peer-reviewed research from journals and conference proceedings were selected, and classified into meaningful categories. We describe this data set descriptively by showing the publication frequency, the publication venue and the origin of the authors and reveal current research focuses. These especially include aspects concerning data quality, including questions related to language coverage and data integrity. These results indicate a number of future research directions, such as, multilingualism and overcoming language gaps, the impact of plurality on the quality of Wikidata's data, Wikidata's potential in various disciplines, and usability of user interface."
547:"We ... study the evolution that editors with different levels of engagement exhibit in their editing behaviour over time. We measure an editor’s engagement in terms of (i) the volume of edits provided by the editor and (ii) their lifespan (i.e. the length of time for which an editor is present at Wikidata). The large-scale longitudinal data analysis that we perform covers Wikidata edits over almost 4 years. We monitor evolution in a session-by-session- and monthly-basis, observing the way the participation, the volume and the diversity of edits done by Wikidata editors change. Using the findings in our exploratory analysis, we define and implement prediction models that use the multiple evolution indicators."
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articles but included considerably more conflicts and many participants instead of a few trained coders. This procedure a stronger ingroup bias for (1) more recent conflicts and (2) conflicts in which the proportion of ingroup members among the top editors was larger. Finally, a third study ruled out that these effects were driven by translations or the raters’ own nationality. Therefore, this paper is the first to demonstrate ingroup bias in
Knowledge – a finding that is of practical as well as theoretical relevance as we outline in the discussion."
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401:"Despite Wikidata’s potential and the notable rise in research activity, the field is still in the early stages of study. Most research is published in conferences, highlighting such immaturity, and provides little empirical evidence of real use cases. Only a few disciplines currently benefit from Wikidata’s applications and do so with a significant gap between research and practice. Studies are dominated by European researchers, mirroring Wikidata’s content distribution and limiting its Worldwide applications."
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1302:(or as above, "People tend to do more when collaborating with more people") paper: a "Project is either a repository on GitHub or a page on Knowledge." So editors who edit pages edited by more editors tend to edit more pages. Perhaps causation flows from editing more pages to editing pages edited by more editors, instead of the other way around as stated in the third paragraph of the conclusion without any causal analysis in support. (I.e., individual productivity drives collaboration instead.)
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sizes. This effect is not an artifact of the heterogeneity of productivity: the relation between group size and productivity holds at the individual level. People tend to do more when collaborating with more people. We propose a generative model of individual productivity that captures the non-linearity in collaboration effort. The proposed model is able to explain and predict group work dynamics in GitHub and
Knowledge by capturing their maximally informative behavioral features."
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515:" we study the relationship between different Wikidata user roles and the quality of the Wikidata ontology. Our analysis shows that the Wikidata ontology has uneven breadth and depth. We identified two user roles: contributors and leaders. The second category is positively associated to ontology depth, with no significant effect on other features. Further work should investigate other dimensions to define user profiles and their influence on the knowledge graph."
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324:), it is useful to understand what articles are particularly difficult for novices to learn from, such as the computing concepts studied in this research. This is content that likely becomes even more confusing if imperfect machine translation leads to odd sentence structure or word choice. Perhaps it should be prioritized for cleanup by native speakers.
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The authors describe how computer programmers often end up at
Knowledge by way of Stack Overflow posts that link Knowledge as a means of better understanding concepts mentioned in an answer. The ability of these communities to build on Knowledge is a really lovely example of beneficial re-use. It has
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2020 examines the utility of
Knowledge articles about computing concepts for novice programmers. The researchers recruit 18 students with varying computer science backgrounds to read Knowledge articles about computing concepts that are new to them. The authors use a sample of four Knowledge articles
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and
Knowledge. By analyzing the activity of over 2 million users on these platforms, we discover that the interplay between group size and productivity exhibits complex, previously-unobserved dynamics: the productivity of smaller groups scales super-linearly with group size, but saturates at larger
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in a more favourable way (e.g., Argentina in the
Spanish article and the United Kingdom in the English article) and, in reverse, the outgroup as more immoral and more responsible for the conflict. These findings were replicated and extended in Study 2, which was limited to the lead sections of
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836:. In: Denny Vrandečić, Kalina Bontcheva, Mari Carmen Suárez-Figueroa, Valentina Presutti, Irene Celino, Marta Sabou, Lucie-Aimée Kaffee, Elena Simperl, eds., Proceedings of the 17th International Semantic Web Conference (ISWC'18), volume 11137 of LNCS, 376-394, 2018. Springer
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685:) in the corresponding language versions of Knowledge (e.g., the Spanish and English Knowledge articles about the Falklands War). Study 1 featured a content coding of translated Knowledge articles by trained raters, which showed that articles systematically presented the
244:). Side note: in a sample of 44 million posts on Stack Overflow that the authors process, 360 thousand (0.8%) have a Knowledge link, pointing to 40 thousand different Knowledge articles in aggregate. They indicate that this rate of linking to Knowledge is similar on the
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Sarasua, Cristina; Checco, Alessandro; Demartini, Gianluca; Difallah, Djellel; Feldman, Michael; Pintscher, Lydia (2018-12-15). "The
Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits".
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While the authors conclude that linking to more structured learning resources from Stack
Overflow and related forums might be beneficial, this research clearly provokes some thought about how Knowledge might be a more effective learning context. For instance,
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are a clear improvement for readers who are not familiar with the concepts mentioned in an article. The other concepts emphasize the value of surfacing examples in articles, not relying on mathematical notation to explain a concept, and having a clear
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Yes that makes sense if the causation flows from collaboration to productivity as the authors state. But this is one of those frustrating papers that spends a lot of redundant effort demonstrating the correlation without analyzing the causation. It's
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Schrage, Florian; Heist, Nicolas; Paulheim, Heiko (2019). "Extracting
Literal Assertions for DBpedia from Knowledge Abstracts". In Maribel Acosta; Philippe Cudré-Mauroux; Maria Maleshkova; Tassilo Pellegrini; Harald Sack; York Sure-Vetter (eds.).
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Ruprechter, Thorsten; Santos, Tiago; Helic, Denis (2020). "On the
Relation of Edit Behavior, Link Structure, and Article Quality on Knowledge". In Hocine Cherifi; Sabrina Gaito; José Fernendo Mendes; Esteban Moro; Luis Mateus Rocha (eds.).
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dumps, and linked data APIs are now forming the backbone of many uses of Wikidata. We describe this influential use case and its underlying infrastructure The data used in this publication is available in the form of anonymised
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Oeberst, Aileen; Beck, Ina von der; Matschke, Christina; Ihme, Toni Alexander; Cress, Ulrike (1 December 2019). "Collectively biased representations of the past: Ingroup Bias in Knowledge articles about intergroup conflicts".
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where they talk through what they are doing and thinking as they try to learn about the concept. The authors then analyzed the transcripts from these interviews to determine what themes were consistent across the students.
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Mora-Cantallops, M., Sánchez-Alonso, S. and García-Barriocanal, E. (2019), "A systematic literature review on Wikidata", Data Technologies and Applications, Vol. 53 No. 3, pp. 250-268.
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Asai, Akari; Hashimoto, Kazuma; Hajishirzi, Hannaneh; Socher, Richard; Xiong, Caiming (2019-11-24). "Learning to Retrieve Reasoning Paths over Knowledge Graph for Question Answering".
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Other recent publications that could not be covered in time for this issue include the items listed below. Contributions, whether reviewing or summarizing newly published research,
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Lucchini, Lorenzo; Tonelli, Sara; Lepri, Bruno (December 2019). "Following the footsteps of giants: modeling the mobility of historically notable individuals using Knowledge".
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Costa-jussà, Marta R.; Lin, Pau Li; España-Bonet, Cristina (2019-12-10). "GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Knowledge Biographies".
437:"Example from Knowledge with a correct and an incorrect example extracted, as well as non-matching literals marked in the abstract" (from "Extracting Literal Assertions ...")
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This study found that on Knowledge, controversial and high-quality articles articles differ from others, according to metrics quantifying editing and linking behavior.
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we present an approach for extracting numerical and date literal values from Knowledge abstracts . We show that our approach can add 643k additional literal values to
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piece. Good general suggestions for improvements. I'm especially active in removing tangential information, fixing terminology issues and improving leads. ~
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266:: if vocabulary or notation has a different meaning in other contexts, this can confuse those readers who think they know what they're reading (but don't).
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This preprint presents a tool for extracting multilingual and gender-balanced parallel corpora at sentence level, with document and gender information.
284:: peripheral information that is not core to learning a concept can make it hard to find the most useful information, especially for non-native readers.
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This study found that the migration place for historically relevant people is limited to few locations, depending on discipline and opportunities.
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Farda-Sarbas, Mariam; Mueller-Birn, Claudia (2019-08-29). "Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata".
290:: math notation and code in articles is generally not explained, which can create confusion for the reader if they are not familiar with it.
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Piscopo, Alessandro; Simperl, Elena (November 2018). "Who Models the World?: Collaborative Ontology Creation and User Roles in Wikidata".
211:"Understanding Knowledge as a Resource for Opportunistic Learning of Computing Concepts" by Martin P. Robillard and Christoph Treude of
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539:"The Evolution of Power and Standard Wikidata Editors: Comparing Editing Behavior over Time to Predict Lifespan and Volume of Edits"
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Murić, Goran; Abeliuk, Andres; Lerman, Kristina; Ferrara, Emilio (2019-11-07). "Collaboration Drives Individual Productivity".
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How useful is Knowledge for novice programmers trying to learn computing concepts?: And other new research publications.
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A monthly overview of recent academic research about Knowledge and other Wikimedia projects, also published as the
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673:"Collectively biased representations of the past: Ingroup Bias in Knowledge articles about intergroup conflicts"
555:"Following the footsteps of giants: Modeling the mobility of historically notable individuals using Knowledge"
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to its full potential was to provide reliable and powerful services for data sharing and query, and the
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991:. Studies in Computational Intelligence. Cham: Springer International Publishing. pp. 242–254.
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834:"Getting the Most out of Wikidata: Semantic Technology Usage in Knowledge’s Knowledge Graph"
466:"Getting the Most out of Wikidata: Semantic Technology Usage in Knowledge’s Knowledge Graph"
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As machine translation is explored as a means of supporting content creation (e.g., via the
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805:. Lecture Notes in Computer Science. Cham: Springer International Publishing. pp. 288–294.
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This preprint presents a new graph-based recurrent retrieval approach to answer multi-hop
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Stanislav Malyshev, Markus Krötzsch, Larry González, Julius Gonsior, Adrian Bielefeldt:
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How useful is Knowledge for novice programmers trying to learn computing concepts?
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The researchers identified the following challenges to learning from Knowledge:
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Many technical articles on Knowledge have the problems identified in the
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has chosen to rely on semantic technologies for this purpose. A live
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A photomontage currently used as the lead illustration in both the
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See also earlier coverage of a related paper by Piscopo et al.: "
272:: explanations are not always enough. Examples are often desired.
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won the Best Paper Award in the In-Use Track of ISWC 2018.
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been examined more widely in work by Vincent et al. (see
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Semantic Systems. The Power of AI and Knowledge Graphs
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which is so often neglected. I'm going to show it to
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Proceedings of the ACM on Human-Computer Interaction
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First literature survey of Wikidata quality research
1282:If your comment has not appeared here, you can try
251:as well. The participants are instructed to use a
197:Knowledge as a learning resource (for programmers)
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731:Robillard, Martin P.; Treude, Christoph (2020).
352:for other data sources available to researchers)
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1300:"Collaboration Drives Individual Productivity"
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663:Knowledge's articles about the Falklands War (
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393:"A systematic literature review on Wikidata"
528:, and earlier coverage of a related paper:
893:Computer Supported Cooperative Work (CSCW)
304:. Two other thoughts about this research:
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761:https://doi.org/10.1108/DTA-12-2018-0110
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1413:Explore Knowledge history by browsing
1358:and see if someone wants to try that.
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530:"Participation patterns on Wikidata"
341:dataset on active editors by country
1470:Knowledge Signpost archives 2020-01
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190:Wikimedia Research Newsletter
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1334:20:03, 27 January 2020 (UTC)
1312:03:51, 27 January 2020 (UTC)
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18:Knowledge:Knowledge Signpost
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493:Wikidata SPARQL query logs
224:that appear frequently in
901:10.1007/s10606-018-9344-y
360:Other recent publications
1378:That's a lot to digest!
522:comments about the paper
318:content translation tool
1394:Opportunistic Learning
1275:. To follow comments,
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854:(CSCW): 141–1–141:18.
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238:Levenshtein distance
230:Dependency injection
1138:"Recent research" →
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511:From the abstract:
446:From the abstract:
422:From the abstract:
397:From the abstract:
288:Unfamiliar Notation
1262:Discuss this story
1237:WikiProject report
1232:On the bright side
1192:Arbitration report
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920:Repository version
486:endpoint, regular
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367:are always welcome
350:meta:Research:Data
311:this past write-up
253:think-aloud method
242:Regular expression
45:← Back to Contents
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1286:purging the cache
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94:PDF download
1452:Suggestions
1269:transcluded
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382:Miriam Redi
172:Miriam Redi
144:X (Twitter)
1356:WT:WPSTATS
1096:1911.11787
1036:1911.10470
972:1912.04778
786:1908.11153
745:25 January
719:References
234:Endianness
82:Share this
77:Contribute
22:2020-01-27
1446:Subscribe
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1273:talk page
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1202:Gallery
687:ingroup
661:Spanish
657:English
454:] at a
452:DBpedia
339:public
332:Briefly
228:posts (
1298:et al.
1296:Murić
1242:Humour
740:SIGCSE
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905:ISSN
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815:ISBN
747:2020
659:and
380:and
215:and
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524:by
488:RDF
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