Wiki-research-l March 2019

wiki-research-l@lists.wikimedia.org

26 participants
19 discussions

Wikipedia Research policy
by song＠cs.umn.edu 14 Jul '23

14 Jul '23

Pursuant to prior discussions about the need for a research policy on Wikipedia, WikiProject Research is drafting a policy regarding the recruitment of Wikipedia users to participate in studies. At this time, we have a proposed policy, and an accompanying group that would facilitate recruitment of subjects in much the same way that the Bot Approvals Group approves bots. The policy proposal can be found at: http://en.wikipedia.org/wiki/Wikipedia:Research The Subject Recruitment Approvals Group mentioned in the proposal is being described at: http://en.wikipedia.org/wiki/Wikipedia:Subject_Recruitment_Approvals_Group Before we move forward with seeking approval from the Wikipedia community, we would like additional input about the proposal, and would welcome additional help improving it. Also, please consider participating in WikiProject Research at: http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Research -- Bryan Song GroupLens Research University of Minnesota

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Query user history edits
by Haifeng Zhang 28 Mar '19

28 Mar '19

Dear folks, Is there a good way to query a user's edit history, e.g., edit count during a period? My current solution is using usercontribs API (https://www.mediawiki.org/wiki/API:Usercontribs). But, the process has been stalled maybe due to some query limit. Thanks, Haifeng Zhang

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LDK Conference || 20.-22.5.19 || Leipzig University
by Sebastian Hellmann 27 Mar '19

27 Mar '19

Apologies for cross-posting ==== 2nd Language, Data and Knowledge (LDK) conference May 20-22, 2019 Leipzig, Germany ==== Call for Participation With the advent of digital technologies, language data becomes more and more valuable. In that context, we would like to draw your attention to the 2nd Language, Data and Knowledge (LDK) conference which will be held in Leipzig from May 20th till 22nd, 2019. This new biennial conference series aims at bringing together researchers from across disciplines concerned with language data in data science and knowledge-based applications. We invite practitioners, professionals, researchers and anyone with an interest in language data to be part of this event in Leipzig and catch up with the latest research outcomes in the areas of acquisition, provenance, representation, maintenance, usability, quality as well as legal, organizational and infrastructure aspects of language data. # Keynote Speakers We already have the following keynote speakers confirmed - Keynote #1: Christian Bizer, Mannheim University - Keynote #2: Christiane Fellbaum, Princeton University - Keynote #3: Eduard Werner, Leipzig University # Associated Events The following events are co-located with LDK 2019: 3rd Summer Datathon on Linguistic Linked Open Data (SD-LLOD-19) from May 12th to 17th, 2019 at the Leibniz Center for Informatics in Wadern, Germany Please find more information about the Datathon here: https://datathon2019.linguistic-lod.org/ Workshops on the 20th May 2019 - Translation Inference across Dictionaries (TIAD) - 3rd Workshop on Humanities in the Semantic web (WHiSe III) - 2nd OntoLex Face to Face Meeting Please find more information about the workshops here: http://2019.ldk-conf.org/co-located-events/ DBpedia Community Meeting on the 23rd May 2019 - 13th DBpedia Community Meeting https://wiki.dbpedia.org/meetings/Leipzig2019 Further information on the LDK program can be found here: http://2019.ldk-conf.org/events-programme/ To register and be part of the LDK conference and its associated events, please go to http://2019.ldk-conf.org/registration/. We are looking forward to meeting you in Leipzig. Kind regards Your LDK-Team

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Request for testing and feedback: Automated article section recommendations
by Diego Saez-Trumper 25 Mar '19

25 Mar '19

The Wikimedia Research team <https://www.mediawiki.org/wiki/Wikimedia_Research> is developing a new method for automatically recommending sections to add to stub articles. This method uses machine learning to suggest sections that could be added to an article, based on the sections that exist in other articles on similar topics. We think this method could be useful for helping new editors find useful onboarding tasks to do. But before we build anything, we need to test the quality of the recommendations. We are looking for experienced editors to evaluate these recommendations and provide feedback to help us improve them. We have built a testing tool <https://secrec.wmflabs.org> that makes it easy to provide quick, survey-style feedback on the quality of the recommendations, and we are also interested in more detailed feedback on the project feedback talkpage <https://meta.wikimedia.org/wiki/Research_talk:Expanding_Wikipedia_articles_…>. If you are interested in giving us feedback, please get started by reading the instructions <https://meta.wikimedia.org/wiki/Research:Expanding_Wikipedia_articles_acros…> here and then start rating articles! If you have questions about the project or more general feedback, you can reach us here <https://meta.wikimedia.org/wiki/Research_talk:Expanding_Wikipedia_articles_…>. Sincerely, Jonathan Morgan <https://en.wikipedia.org/wiki/User:Jmorgan_(WMF)> & Diego Saez-Trumper <https://en.wikipedia.org/wiki/User:Diego_(WMF)>, Wikimedia Foundation Research

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Upcoming Research Newsletter: New Papers Open For Review
by Mohammed Sadat Abdulai 24 Mar '19

24 Mar '19

Hi everyone, We’re preparing for the March 2019 research newsletter and looking for contributors. Please take a look at https://etherpad.wikimedia.org/p/WRN201903 and add your name next to any paper you are interested in covering. Our target publication date is on March 30 UTC. If you can't make this deadline but would like to cover a particular paper in the subsequent issue, leave a note next to the paper's entry in the etherpad. As usual, short notes and one-paragraph reviews are most welcome. Highlights from this month: - Analysis of the Wikipedia Network of Mathematicians - Detecting pages to protect in Wikipedia across multiple languages - How the medium shapes the message: Printing and the rise of the arts and sciences - Trajectories of Blocked Community Members: Redemption, Recidivism and Departure - What is the central bank of Wikipedia? - Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test” Mohammed Abdulai and Tilman Bayer [1] http://meta.wikimedia.org/wiki/Research:Newsletter

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please hire a CTO who wants to protect reader privacy
by James Salsman 23 Mar '19

23 Mar '19

I noticed just now that the Foundation is soliciting applications for a new CTO: https://www.linkedin.com/feed/update/urn:li:activity:6515003866130505729 Can we please hire a CTO who would prefer to protect reader privacy above the interests of any State or non-state actors, whether they have infiltrated staff, contractor, and NDA signatory ranks, and whether it interferes with reader statistics and analytics or not, please? In particular, I would like to repeat my request that we should not be logging personally identifiable information which might increase our subpoena burden or result in privacy violation incidents. Fuzzing geolocation is okay, but we should not be transmitting IP addresses into logs across even a LAN, for example, and we certainly shouldn't be purchasing hardware with backdoor coprocessors wasting electricity and exposing us to government or similar intrusions: https://lists.wikimedia.org/pipermail/analytics/2017-January/005696.html Best regards, Jim

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[Wikimedia Research Showcase] March 20 at 11:30 AM PST, 18:30 UTC
by Janna Layton 20 Mar '19

20 Mar '19

Hello everyone, I'm just sending a reminder that the below Showcase will be starting in half an hour. -Janna Layton Hi all, The next Research Showcase, “Learning How to Correct a Knowledge Base from the Edit History” and “TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables” will be live-streamed this Wednesday, March 20, 2019, at 11:30 AM PST/18:30 UTC (Please note the change in time in UTC due to daylight saving changes in the U.S.). The first presentation is about using edit history to automatically correct constraint violations in Wikidata, and the second is about interlinking Wikipedia tables. YouTube stream: https://www.youtube.com/watch?v=6p62PMhkVNM As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases at https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase . This month's presentations: Learning How to Correct a Knowledge Base from the Edit History By Thomas Pellissier Tanon (Télécom ParisTech), Camille Bourgaux (DI ENS, CNRS, ENS, PSL Univ. & Inria), Fabian Suchanek (Télécom ParisTech), WWW'19. The curation of Wikidata (and other knowledge bases) is crucial to keep the data consistent, to fight vandalism and to correct good faith mistakes. However, manual curation of the data is costly. In this work, we propose to take advantage of the edit history of the knowledge base in order to learn how to correct constraint violations automatically. Our method is based on rule mining, and uses the edits that solved violations in the past to infer how to solve similar violations in the present. For example, our system is able to learn that the value of the [[d:Property:P21|sex or gender]] property [[d:Q467|woman]] should be replaced by [[d:Q6581072|female]]. We provide [https://tools.wmflabs.org/wikidata-game/distributed/#game=43 a Wikidata game] that suggests our corrections to the users in order to improve Wikidata. Both the evaluation of our method on past corrections, and the Wikidata game statistics show significant improvements over baselines. TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables By Besnik Fetahu Wikipedia tables represent an important resource, where information is organized w.r.t table schemas consisting of columns. In turn each column, may contain instance values that point to other Wikipedia articles or primitive values (e.g. numbers, strings etc.). In this work, we focus on the problem of interlinking Wikipedia tables for two types of table relations: equivalent and subPartOf. Through such relations, we can further harness semantically related information by accessing related tables or facts therein. Determining the relation type of a table pair is not trivial, as it is dependent on the schemas, the values therein, and the semantic overlap of the cell values in the corresponding tables. We propose TableNet, an approach that constructs a knowledge graph of interlinked tables with subPartOf and equivalent relations. TableNet consists of two main steps: (i) for any source table we provide an efficient algorithm to find all candidate related tables with high coverage, and (ii) a neural based approach, which takes into account the table schemas, and the corresponding table data, we determine with high accuracy the table relation for a table pair. We perform an extensive experimental evaluation on the entire Wikipedia with more than 3.2 million tables. We show that with more than 88% we retain relevant candidate tables pairs for alignment. Consequentially, with an accuracy of 90% we are able to align tables with subPartOf or equivalent relations. Comparisons with existing competitors show that TableNet has superior performance in terms of coverage and alignment accuracy. -- Janna Layton (she, her) Administrative Assistant - Audiences & Technology Wikimedia Foundation <https://wikimediafoundation.org/>

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WikiLabels maintenance on Tuesday, March 19
by James Hare 19 Mar '19

19 Mar '19

The Scoring Platform Team at the Wikimedia Foundation will be performing maintenance on WikiLabels <https://labels.wmflabs.org>. We are moving the database to a different server. This work will take place from 14:00-14:30 UTC (7am Pacific) on Tuesday, March 19. WikiLabels may go down in the process. Please let me know if you have any questions. -- *James Hare* (he/him) Associate Product Manager Wikimedia Foundation <https://wikimediafoundation.org/>

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[Wikimedia Research Showcase] March 20 at 11:30 AM PST, 18:30 UTC
by Leila Zia 19 Mar '19

19 Mar '19

Hi all, The next Research Showcase, “Learning How to Correct a Knowledge Base from the Edit History” and “TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables” will be live-streamed this Wednesday, March 20, 2019, at 11:30 AM PST/18:30 UTC (Please note the change in time in UTC due to daylight saving changes in the U.S.). The first presentation is about using edit history to automatically correct constraint violations in Wikidata, and the second is about interlinking Wikipedia tables. YouTube stream: https://www.youtube.com/watch?v=6p62PMhkVNM As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases at https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase . This month's presentations: Learning How to Correct a Knowledge Basefrom the Edit History By Thomas Pellissier Tanon (Télécom ParisTech), Camille Bourgaux (DI ENS, CNRS, ENS, PSL Univ. & Inria), Fabian Suchanek (Télécom ParisTech), WWW'19. The curation of Wikidata (and other knowledge bases) is crucial to keep the data consistent, to fight vandalism and to correct good faith mistakes. However, manual curation of the data is costly. In this work, we propose to take advantage of the edit history of the knowledge base in order to learn how to correct constraint violations automatically. Our method is based on rule mining, and uses the edits that solved violations in the past to infer how to solve similar violations in the present. For example, our system is able to learn that the value of the [[d:Property:P21|sex or gender]] property [[d:Q467|woman]] should be replaced by [[d:Q6581072|female]]. We provide [https://tools.wmflabs.org/wikidata-game/distributed/#game=43 a Wikidata game] that suggests our corrections to the users in order to improve Wikidata. Both the evaluation of our method on past corrections, and the Wikidata game statistics show significant improvements over baselines. TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables By Besnik Fetahu Wikipedia tables represent an important resource, where information is organized w.r.t table schemas consisting of columns. In turn each column, may contain instance values that point to other Wikipedia articles or primitive values (e.g. numbers, strings etc.). In this work, we focus on the problem of interlinking Wikipedia tables for two types of table relations: equivalent and subPartOf. Through such relations, we can further harness semantically related information by accessing related tables or facts therein. Determining the relation type of a table pair is not trivial, as it is dependent on the schemas, the values therein, and the semantic overlap of the cell values in the corresponding tables. We propose TableNet, an approach that constructs a knowledge graph of interlinked tables with subPartOf and equivalent relations. TableNet consists of two main steps: (i) for any source table we provide an efficient algorithm to find all candidate related tables with high coverage, and (ii) a neural based approach, which takes into account the table schemas, and the corresponding table data, we determine with high accuracy the table relation for a table pair. We perform an extensive experimental evaluation on the entire Wikipedia with more than 3.2 million tables. We show that with more than 88% we retain relevant candidate tables pairs for alignment. Consequentially, with an accuracy of 90% we are able to align tables with subPartOf or equivalent relations. Comparisons with existing competitors show that TableNet has superior performance in terms of coverage and alignment accuracy. Best, Leila

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Sampling new editors in English Wikipedia
by Haifeng Zhang 19 Mar '19

19 Mar '19

Hi folks, My work needs to randomly sample new editors in each month, e.g., 100 editors per month. Do any of you have good suggestions for how to do this efficiently? I could think of using the dump files, but wonder are there other options? Thanks, Haifeng Zhang

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Wiki-research-l March 2019