Title: What We Talk About When We Talk About Wikidata Quality: A Literature Survey

Authors: Alessandro Piscopo (University of Southampton), Elena Simperl (University of Southampton)

Abstract: Launched in 2012, Wikidata has already become a success story. It is a collaborative knowledge graph, whose large community has produced so far data about more than 55 million entities. Understanding the quality of the data in Wikidata is key to its widespread adoption and future development. No study has investigated so far to what extent and which aspects of this topic have been addressed. To fill this gap, we surveyed prior literature about data quality in Wikidata. Our analysis includes 28 papers and categorise by quality dimensions addressed. We showed that a number of quality dimensions has not been yet adequately covered, e.g. accuracy and trustworthiness. Future work should focus on these.

Download: This contribution is part of the OpenSym 2019 proceedings and is available as a PDF file.

Title: When Humans and Machines Collaborate: Cross-lingual Label Editing in Wikidata

Authors: Lucie-Aimée Kaffee (University of Southampton and TIB Leibniz Information Centre for Science and Technology), Kemele M Endris (TIB Leibniz Information Centre for Science and Technology), Elena Simperl (University of Southampton)

Abstract: The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data.

Download: This contribution is part of the OpenSym 2019 proceedings and is available as a PDF file.

Title: Approving Automation: Analyzing Requests for Permissions of Bots in Wikidata

Authors: Mariam Farda-Sarbas (Freie Universitat Berlin), Hong Zhu (Freie Universität Berlin), Marisa Nest (Freie Universität Berlin), Claudia Muller-Birn (Freie Universität Berlin)

Abstract: Wikidata, initially developed to serve as a central structured knowledge base for Wikipedia, is now a melting point for structured data for companies, research projects and other peer production communities. Wikidata’s community consists of humans and bots, and most edits in Wikidata come from these bots. Prior research has raised concerns regarding the challenges for editors to ensure the quality of bot-generated data, such as the lack of quality control and knowledge diversity. In this research work, we provide one way of tackling these challenges by taking a closer look at the approval process of bot activity on Wikidata. We collected all bot requests, i.e. requests for permissions (RfP) from October 2012 to July 2018. We analyzed these 683 bot requests by classifying them regarding activity focus, activity type, and source mentioned. Our results show that the majority of task requests deal with data additions to Wikidata from internal sources, especially from Wikipedia. However, we can also show the existing diversity of external sources used so far. Furthermore, we examined the reasons which caused the unsuccessful closing of RfPs. In some cases, the Wikidata community is reluctant to implement specific bots, even if they are urgently needed because there is still no agreement in the community regarding the technical implementation. This study can serve as a foundation for studies that connect the approved tasks with the editing behavior of bots on Wikidata to understand the role of bots better for quality control and knowledge diversity.

Download: This contribution is part of the OpenSym 2019 proceedings and is available as a PDF file.

Title: Dwelling on Wikipedia: Investigating Time Spent by Global Encyclopedia Readers

Authors: Nathan TeBlunthuis (Wikimedia Foundation), Tilman Bayer (Wikimedia Foundation), Olga Vasileva (Wikimedia Foundation)

Abstract: Much existing knowledge about global consumption of peer-produced information goods is supported by data on Wikipedia page view counts and surveys. In 2017, the Wikimedia Foundation began measuring the time readers spend on a given page view (dwell time), enabling a more detailed understanding of such reading patterns. In this paper, we validate and model this new data source and, building on existing findings, use regression analysis to test hypotheses about how patterns in reading time vary between global contexts. Consistent with prior findings from self-report data, our complementary analysis of behavioral data provides evidence that Global South readers are more likely to use Wikipedia to gain in-depth understanding of a topic. We find that Global South readers spend more time per page view and that this difference is amplified on desktop devices, which are thought to be better suited for in-depth information seeking tasks.

Download: This contribution is part of the OpenSym 2019 proceedings and is available as a PDF file.