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Title: When humans and machines collaborate: Cross-lingual Label Editing in Wikidata
Authors: Kaffee, L.-A.Endris, K.M.Simperl, E.
Publishers Version: https://doi.org/10.1145/3306446.3340826
Issue Date: 2019
Published in: Proceedings of the 15th International Symposium on Open Collaboration 2019
Publisher: New York City : Association for Computing Machinery
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.
Keywords: Collaborative Knowledge Graph; Multilingual Data; Wikidata; Automation; Botnet; Distributed computer systems; Extraction; Automated extraction; Collaborative Editing; Collaborative knowledge; Hybrid approach; Knowledge graphs; Multilingual Data; Natural languages; Wikidata; Data handling
DDC: 004
License: CC BY 4.0 Unported
Link to License: https://creativecommons.org/licenses/by/4.0/
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