cross-lingual label editing in wikidata
| Autor: | Kemele M. Endris, Elena Simperl, Lucie-Aimée Kaffee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Process (engineering)
Group method of data handling Computer science media_common.quotation_subject Maintainability Botnet Extraction Community 02 engineering and technology Automated extraction Natural languages World Wide Web Collaborative knowledge Collaborative editing Automation 020204 information systems Knowledge graphs 0202 electrical engineering electronic engineering information engineering Collaborative Knowledge Graph 0501 psychology and cognitive sciences Quality (business) Multilingual Data Konferenzschrift media_common business.industry 05 social sciences Collaborative Editing Data handling Hybrid approach Dewey Decimal Classification::600 | Technik Distributed computer systems Wikidata Multilinguality business ddc:600 Natural language 050104 developmental & child psychology |
| Zdroj: | Proceedings of the 15th International Symposium on Open Collaboration-OpenSym '19 Proceedings of the 15th International Symposium on Open Collaboration OpenSym Kaffee, L-A, Endris, K M & Simperl, E 2019, When humans and machines collaborate : cross-lingual label editing in Wikidata . in OpenSym '19 Proceedings of the 15th International Symposium on Open Collaboration . pp. 1–9 . https://doi.org/10.1145/3306446.3340826 |
| DOI: | 10.1145/3306446.3340826 |
| Popis: | 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. © 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-6319-8/19/08. |
| Databáze: | OpenAIRE |
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