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When humans and machines collaborate: cross-lingual label editing in Wikidata

When humans and machines collaborate: cross-lingual label editing in Wikidata
When humans and machines collaborate: cross-lingual label editing in Wikidata
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.
Wikidata, Multilinguality, Community
ACM
Kaffee, Lucie-Aimée
8975c12f-9033-47ed-a2eb-b674b707c2ac
Endris, Kemele M.
c75f39b9-262a-482c-9ec5-1ba96b19bb2d
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Kaffee, Lucie-Aimée
8975c12f-9033-47ed-a2eb-b674b707c2ac
Endris, Kemele M.
c75f39b9-262a-482c-9ec5-1ba96b19bb2d
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Kaffee, Lucie-Aimée, Endris, Kemele M. and Simperl, Elena (2019) When humans and machines collaborate: cross-lingual label editing in Wikidata. In OpenSym '19 Proceedings of the 15th International Symposium on Open Collaboration. ACM. 9 pp .

Record type: Conference or Workshop Item (Paper)

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.

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Cross-lingual Label Editing in Wikidata - Version of Record
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More information

Published date: 20 August 2019
Venue - Dates: 15th International Symposium on Open Collaboration, Skövde, Sweden, 2019-08-20 - 2019-08-22
Keywords: Wikidata, Multilinguality, Community

Identifiers

Local EPrints ID: 433768
URI: https://eprints.soton.ac.uk/id/eprint/433768
PURE UUID: 4cec75d2-6e9d-4997-82e9-d28de593b6ce
ORCID for Lucie-Aimée Kaffee: ORCID iD orcid.org/0000-0002-1514-8505
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 03 Sep 2019 16:30
Last modified: 25 Oct 2019 00:32

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