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Paper Number
1646
Paper Type
Completed
Description
Wikis are ubiquitous in organisational and private use and provide a wealth of textual data. Maintaining the currency of this textual data is important and difficult, requiring large manual efforts. Previous approaches from literature provide valuable contributions for assessing the currency of structured data or whole wiki articles but are unsuitable for textual wiki data like single sentences. Thus, we propose a novel approach supporting the assessment and improvement of the currency of textual wiki data in an automated manner. Grounded on a theoretical model, our approach makes use of data retrieved from recently published news articles and a language model to determine the currency of fact-based wiki sentences and suggest possible updates. Our evaluation conducted on 543 sentences from six wiki domains shows that the approach yields promising results with accuracies over 80% and thus is well-suited to support assessment and improvement of the currency of textual wiki data.
Recommended Citation
Heinrich, Bernd; Huber, Maximilian Felix; Krapf, Thomas; and Schiller, Alexander, "The Currency of Wiki Articles – A Language Model-based Approach" (2023). ICIS 2023 Proceedings. 14.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/14
The Currency of Wiki Articles – A Language Model-based Approach
Wikis are ubiquitous in organisational and private use and provide a wealth of textual data. Maintaining the currency of this textual data is important and difficult, requiring large manual efforts. Previous approaches from literature provide valuable contributions for assessing the currency of structured data or whole wiki articles but are unsuitable for textual wiki data like single sentences. Thus, we propose a novel approach supporting the assessment and improvement of the currency of textual wiki data in an automated manner. Grounded on a theoretical model, our approach makes use of data retrieved from recently published news articles and a language model to determine the currency of fact-based wiki sentences and suggest possible updates. Our evaluation conducted on 543 sentences from six wiki domains shows that the approach yields promising results with accuracies over 80% and thus is well-suited to support assessment and improvement of the currency of textual wiki data.
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13-DataAnalytics