A Library Perspective on Nearly-Unsupervised Information Extraction Workflows in Digital Libraries

Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and...

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Veröffentlicht in:	Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries S. 1 - 11
Hauptverfasser:	Kroll, Hermann, Pirklbauer, Jan, Plotzky, Florian, Balke, Wolf-Tilo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 20.06.2022
Schlagworte:	Cleaning Digital Libraries Encyclopedias Libraries Open Information Extraction Reliability engineering Scalability Semantics Training data Workflows
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Zusammenfassung:	Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and open extraction methods usually produce not-canonicalized extraction results. This paper tackles the question how digital libraries can handle such extractions and if their quality is sufficient in practice. We focus on unsupervised extraction workflows by analyzing them in case studies in the domains of encyclopedias (Wikipedia), pharmacy and political sciences. We report on opportunities and limitations. Finally we discuss best practices for unsupervised extraction workflows.CCS CONCEPTS* Information systems → Information extraction; Data extraction and integration; Document representation.
DOI:	10.1145/3529372.3530924