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...

Full description

Saved in:

Bibliographic Details
Published in:	Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries pp. 1 - 11
Main Authors:	Kroll, Hermann, Pirklbauer, Jan, Plotzky, Florian, Balke, Wolf-Tilo
Format:	Conference Proceeding
Language:	English
Published:	ACM 20.06.2022
Subjects:	Cleaning Digital Libraries Encyclopedias Libraries Open Information Extraction Reliability engineering Scalability Semantics Training data Workflows
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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