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In tasks like question answering or text summarisation, it is essential tohave background knowledge about the relevant entities. The information aboutentities - in particular, about long-tail or emerging entities - in publiclyavailable knowledge graphs like DBpedia or CaLiGraph is far from complete. Inthis paper, we present an approach that exploits the semi-structured nature oflistings (like enumerations and tables) to identify the main entities of thelisting items (i.e., of entries and rows). These entities, which we callsubject entities, can be used to increase the coverage of knowledge graphs. Ourapproach uses a transformer network to identify subject entities at thetoken-level and surpasses an existing approach in terms of performance whilebeing bound by fewer limitations. Due to a flexible input format, it isapplicable to any kind of listing and is, unlike prior work, not dependent onentity boundaries as input. We demonstrate our approach by applying it to thecomplete Wikipedia corpus and extracting 40 million mentions of subjectentities with an estimated precision of 71% and recall of 77%. The results areincorporated in the most recent version of CaLiGraph.

Transformer-based Subject Entity Detection in Wikipedia Listings