Knowledge Graph Population with Out-of-KG Entities

Cedric, Möller,

doi:10.1007/978-3-031-11609-4_35

Cited by 1 publication

(2 citation statements)

References 42 publications

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“…One important contribution of our work is to provide general strategies to construct out-of-KB EL datasets. The research endeavour that mostly resembles ours is the ongoing work of Möller (2022) on EL for KB enrichment. This study so far targets news events to enrich Wikipedia.…”

Section: Out-of-kb Mention and Entity Discoverymentioning

confidence: 99%

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Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking

Dong¹,

Chen²,

He³

et al. 2023

Preprint

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Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical role in KB maintenance, but has not yet been fully explored. The current methods are mostly limited to the simple thresholdbased approach and feature-based classification; the datasets for evaluation are relatively rare. In this work, we propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have a corresponding KB entity by matching them to a special NIL entity. To this end, we integrate novel techniques including NIL representation, NIL classification, and synonym enhancement. We also propose Ontology Pruning and Versioning strategies to construct out-of-KB mentions from normal, in-KB EL datasets. Results on four datasets of clinical notes and publications show that BLINKout outperforms existing methods to detect out-of-KB mentions for medical ontologies UMLS and SNOMED CT.

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Section: Out-of-kb Mention and Entity Discoverymentioning

confidence: 99%

“…This study so far targets news events to enrich Wikipedia. Similar to the plans in Möller (2022), our future work will canonicalise the new mentions by e.g., grouping and naming, and place them in the KB.…”

Section: Out-of-kb Mention and Entity Discoverymentioning

confidence: 99%