Entity Linking with Out-of-Knowledge-Graph Entity Detection and Clustering Using Only Knowledge Graphs
Abstract: . Entity Linking is crucial for numerous downstream tasks, such as question answering, knowledge graph population, and general knowledge extraction. A frequently overlooked aspect of entity linking is the potential encounter with entities not yet present in a target knowledge graph. Although some recent studies have addressed this issue, they primarily utilize full-text knowledge bases or depend on external information. However, these resources are not available in most use cases. In this work, we solely rely on the information within a knowledge graph and assume no external information is accessible. To investigate the challenge of identifying and disambiguating entities absent from the knowledge graph, we introduce a comprehensive silver-standard benchmark dataset that covers texts from 1999 to 2022. Based on our novel dataset, we develop an approach using pre-trained language models and knowledge graph embeddings without the need for a parallel full-text corpus. Moreover, by assessing the influence of knowledge graph embeddings on the given task, we show that implementing a sequential entity linking approach, which considers the whole sentence, can outperform clustering techniques that handle each mention separately in specific instances.
Show BibTeX
@inproceedings{DBLP:conf/i-semantics/MollerU24,
author = {Cedric M{\"{o}}ller and
Ricardo Usbeck},
editor = {Angelo A. Salatino and
Mehwish Alam and
Femke Ongenae and
Sahar Vahdati and
Anna Lisa Gentile and
Tassilo Pellegrini and
Shufan Jiang},
title = {Entity Linking with Out-of-Knowledge-Graph Entity Detection and Clustering
Using Only Knowledge Graphs},
booktitle = {Knowledge Graphs in the Age of Language Models and Neuro-Symbolic
{AI} - Proceedings of the 20th International Conference on Semantic
Systems, 17-19 September 2024, Amsterdam, The Netherlands},
series = {Studies on the Semantic Web},
volume = {60},
pages = {88--105},
publisher = {{IOS} Press},
year = {2024},
url = {https://doi.org/10.3233/SSW240009},
doi = {10.3233/SSW240009},
timestamp = {Wed, 05 Nov 2025 16:09:05 +0100},
biburl = {https://dblp.org/rec/conf/i-semantics/MollerU24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}