TY - CHAP

T1 - Entity Linking with Out-of-Knowledge-Graph Entity Detection and Clustering Using Only Knowledge Graphs

AU - Möller, Cedric

AU - Usbeck, Ricardo

N1 - Conference code: 20

PY - 2024

Y1 - 2024

N2 - 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 such as crawled webpages. Full-text knowledge bases are not available in all domains and using external information is connected to increased effort. 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.

AB - 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 such as crawled webpages. Full-text knowledge bases are not available in all domains and using external information is connected to increased effort. 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.

KW - Business informatics

KW - Entity Linking

KW - Entity Disambiguation

KW - Out-of-KG Entities

U2 - 10.3233/SSW240009

DO - 10.3233/SSW240009

M3 - Article in conference proceedings

T3 - Studies on the Semantic Web

SP - 88

EP - 105

BT - Knowledge Graphs in the Age of Language Models and Neuro-Symbolic AI-

A2 - Salatino, Angelo A.

A2 - Alam, Mehwish

A2 - Ongenae, Femke

A2 - Vahdati, Sahar

A2 - Gentile, Anna Lisa

A2 - Pellegrini, Tassilo

A2 - Jiang, Shufan

PB - IOS Press BV

CY - Amsterdam

T2 - 20th International Conference on Semantic Systems

Y2 - 17 September 2024 through 19 September 2024

ER -