research-article

Robust named entity disambiguation with random walks

Editor: Harith Alani Authors: Zhaochen Guo, Denilson BarbosaAuthors Info & Claims

Semantic Web, Volume 9, Issue 4

Pages 459 - 479

https://doi.org/10.3233/SW-170273

Published: 01 January 2018 Publication History

Abstract

Named Entity Disambiguation is the task of assigning entities from a Knowledge Graph (KG) to mentions of such entities in a textual document. The state-of-the-art for this task balances two disparate sources of similarity: lexical, defined as the pairwise similarity between mentions in the text and names of entities in the KG; and semantic, defined through some graph-theoretic property of a subgraph of the KG induced by the choice of entities for each mention. Departing from previous work, our notion of semantic similarity is rooted in Information Theory and is defined as the mutual information between random walks on the disambiguation graph induced by choice of entities for each mention. We describe an iterative algorithm based on this idea, and show an extension that uses learning-to-rank, which yields further improvements. Our experimental evaluation demonstrates that this approach is robust and very competitive on well-known existing benchmarks. We also justify the need for new and more difficult benchmarks, and provide an extensive experimental comparison of our method and previous work on these new benchmarks.

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Cited By

Li XDou ZZhou YLiu FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive TasksProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657778(26-37)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657778
Jiang JChang WZhang JHsieh CYu HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Entity Disambiguation with Extreme Multi-label RankingProceedings of the ACM Web Conference 202410.1145/3589334.3645498(4172-4180)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645498
Nafa YChen QHou BLi Z(2024)Adaptive deep learning for entity disambiguation via knowledge-based risk analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122342238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122342
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Index Terms

Robust named entity disambiguation with random walks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image Semantic Web

Semantic Web Volume 9, Issue 4

2018

157 pages

ISSN:1570-0844

EISSN:2210-4968

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Li XDou ZZhou YLiu FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive TasksProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657778(26-37)Online publication date: 10-Jul-2024
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Jiang JChang WZhang JHsieh CYu HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Entity Disambiguation with Extreme Multi-label RankingProceedings of the ACM Web Conference 202410.1145/3589334.3645498(4172-4180)Online publication date: 13-May-2024
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Guellil IGarcia-Dominguez ALewis PHussain SSmith G(2024)Entity linking for English and other languages: a surveyKnowledge and Information Systems10.1007/s10115-023-02059-266:7(3773-3824)Online publication date: 1-Jul-2024
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