research-article

QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations

Authors:

Bang LiuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1008 - 1017

https://doi.org/10.1145/3485447.3511943

Published: 25 April 2022 Publication History

Abstract

Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1

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Cited By

Shi JDong HChen JWu ZHorrocks IChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Taxonomy Completion via Implicit Concept InsertionProceedings of the ACM Web Conference 202410.1145/3589334.3645584(2159-2169)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645584
Dong HChen JHe YGao YHorrocks I(2024)A Language Model Based Framework for New Concept Placement in OntologiesThe Semantic Web10.1007/978-3-031-60626-7_5(79-99)Online publication date: 26-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-60626-7_5
Dong HChen JHe YHorrocks IFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and PlacementProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615126(5316-5320)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615126
Show More Cited By

Index Terms

QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
  2. Information systems applications

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

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Shi JDong HChen JWu ZHorrocks IChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Taxonomy Completion via Implicit Concept InsertionProceedings of the ACM Web Conference 202410.1145/3589334.3645584(2159-2169)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645584
Dong HChen JHe YGao YHorrocks I(2024)A Language Model Based Framework for New Concept Placement in OntologiesThe Semantic Web10.1007/978-3-031-60626-7_5(79-99)Online publication date: 26-May-2024
https://dl.acm.org/doi/10.1007/978-3-031-60626-7_5
Dong HChen JHe YHorrocks IFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and PlacementProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615126(5316-5320)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615126
Jiang SYao QWang QSun Y(2023)A Single Vector Is Not Enough: Taxonomy Expansion via Box EmbeddingsProceedings of the ACM Web Conference 202310.1145/3543507.3583310(2467-2476)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583310

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