Conference item
Taxonomy completion via implicit concept insertion
- Abstract:
- High quality taxonomies play a critical role in various domains such as e-commerce, web search and ontology engineering. While there has been extensive work on expanding taxonomies from externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure within the taxonomy. In this work, we show the usefulness of this kind of enrichment, and explore its viability with a new taxonomy completion system ICON (Implicit CONcept Insertion). ICON generates new concepts by identifying implicit concepts based on the existing concept structure, generating names for such concepts and inserting them in appropriate positions within the taxonomy. ICON integrates techniques from entity retrieval, text summary, and subsumption prediction; this modular architecture offers high flexibility while achieving state-of-the-art performance. We have evaluated ICON on two e-commerce taxonomies, and the results show that it offers significant advantages over strong baselines including recent taxonomy completion models and the large language model, ChatGPT.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.0MB, Terms of use)
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- Publisher copy:
- 10.1145/3589334.3645584
Authors
- Publisher:
- Association for Computing Machinery
- Journal:
- Proceedings of the ACM on Web Conference 2024 More from this journal
- Pages:
- 2159–2169
- Publication date:
- 2024-05-13
- Acceptance date:
- 2024-01-23
- Event title:
- The Web Conference (WWW 2024)
- Event location:
- Singapore
- Event website:
- https://www2024.thewebconf.org/
- Event start date:
- 2024-05-13
- Event end date:
- 2024-05-17
- DOI:
- ISBN:
- 9798400701719
- Language:
-
English
- Pubs id:
-
1622186
- Local pid:
-
pubs:1622186
- Deposit date:
-
2024-02-21
- ARK identifier:
Terms of use
- Copyright holder:
- Shi et al.
- Copyright date:
- 2024
- Rights statement:
- © 2024 Owner/Author This work is licensed under a Creative Commons Attribution International 4.0 License.
- Notes:
- This paper will be presented at the The Web Conference (WWW 2024), 13th-17th May 2024, Singapore. This is the accepted manuscript version of the article. The final version will be available online from a forthcoming edition of the conference proceedings.
- Licence:
- CC Attribution (CC BY)
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