Ontology Enrichment for Effective Fine-grained Entity Typing

Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly given the massive scale of data. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose øurs, where we (1) enrich each node in the ontology structure with two categories of extra information:instance information for training sample augmentation andtopic information to relate types with contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that øurs achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods. øurs also enjoys strong transferability to unseen and finer-grained types. We will open source this work upon acceptance.

Original languageEnglish (US)
Title of host publicationKDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2318-2327
Number of pages10
ISBN (Electronic)9798400704901
DOIs
StatePublished - Aug 25 2024
Event30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain
Duration: Aug 25 2024Aug 29 2024

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)2154-817X

Conference

Conference30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Country/TerritorySpain
CityBarcelona
Period8/25/248/29/24

Keywords

  • fine-grained entity typing
  • language models
  • natural language inference
  • zero-shot learning

ASJC Scopus subject areas

  • Software
  • Information Systems

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