Year: 2025
Type: conference-paper
Abstract: Information Extraction tasks such as Named Entity Recognition and Relation Extraction are often developed using diverse tagsets and annotation guidelines.This presents major challenges for model generalization, cross-dataset evaluation, tool interoperability, and broader industry adoption.To address these issues, we propose an information extraction ontology, WojoodOntology, which covers a wide range of named entity types and relations.WojoodOntology serves as a semantic mediation framework that facilitates alignment across heterogeneous tagsets and annotation guidelines.We propose two ontology-based mapping methods: (i) as a set of mapping rules for uni-directional tagset alignment; and (ii) as ontology-based prompting, which incorporates the ontology concepts directly into prompts, enabling large language models (LLMs) to perform more effective and bi-directional mappings.Our experiments show a 15% improvement in out-of-domain mapping accuracy when using ontology-based prompting compared to rule-based methods.Furthermore, WojoodOntology is aligned with Schema.org and Wikidata, enabling interoperability with knowledge graphs and facilitating broader industry adoption.The Wo-joodOntology is open source and available at https://sina.birzeit.edu/wojood.
Institutions: Birzeit University, Hamad bin Khalifa University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037660