DynoClass: A Dynamic Table-Class Detection System Without the Need for Predefined Ontologies
Abstract
Table-class detection plays a crucial role in various data tasks. Traditional approaches typically depend on predefined ontologies such as DBpedia, but these are often insufficient for domain-specific or evolving datasets. In response, we present DynoClass, a novel table-class detection system that leverages the power of large language models (LLMs) and eliminates the reliance on external ontologies. DynoClass uses LLMs to generate table classes and descriptions directly from sample data and existing documentation, dynamically constructing hierarchical ontology classes. This approach matches the performance of traditional methods while eliminating the need for predefined ontologies.
Cite
Text
Wang et al. "DynoClass: A Dynamic Table-Class Detection System Without the Need for Predefined Ontologies." NeurIPS 2024 Workshops: TRL, 2024.Markdown
[Wang et al. "DynoClass: A Dynamic Table-Class Detection System Without the Need for Predefined Ontologies." NeurIPS 2024 Workshops: TRL, 2024.](https://mlanthology.org/neuripsw/2024/wang2024neuripsw-dynoclass/)BibTeX
@inproceedings{wang2024neuripsw-dynoclass,
title = {{DynoClass: A Dynamic Table-Class Detection System Without the Need for Predefined Ontologies}},
author = {Wang, Haonan and Wu, Eugene and Liu, Kechen and Liu, Jiaxiang},
booktitle = {NeurIPS 2024 Workshops: TRL},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/wang2024neuripsw-dynoclass/}
}