---
title: "A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text"
authors:
  - "Andrea Seveso"
  - "Fabio Mercorio"
  - "Mario Mezzanzanica"
doi: "10.24963/ijcai.2021/690"
url: "https://ijcai.org/proceedings/2021/690"
pdf: "https://ijcai.org/proceedings/2021/0690.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4917-4918"
track: "Doctoral Consortium"
keywords:
  - "Data Mining: Recommender Systems"
  - "Knowledge Representation and Reasoning: Semantic Web"
  - "Natural Language Processing: Knowledge Extraction"
  - "Humans and AI: Human-AI Collaboration"
---

# A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

Andrea Seveso, Fabio Mercorio, Mario Mezzanzanica

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4917-4918. DOI: [10.24963/ijcai.2021/690](https://doi.org/10.24963/ijcai.2021/690)

## Abstract

Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.