# GIANT: Scalable Creation of a Web-scale Ontology

arXiv: https://arxiv.org/abs/2004.02118 · HTML: https://mlsanity.com/paper/2004.02118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion](https://mlsanity.com/paper/2101.11268/) (2021)
- [CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts](https://mlsanity.com/paper/2408.09070/) (2024)
- [Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision](https://mlsanity.com/paper/2203.14921/) (2022)
- [TAG: Toward Accurate Social Media Content Tagging with a Concept Graph](https://mlsanity.com/paper/2110.06892/) (2021)
- [GANTEE: Generative Adversatial Network for Taxonomy Entering Evaluation](https://mlsanity.com/paper/2303.14480/) (2023)

## Abstract (arXiv)

Understanding what online users may pay attention to is key to content recommendation and search services. These services will benefit from a highly structured and web-scale ontology of entities, concepts, events, topics and categories. While existing knowledge bases and taxonomies embody a large volume of entities and categories, we argue that they fail to discover properly grained concepts, events and topics in the language style of online population. Neither is a logically structured ontology maintained among these notions. In this paper, we present GIANT, a mechanism to construct a user-centered, web-scale, structured ontology, containing a large number of natural language phrases conforming to user attentions at various granularities, mined from a vast volume of web documents and search click graphs. Various types of edges are also constructed to maintain a hierarchy in the ontology. We present our graph-neural-network-based techniques used in GIANT, and evaluate the proposed methods as compared to a variety of baselines. GIANT has produced the Attention Ontology, which has been deployed in various Tencent applications involving over a billion users. Online A/B testing performed on Tencent QQ Browser shows that Attention Ontology can significantly improve click-through rates in news recommendation.