TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network (2001.09522v1)

Abstract: Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of <query concept, anchor concept> pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.

• The paper introduces a self-supervised approach that generates pseudo-training data from existing taxonomies to enhance expansion accuracy.
• It employs position-enhanced GNNs to capture local structural nuances, improving predictions for taxonomy growth.
• Extensive evaluations on diverse datasets demonstrate TaxoExpan's robustness and superiority over state-of-the-art taxonomy expansion methods.

An Overview of the TaxoExpan Framework for Self-supervised Taxonomy Expansion

The paper "TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network" presents a novel approach to dynamically expand existing taxonomies. Taxonomies are critical for various web applications, including product recommendation, query understanding, and web search enhancement. As web content grows, existing taxonomies risk becoming outdated. Taxonomy expansion is necessary to incorporate new and emerging concepts without disrupting the pre-existing hierarchical structure.

The proposed framework, TaxoExpan, leverages self-supervised learning to expand taxonomies effectively. The approach automatically generates training data from the existing taxonomy, eliminating reliance on manually labeled data, which is often expensive and impractical to obtain at scale. TaxoExpan uses position-enhanced Graph Neural Networks (GNNs) to capture local structural information around each candidate anchor concept within a taxonomy, thus improving the accuracy of expansion decisions.

Key Contributions

1. Self-supervised Learning Approach: TaxoExpan automatically generates pseudo-training data by viewing each concept in the existing taxonomy as a query and one of its parent concepts as an anchor. This results in positive and negative query-anchor pairs used for training, enabling effective model learning without human labeling.
2. Graph Neural Network Enhancement: The framework incorporates positional embeddings within GNNs to distinguish nodes' relative positions concerning the query concept. This enhancement significantly boosts prediction performance by capturing intricate local structures.
3. Noise-robust Training Objective: By adopting an InfoNCE loss for training, TaxoExpan groups pairs sharing the same query concept, which leads to improved model robustness against label noise in self-supervised data.
4. Extensive Experimental Evaluation: The approach is validated on three large datasets from diverse domains, including a subset of the Microsoft Academic Graph. TaxoExpan demonstrates superior performance in taxonomy expansion tasks compared to existing state-of-the-art methods.

Practical Implications

The implications of this research are significant for both academic and practical applications. Efficient taxonomy expansion can enhance search engines, recommendation systems, and content organization. By utilizing self-supervised machine learning techniques, organizations can save resources and improve the agility and accuracy of taxonomy updates, keeping pace with emerging concepts and terminology.

Speculative Future Directions

Looking forward, TaxoExpan's methodology could be applied to other relational and hierarchical domains beyond taxonomies, such as knowledge graphs and ontology development. Further research may focus on integrating feedback from downstream applications to refine expansion models, as well as exploring interactions between new concepts to develop more comprehensive and multi-faceted expansions. Moreover, automation in cleaning and refining existing taxonomies through methodologies such as TaxoExpan could reduce the need for manual curation while ensuring high-quality organizational structures.

TaxoExpan represents a significant advancement in self-supervised learning and taxonomy management, underlying the potential of keeping knowledge systems both current and comprehensive with minimal manual interference.