Graph Neural Network Taxonomy Construction via Cross-Domain Transfer
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Solution Overview
Problem
Existing solutions face challenges in efficiently constructing taxonomies for unseen domains due to the absence or incompleteness of domain-specific taxonomies, and existing methods for natural language processing struggle with accurately identifying and understanding changes in structures, leading to inaccurate or incomplete taxonomies.
Innovation Solution
A system utilizing a graph-based cross-domain knowledge transfer framework, which extracts hyponym-hypernym term pairs from a corpus, constructs a cross-domain directed acyclic graph, and employs a graph neural network to train a machine learning model for taxonomy structure building, applying semantic clustering aggregation and selective filtering to generate a predicted taxonomy for an unseen domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing NLP methods are used to construct taxonomies for unseen domains, then the process can be automated, but the accuracy and completeness of the taxonomy generation deteriorates due to inability to accurately identify and understand structural changes
Solution Approach 1:
The patent introduces a cross-domain knowledge graph as an intermediary structure that mediates between source domain taxonomies and target domain terminology. The knowledge graph serves as a bridge, allowing latent features from source domains to be transferred and adapted to unseen target domains, thereby maintaining high accuracy in taxonomy generation without requiring domain-specific manual annotation.
Solution Approach 2:
The system changes the parameter space from direct NLP processing to graph-based latent feature extraction. By transforming terminology into a knowledge graph structure with rich relational parameters and then using graph neural networks to extract latent features, the system achieves superior accuracy in capturing semantic relationships and structural changes across domains.
2Ease of manufacture
If domain-specific taxonomies are used as input, then taxonomy construction is straightforward, but the system becomes inapplicable to unseen domains where such taxonomies are absent or incomplete
Solution Approach 1:
The patent creates a universal taxonomy construction system that can handle both seen and unseen domains through a single framework. The cross-domain knowledge graph and graph neural network components serve multiple functions: they process source domain taxonomies, extract transferable latent features, and generate target domain taxonomies, making the system universally applicable across diverse domains without requiring domain-specific customization.
Solution Approach 2:
The system performs preliminary extraction of latent features from source domain taxonomies before applying them to target domains. This preliminary action of learning from existing domains and storing knowledge in the graph structure enables the system to handle unseen domains effectively, as the latent features are pre-computed and can be quickly adapted to new domains without requiring retraining.
3Adaptability or versatility
If cross-domain knowledge transfer is applied, then taxonomy generation for unseen domains becomes possible, but the complexity of the system increases due to multiple processing stages
Solution Approach 1:
The patent replaces traditional mechanical NLP processing mechanisms with graph-based neural network mechanisms. Instead of using conventional text processing pipelines, the system uses graph neural networks to perform latent feature extraction and taxonomy generation, which simplifies the overall system architecture while enabling cross-domain applicability. The graph-based approach integrates multiple processing stages into a unified neural network framework.
Data Source
AI summary
A system, computer program product, and method are provided for employing a graph neural network (GNN) to construct a taxonomy. The GNN is subject to a training cycle and an inference cycle. The training cycle encodes cross-domain terms pairs from a set of noisy cross domain pairs extracted from a corpora, and outputs a preliminary taxonomy. The inference cycle identifies candidate term pairs and selectively subjects the candidate term pairs to selective filtering to produce a system predicted taxonomy from the preliminary taxonomy.