Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

Abstract

Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. However, one critical and fundamental challenge in fixing the incompleteness of taxonomies is the incompleteness of the extracted concepts, especially for those whose names have multiple words and consequently low frequency in the corpus. To resolve the limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy completion by identifying positions in existing taxonomies that need new concepts and then generating appropriate concept names. Instead of relying on the corpus for concept embeddings, GenTaxo learns the contextual embeddings from their surrounding graph-based and language-based relational information, and leverages the corpus for pre-training a concept name generator. Experimental results demonstrate that GenTaxo improves the completeness of taxonomies over existing methods.

References in corpus (8)

• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• A Survey of Knowledge-Enhanced Text Generation
• TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
• STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
• Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
• Taxonomy Completion via Triplet Matching Network
• Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention

Cited by in corpus (6)

• A Survey of Knowledge-Enhanced Text Generation
• TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations
• TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters
• Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
• Generating and Visualizing Trace Link Explanations
• Towards Visual Taxonomy Expansion