Paper Info
Title
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion.
Abstract
Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the is-a relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus, and allow users to input a seed taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.
Year
DOI
Venue
2018
10.1145/3219819.3220115
KDD
Keywords
Field
DocType
Taxonomy Construction,Hierarchical Tree Expansion,Set Expansion,Weakly-supervised Relation Extraction
Global structure,Automatic taxonomy induction,Computer science,Taxonomy (biology),Set expansion,Artificial intelligence,Machine learning,Recursion,Relationship extraction
Conference
ISBN
Citations 
PageRank 
978-1-4503-5552-0
6
0.50
References 
Authors
41
8
Name
Order
Citations
PageRank
Jiaming Shen1233.21
Zeqiu Wu2594.11
Dongming Lei3382.36
Chao Zhang4939103.66
Xiang Ren588560.08
Michelle Vanni65110.16
Brian M. Sadler73179286.72
Jiawei Han8430853824.48