Domain-Specific Hyponym Relations

Item Name: Domain-Specific Hyponym Relations
Author(s): Jun Liu, Bifan Wei, Jian Ma, Chenchen Wang
LDC Catalog No.: LDC2014T07
ISBN: 1-58563-673-8
ISLRN: 382-492-972-333-8
Release Date: April 14, 2014
Member Year(s): 2014
DCMI Type(s): Text
Data Source(s): web collection
Application(s): relation extraction, information extraction
Language(s): English
Language ID(s): eng
License(s): Creative Commons Attribution-NonCommercial-ShareAlike 3.0 (NFP, Non-Member)
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 (FP)
Online Documentation: LDC2014T07 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Liu, Jun, et al. Domain-Specific Hyponym Relations LDC2014T07. Web Download. Philadelphia: Linguistic Data Consortium, 2014.

Introduction

Domain-Specific Hyponym Relations was developed by the Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology at Xian Jiaotung University, Xian, Shaanxi, China. It provides more than 5,000 English hyponym relations in five domains including data mining, computer networks, data structures, Euclidean geometry and microbiology. All hypernym and hyponym words were taken from Wikipedia article titles.

A hyponym relation is a word sense relation that is an IS-A relation. For example, dog is a hyponym of animal and binary tree is a hyponym of tree structure. Among the applications for domain-specific hyponym relations are taxonomy and ontology learning, query result organization in a faceted search and knowledge organization and automated reasoning in knowledge-rich applications.

Data

The data is presented in XML format, and each file provides hyponym relations in one domain. Within each file, the term, Wikipedia URL, hyponym relation and the names of the hyponym and hypernym words are included. The distribution of terms and relations is set forth in the table below:

Dataset Terms Hyponym Relations
Data Mining 278 364
Computer Network 336 399
Data Structure 315 578
Euclidean Geometry 455 690
Microbiology 1,028 3,533

Samples

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