A PAM-based ontology concept and hierarchy learning method

Authors:
Miaomiao Yu;Junli Wang;Xiaodong Zhao
Affiliations:
;;
Venue:
Journal of Information Science
Year:
2014

Citing 11
Cited 0

Latent dirichlet allocation

The Journal of Machine Learning Research
Pachinko allocation: DAG-structured mixture models of topic correlations

ICML '06 Proceedings of the 23rd international conference on Machine learning
Evaluating WordNet-based Measures of Lexical Semantic Relatedness

Computational Linguistics
Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora

WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Ontology Construction Based on Latent Topic Extraction in a Digital Library

ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Probabilistic topic models

Communications of the ACM
Capturing correlations of multiple labels: A generative probabilistic model for multi-label learning

Neurocomputing
Topic models for taxonomies

Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Ontology learning from text: A look back and into the future

ACM Computing Surveys (CSUR)
Image indexing and retrieval with Pachinko allocation model: application on local and global features

PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
LDA-based online topic detection using tensor factorization

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

As a shared conceptual model that can express knowledge and a modelling tool that can describe conceptual model in the semantic and knowledge level, ontology plays an important role in the related fields of the Semantic Web, natural language processing and information retrieval. Ontology learning is a series of methods and technologies to construct ontology automatically or semi-automatically. Concept and hierarchy learning are the most important parts of the ontology construction. This paper proposes an ontology concept and hierarchy learning method based on the Pachinko Allocation Model. The above problem is transformed into a probability and statistical inference problem by building an ontology concept learning model. Gibbs sampling is used to estimate the parameters. Then, using the ontology concept generation algorithm based on WordNet, an abstract description of the ontology concept is obtained. Experimental results on the standard test dataset show that the proposed method can offer an effective solution to ontology concept and hierarchy learning.