A PAM-based ontology concept and hierarchy learning method

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.

Microblog Hot Spot Mining Based on PAM Probabilistic Topic Model

“…As to the accuracy of document classification, we can refer to [11], it is shown as follows: When we randomly choose a subset of abstracts ( 1) from its large collection, where there are 4000 documents, 278438 word tokens and 25597 unique words. Based on PAM, the topic correlation can be seen in Figure 5.…”