Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances

Abstract

Enriching instances into an ontology is an important task because the process extends knowledge in ontology to cover more extensively the domain of interest, so that greater benefits can be obtained. There are many techniques to classify instances of concepts with two popular techniques being the statistical and data mining methods. The paper compares the use of the two methods to classify instances to enrich ontology having greater domain knowledge, and selects a conditional random field for the statistical method and feature-weight k-nearest neighbor classification for the data mining method. The experiments are conducted on tourism ontology. The results show that conditional random fields methods provide greater precision and recall value than the other, specifically, F1-measure is 74.09% for conditional random fields and 60.04% for feature-weight k-nearest neighbor classification.

An Approach for Automatic Ontology Enrichment from Texts

“…In literature, different works of term extraction from textual corpus use two main approaches: statistic analysis and linguistic analysis approaches [17], [27], [28], [29]. The first one bases on statistic techniques of measures to facilitate the detection of new concepts and relations between them.…”