A Comparison of Statistical and Data Mining Techniques for Enrichment Ontology with Instances

摘要：

　　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,however,two popular ones are the statistic and data mining methods.This paper compares the use of these two methods to classify instances to enrich ontology having greater domain knowledge.This paper selects conditional random field for the statistics method and feature-weight k-nearest neighbor classification for the data mining method.The experiments were conducted on the tourism ontology.The results showed that conditional random fields methods provided 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.

关键词： Ontology Enrichment Statistical Technique Classification Conditional Random Fields (CRFs) Feature-weighted k-Nearest Neighbor

作者: Aurawan Imsombut Jesada Kajornrit

作者单位: College of Innovative Technology and Engineering,Dhurakij Pundit University,Bangkok,Thailand

会议类型: 国际会议

会议名称: ICEFS2017(International Conference on Economics, Finance and Statistics 2017) (2017经济、金融与统计国际会议)

会议地点: 香港

会议语种:英文

页码: 408-413

在线出版日期: 2017-01-14（万方平台首次上网日期，不代表论文的发表时间）