基于主动学习的本体概念关系判断

该文依据关系判断任务特点将主动学习应用到本体概念关系的辅助判断中,对边缘采样、熵采样、最不确信采样等主动学习查询生成策略进行了比较研究。在此基础上,从实际应用角度出发,讨论了在三种不同样本初始情况下主动学习技术的应用。对于初始样本正反例充足的情况,采用基于熵采样和边缘采样产生查询;对于初始样本仅有正例的情况,依据样本相似度主动的学习策略生成候选反例;对于缺乏初始样本的情况,使用概念在样本间距离等统计信息,同时生成候选正例和候选反例。从而,实现了在概念关系判定过程中对用户反馈信息的有效利用。

Ontology Conceptual Relation Judgment Based on Active Learning

According to the characteristics of relation judgment task, this paper applied active learning to the ontology conceptual relation judgment, making a comparative study for active learning query generation strategy, including margin sampling, entropy sampling, least confident sampling etc. From a practical point of view, we discussed the application of active learning techniques in three different samples of the initial case. For the initial sample of positive and negative sufficient condition, we used margin sampling and the entropy sampling to generate queries; for the initial sample only the positive cases, we generated candidate negative-sample according to the similarity active learning strategies; for lack of the initial sample, we used the concept of distance between the model and other statistical information to generated a candidate for positive-sample and the candidate negative-sample. Thus, we achieved the effective use of user feedback in the decision process of the conceptual relationship.
Key wordsontology; concept relation; assistant judgment; active learning