一种改进的面向文本的领域概念筛选算法

Improved Text-oriented Algorithm for the Domain-specific Concept Sieving

在语义技术及其应用中,本体学习是一个研究热点,而领域概念筛选则是本体学习的基础。对于领域概念筛选问题,领域一致度与领域相关度相结合的方法效果较好,却也存在信息描述不全的缺点,因此提出了一种针对此问题的改进的领域概念筛选算法。通过计算候选概念之间的语义相似度,识别出低频的具有同义关系和整体-部分关系的词语集,过滤掉部分冗余概念,然后采用改进的领域相关度和领域一致度相结合的公式进行筛选。实验表明,该方法提高了领域概念筛选的有效性。

Ontology learning is a hot research field in semantic technology and its application,and the domain-specific concept sieving is the foundation of ontology learning.Although the method based on domain relevance expressions and domain consensus expressions displays the good effectiveness for the domain-specific concept sieving,it exists the faults of unilateral description information.So this paper presented an improved sieving algorithm of the domain-specific concept to solve the above problems.First,low frequency with synonymy and the part-of relationship words set were identified and redundant concepts were filtered out through calculating the semantic similarity between candidate concept,and then using the improved field concept similarity and domain concepts consistent degree formula sieve concepts.The experiments show that this method improves the effectiveness of the domain-specific concept sieving.