基于Web信息使用改进的无监督关系抽取方法构建交通本体

领域本体是对领域概念及其关系的一种高效合理的展现形式.在构建领域本体过程中,常常遇到的问题就是尽管本体概念完备但概念间关系复杂多样导致人工标记关系代价过高.使用无监督学习的关系抽取算法对包含丰富的领域概念的web信息进行抽取解决了这一问题.然而,传统的无监督学习的算法没有考虑到"单样例多概念对"的问题,导致最终抽取的概念关系不完整.本文利用交通领域的Web信息构建本体,将样例概念关系对权重引入传统的无监督学习方法Kmeans中,解决了此项问题并通过实验证明该算法取得了良好的效果.

Using Improved Unsupervised Relation Extraction Method to Construct Traffic Ontology Based on Web

Domain ontology is an efficient and reasonable display form of domain concepts and their relationships. In the process of building domain ontology, the problem often encountered is that ontology concept is complete but concept relations are complex and diverse and artificial tag cost too much. Using unsupervised Relation Extraction algorithm on rich Web information related with domain Ontology concepts solve previous problem. But the traditional method based on unsupervised learning does not take into account the situation of a single sample with more concepts, leading to the final incomplete results. We used Web information in trafffic field to construct ontology, introduced the weight of sample concept relation pair to a traditional unsupervised learning approach-Kmeans to solve this problem and achieved good results through experiments.