Web表格中本体实例自动获取方法

当前许多领域信息都采用表格形式展现,因此,如何从表格中抽取本体逐渐引起了人们的关注.为了提高从Web表格中抽取本体实例的准确性,提出了基于语义相似度的词汇语义类的获取方法.该方法采用了基于SVM的语义相似度计算方法,提高了判断语义相似度的准确性,克服了以前依靠句法相似度分析表格结构存在的局限性.最后,根据实验结果,对该方法的性能进行评估.实验结果显示,该方法可以有效地从Web表格中抽取本体实例.

Automatic Acquisition Method of Ontology Instances from Web Tables

Information is mainly represented in tabular form in the modern society and scientific filed,so more and more attentions have been paid on how to extract ontology instances from the Web tables.In order to improve the accuracy of extracting ontology instances from the Web tables,an acquisition method of semantic class of words was proposed on the basis of semantic similarity.The method is based on SVM semantic similarity calculation method,which improves the accuracy of checking the semantic similarity,and overcomes the limitations of the previous analysis method of table structure by using syntactic similarity.Finally,according to the experimental results,the performance of this method is evaluated.Experimental results show that this method can effectively extract the ontology instances from Web tables.