基于领域本体的文档向量空间模型构建

针对VSM不能揭示隐藏在不同特征词后面的相同概念语义、反映文档中的潜在语义关系、在相似度计算中精度较低的问题,提出一种基于领域本体的文档向量空间模型DOBVSM(domain ontology-based vector spacemodel)。该模型把领域本体中的概念扩展为文档特征词,并通过概念间的语义关系对特征词权重进行调整,最终建立包含语义关系的文档DOBVSM。通过实验分析表明:DOBVSM计算的文档相似度值更加发散,与专家评价值最为接近,能够较好地反映文档之间的相似情况。

Document vector space model construction based on domain ontology

As VSM couldn't reveal the same conceptual semantics hidden in different feature words and the latent semantic relations in the documents and had a low accuracy in document similarity calculation, this paper presented DOBVSM. This model added the conceptions in domain ontology to the feature word set, and then adjusted the weight of feature words using the relation between conceptions, constructed the document DOBVSM contains semantic relations. Experimental analysis show that the documents'similarity computed by DOBVSM was more divergent, closest to the expert evaluation and can perform batter in document similarity.