CAJ | 학술논문

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 space model)。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。
침대VSM불능게시은장재불동특정사후면적상동개념어의、반영문당중적잠재어의관계、재상사도계산중정도교저적문제, 제출일충기우영역본체적문당향량공간모형DOBVSM(domain ontology-based vector space model)。해모형파영역본체중적개념확전위문당특정사, 병통과개념간적어의관계대특정사권중진행조정, 최종건립포함어의관계적문당DOBVSM。통과실험분석표명：DOBVSM계산적문당상사도치경가발산, 여전가평개치최위접근, 능구교호지반영문당지간적상사정황。
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.