计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2013年
10期
236-240,244
,共6页
信息内容%本体%语义相似度%子节点%分类树%熵
信息內容%本體%語義相似度%子節點%分類樹%熵
신식내용%본체%어의상사도%자절점%분류수%적
Information Content(IC)%ontology%semantic similarity%child node%classification tree%entropy
提出一种用于计算WordNet中概念信息内容(IC)值的模型。引入熵的概念，不仅考虑概念的子节点数目和概念所处分类树中的深度，而且考虑了概念子节点的空间结构，使得概念的IC值更为精确。将该模型代入到基于IC的语义陒似度算法中，实验结果表明，该模型可有效提高算法的准确度。
提齣一種用于計算WordNet中概唸信息內容(IC)值的模型。引入熵的概唸，不僅攷慮概唸的子節點數目和概唸所處分類樹中的深度，而且攷慮瞭概唸子節點的空間結構，使得概唸的IC值更為精確。將該模型代入到基于IC的語義陒似度算法中，實驗結果錶明，該模型可有效提高算法的準確度。
제출일충용우계산WordNet중개념신식내용(IC)치적모형。인입적적개념，불부고필개념적자절점수목화개념소처분류수중적심도，이차고필료개념자절점적공간결구，사득개념적IC치경위정학。장해모형대입도기우IC적어의희사도산법중，실험결과표명，해모형가유효제고산법적준학도。
A new Information Content(IC) model is given in this paper which can be used to calculate the IC value of concept in WordNet. The concept of entropy is introduced in the model which considers not only the number of child node of concept and the depth in the tree of taxonomy of WordNet, but also the spatial structure of hyponyms of the concept, the model makes the value of IC of the concept more accurate. Experimental results show that the precision of the semantic similarity algorithms using the IC values computed by the entropy model can be improved.
