.png){kind=icon}
返回
A semantic similarity method based on information content exploiting multiple ontologies
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2012.08.049.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The quantification of the semantic similarity between terms is an important research area that configures a valuable tool for text understanding. Among the different paradigms used by related works to compute semantic similarity, in recent years, information theoretic approaches have shown promising results by computing the information content (IC) of concepts from the knowledge provided by ontologies. These approaches, however, are hampered by the coverage offered by the single input ontology. In this paper, we propose extending IC-based similarity measures by considering multiple ontologies in an integrated way. Several strategies are proposed according to which ontology the evaluated terms belong. Our proposal has been evaluated by means of a widely used benchmark of medical terms and MeSH and SNOMED CT as ontologies. Results show an improvement in the similarity assessment accuracy when multiple ontologies are considered. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Information content
Semantic similarity
Ontologies
MeSH
SNOMED CT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
Determining semantic similarity among entity classes from different ontologies确定来自不同本体的实体课程之间的语义相似度
PREAI
分享收藏
查看更多内容