.png){kind=icon}
返回
A learning-based ontology alignment approach using inductive logic programming
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2019.02.014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Ontology alignment
Inductive logic programming
Ontology matching
Semantic web
Ontology mapping learning
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
The adult mouse anatomical dictionary: a tool for annotating and integrating data.png)
GENOME BIOLOGY
OAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏分享收藏Instance-based ontology matching for e-learning material using an associative pattern classifier
PREAI
分享收藏分享收藏分享收藏
查看更多内容