Learning of OWL Class Descriptions on Very Large Knowledge Bases

被引：36

作者：

Hellmann, Sebastian ^{[1
]}

Lehmann, Jens

Auer, Soeren ^{[2
]}

机构：

[1] Univ Leipzig, Agile Knowledge Engn & Semant Web AKSW Res Grp, Leipzig, Germany

[2] Univ Penn, Database Res Grp, Philadelphia, PA 19104 USA

来源：

INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS | 2009年 / 5卷 / 02期

关键词：

Data Processing; Data Extraction; Information Filtering; Knowledge Base; Ontologies; Reasoning; RDF; Scalability; Semantic Web; SPARQL;

D O I：

10.4018/jswis.2009040102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, the authors present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. They describe in detail how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Their algorithms are made available in the open source DL-Learner project and we present several real-life scenarios in which they can be used by Semantic Web applications. [Article copies are available for purchase from InfoSci-on-Demand.com]

引用

页码：25 / 48

页数：24