Exploiting Linked Open Data to Uncover Entity Types

被引:4
作者
Gao, Jie [1 ]
Mazumdar, Suvodeep [1 ]
机构
[1] Univ Sheffield, Dept Comp Sci, OAK Grp, Sheffield S10 2TN, S Yorkshire, England
来源
SEMANTIC WEB EVALUATION CHALLENGES | 2015年 / 548卷
关键词
D O I
10.1007/978-3-319-25518-7_5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes.
引用
收藏
页码:51 / 62
页数:12
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