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发现维基百科中实体之间缺失的语义关系

Discovering Missing Semantic Relations between Entities in Wikipedia
课程网址: http://videolectures.net/iswc2013_wang_semantic_relations/  
主讲教师: 王志春
开课单位: 北京师范大学
开课时间: 2013-11-28
课程语种: 英语
中文简介:
维基百科的信息框包含各种实体的丰富结构化信息,DBpedia 项目已对这些信息进行了探索,以生成大规模关联数据集。在所有信息框属性中,那些值中具有超链接的属性标识实体之间的语义关系,这对于在 DBpedia 实例之间创建 RDF 链接非常重要。然而,相当多的超链接没有被编辑者在信息框中注释,这导致维基百科中缺少许多实体之间的关系。在本文中,我们提出了一种自动发现维基百科信息框中缺失的实体链接的方法,以便可以建立实体之间缺失的语义关系。我们的方法首先识别给定信息框中提到的实体,然后计算几个特征来估计给定属性值可能链接到候选实体的可能性。使用学习模型来获取不同特征的权重,并预测每个属性值的目标实体。我们在英文维基百科数据上评估了我们的方法,实验结果表明我们的方法可以有效地找到实体之间缺失的关系,并且在精度和召回率方面都显着优于基线方法。
课程简介: Wikipedia’s infoboxes contain rich structured information of various entities, which have been explored by the DBpedia project to generate large scale Linked Data sets. Among all the infobox attributes, those attributes having hyperlinks in its values identify semantic relations between entities, which are important for creating RDF links between DBpedia’s instances. However, quite a few hyperlinks have not been anotated by editors in infoboxes, which causes lots of relations between entities being missing in Wikipedia. In this paper, we propose an approach for automatically discovering the missing entity links in Wikipedia’s infoboxes, so that the missing semantic relations between entities can be established. Our approach first identifies entity mentions in the given infoboxes, and then computes several features to estimate the possibilities that a given attribute value might link to a candidate entity. A learning model is used to obtain the weights of different features, and predict the destination entity for each attribute value. We evaluated our approach on the English Wikipedia data, the experimental results show that our approach can effectively find the missing relations between entities, and it significantly outperforms the baseline methods in terms of both precision and recall.
关 键 词: 维基百科; 关联数据集; 链接预测
课程来源: 视频讲座网
数据采集: 2023-12-12:wujk
最后编审: 2023-12-12:wujk
阅读次数: 14