Related Entity Finding Using Semantic Clustering Based on Wikipedia Categories

机译：基于维基百科类别的使用语义聚类的相关实体查找

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摘要

We present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Our system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval measures to choose the most relevant. For each one we look up its Wikipedia page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives our system the ability to compute a measure of semantic relatedness between entities, even if the entities do not share any common category. We use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that axe closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.

机译：我们提出了一个执行相关实体查找（即问题回答）的系统，该问题利用来自WWW的语义信息并返回URI作为答案。我们的系统使用搜索引擎来收集所有候选答案实体，然后使用信息检索度量的线性组合来选择最相关的实体。对于每个人，我们都查找其Wikipedia页面，并基于Wikipedia类别名称的标记化来构造一种新颖的矢量表示形式。这种新颖的表示方式使我们的系统能够计算实体之间的语义相关性度量，即使实体不共享任何公共类别也是如此。我们使用此属性对候选实体执行语义聚类，并显示最大的聚类包含语义上密切相关的实体，可以将其视为查询的答案。根据2009 TREC相关实体查找任务中的20个主题测得的绩效显示出具有竞争力的结果。

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《Language processing and intelligent information systems》|2013年|157-170|共14页
会议地点 Warsaw(PL)
作者
Georgios Stratogiannis; Georgios Siolas; Andreas Stafylopatis;
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作者单位

National Technical University of Athens, Department of Electrical and Computer Engineering Intelligent Systems Laboratory Zografou Campus Iroon Polytexneiou 9, 15780 Zografou;

National Technical University of Athens, Department of Electrical and Computer Engineering Intelligent Systems Laboratory Zografou Campus Iroon Polytexneiou 9, 15780 Zografou;

National Technical University of Athens, Department of Electrical and Computer Engineering Intelligent Systems Laboratory Zografou Campus Iroon Polytexneiou 9, 15780 Zografou;

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原文格式 PDF
正文语种 eng
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关键词
Related Entity Finding; Wikipedia category vector representation; Semantic clustering;

机译：相关实体查找；维基百科类别矢量表示；语义聚类;

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专利

1. Semantic Question Answering Using Wikipedia Categories Clustering [J] . Georgios Stratogiannis, Georgios Siolas, Andreas Stafylopatis International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms . 2014,第4期

机译：使用Wikipedia类别聚类的语义问题回答
2. Finding the Semantic Relationship Between Wikipedia Articles Based on a Useful Entry Relationship [J] . Lin-Chih Chen International Journal of Data Warehousing and Mining . 2017,第4期

机译：基于有用的入境关系找到维基百科文章的语义关系
3. Named Entity Recognition with Word Embeddings and Wikipedia Categories for a Low-Resource Language [J] . Das Arjun, Ganguly Debasis, Garain Utpal ACM transactions on Asian language information processing . 2017,第3期

机译：资源嵌入的单词嵌入和维基百科类别的命名实体识别
4. Related Entity Finding Using Semantic Clustering Based on Wikipedia Categories [C] . Georgios Stratogiannis, Georgios Siolas, Andreas Stafylopatis International Conference on Intelligent Information Systems . 2013

机译：相关实体使用基于维基百科类别的语义聚类
5. Measuring named entity similarity through Wikipedia category hierarchies [D] . Ashman, Jared M. 2010

机译：通过Wikipedia类别层次结构测量命名实体的相似性
6. Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia category graph [O] . Muhammad Jawad Hussain, Shahbaz Hassan Wasti, Guangjian Huang, 2020

机译：用于在Wikipedia类别图中使用多个继承来计算概念之间的语义相似性的实验数据
7. Figure 8: Finding semantically related entities in the DBpedia ontology: The Linked_data and Controlled_vocabulary entities in the DBpedia knowledge base are assumed to be semantically related to each other, since they are both contained under the same category, i.e., Semantic_Web. [O] . -1

机译：图8：在DBPedia本体中查找语义相关实体：DBPedia知识库中的Linked_data和Scround_vocabulary实体被假定在语义上彼此进行语义相关，因为它们都包含在同一类别下，即，Semantic_Web。

Related Entity Finding Using Semantic Clustering Based on Wikipedia Categories

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