探索维基百科中语义相关的概念：以SeRE为例
Exploring semantically-related concepts from Wikipedia: the case of SeRE
摘要：
在本文中，我们提出了我们的Web应用程序SeRE旨在探索语义相关的概念。维基百科和DBpedia是丰富的数据源，可以提取给定主题的相关实体，例如入站和出站链接，更广泛和更窄的术语，分类信息等。我们使用维基百科全文正文来计算提取术语的语义相关性，从而生成与主题最相关的实体列表。对于任何给定的查询，SeRE的用户界面可视化这些相关的概念，按照语义相关性排序;来自维基百科文章的片段解释了这两个实体之间的联系。在一项用户研究中，我们研究了如何使用SeRE来找到给定主题的重要实体及其关系，并回答了如何使用分类系统进行过滤的问题。
英文摘要：
In this paper we present our web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. We use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study we examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
