Harnessing linked knowledge sources for topic classification in social media
Harnessing linked knowledge sources for topic classification in social media
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DOI：
10.1145/2481492.2481497
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发表时间：
2013-05
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通讯作者：
A. Cano;Andrea Varga;Matthew Rowe;F. Ciravegna;Yulan He
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文献类型：
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作者：
A. Cano;Andrea Varga;Matthew Rowe;F. Ciravegna;Yulan He
短文本消息的主题分类（TC）提供了一种有效且快速的方式来揭示世界各地发生的事件，从与灾难（例如桑迪飓风）相关的事件到与暴力（例如埃及革命）相关的事件。以前的TC方法主要集中在利用单个知识源（KS）（例如DBpedia或Freebase），而不考虑在检测推文的主题时围绕KS中存在的概念的图结构。在本文中，我们介绍了一种新的方法，利用这种图结构从多个链接的KS，通过：（i）建立一个概念表示的KS，（ii）利用上下文信息的概念，利用语义概念图，和（iii）提供一个原则的方式组合KS。在暴力检测（VD）和紧急响应（ER）的背景下评估我们的TC分类器的实验显示出有希望的结果，显着优于各种基线模型，包括使用单个KS没有链接的数据的方法和只使用推文的方法。
Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.