Making sense of social media streams through semantics: A survey
Making sense of social media streams through semantics: A survey
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DOI：
10.3233/sw-130110
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发表时间：
2014
期刊：
影响因子：
3
通讯作者：
Kalina Bontcheva;D. Rout
中科院分区：
文献类型：
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作者：
Kalina Bontcheva;D. Rout
使用语义技术对社交媒体进行挖掘和智能信息访问是一个具有挑战性的新兴研究领域。传统的搜索方法不再能够解决媒体流中更复杂的信息搜索行为，这些行为已经演变为意义制造、学习、调查和社交搜索。与精心编写的新闻文本和更长的网络上下文不同，社交媒体流由于其大规模、简短、嘈杂、上下文相关和动态的性质，提出了许多新的挑战。本文定义了这一新应用领域的五个关键研究问题，通过对从社交媒体流中挖掘语义的最新方法的调查；用户、网络和行为建模；以及智能的、基于语义的信息访问。这项调查不仅包括语义网研究领域的关键方法，还包括自然语言处理和用户建模等相关领域的关键方法。最后，讨论了关键的突出挑战，并提出了新的研究方向。
Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches to mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.