Semantic and Event-Based Approach for Link Prediction
Semantic and Event-Based Approach for Link Prediction
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DOI：
10.1007/978-3-540-89447-6_7
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
Till Wohlfarth;R. Ichise
中科院分区：
文献类型：
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作者：
Till Wohlfarth;R. Ichise
研究人员之间的合作所带来的科学突破往往超出预期。但是，找到能够带来这种协同效应的合作伙伴可能需要时间，而且考虑到各个学科的大量专家，有时会一无所获。我们建议建立一个网络中的节点代表研究人员和链接- coauthorships链接预测。在这种方法中，我们使用的结构构造图，并提出添加一个语义和事件为基础的方法，以提高预测的准确性。在这种情况下，预测器可能会为未来的合作提供很好的建议。我们将能够通过欠采样和平衡数据在合理的时间内计算大量数据集的分类。这种模式可以推广到其他领域，在那里的伙伴关系的研究是重要的，在世界的机构，协会或公司。我们相信，它也可以帮助找到社区的主题，因为链接预测包含隐含的信息研究人员之间的语义关系。
The scientific breakthroughs resulting from the collaborations between researchers often outperform the expectations. But finding the partners who will bring this synergic effect can take time and sometime gets nowhere considering the huge amounts of experts in various disciplines. We propose to build a link predictor in a network where nodes represent researchers and links - coauthorships. In this method we use the structure of the constructed graph, and propose to add a semantic and event based approach to improve the accuracy of the predictor. In this case, predictors might offer good suggestions for future collaborations. We will be able to compute the classification of a massive dataset in a reasonable time by under-sampling and balancing the data. This model could be extended in other fields where the research of partnership is important as in world of institutions, associations or companies. We believe that it could also help with finding communities of topics, since link predictors contain implicit information about the semantic relation between researchers.