Trend Template: Mining Trends With a Semi-formal Trend Model
Trend Template: Mining Trends With a Semi-formal Trend Model
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趋势模板：用半正式趋势模型挖掘趋势
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
D. Montesi
中科院分区：
文献类型：
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作者：
Olga Streibel;Lars Wißler;R. Tolksdorf;D. Montesi
在用户必须及时处理大量信息的不同场景中，例如在财务分析期间，对上升或下降趋势的预测是有帮助的。在数据分析的各种情况下，这种时间方面需要新的数据挖掘技术。假设一组给定的数据，例如网络新闻，包含有关潜在趋势的信息，例如金融危机，可以应用统计或概率方法，以找出有关此趋势的更多信息。然而，我们认为，为了理解的背景下，结构和解释的趋势，有必要采取以知识为基础的方法。在我们的研究中，我们定义了趋势挖掘，并提出了一个基于本体的趋势模型，从文本数据挖掘趋势的应用。我们介绍了趋势挖掘的初步定义以及我们的趋势模型的两个组成部分：趋势模板和趋势本体。此外，我们讨论了我们的实验结果与趋势本体的测试语料库的德国网络新闻。我们表明，我们的趋势挖掘方法是相关的无处不在的数据挖掘的不同场景。
Predictions of uprising or falling trends are helpful in different scenarios in which users have to deal with huge amount of information in a timely manner, such as during financial analysis. This temporal aspect in various cases of data analysis requires novel data mining techniques. Assuming that a given set of data, e.g. web news, contains information about a potential trend, e.g. financial crisis, it is possible to apply statistical or probabilistic methods in order to find out more information about this trend. However, we argue that in order to understand the context, the structure, and explanation of a trend, it is necessary to take a knowledge-based approach. In our study we define trend mining and propose the application of an ontology-based trend model for mining trends from textual data. We introduce the preliminary definition of trend mining as well as two components of our trend model: the trend template and the trend ontology. Furthermore, we discuss the results of our experiments with trend ontology on the test corpus of German web news. We show that our trend mining approach is relevant for different scenarios in ubiquitous data mining.