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Register a new userPooling Hybrid Representations for Web Structured Data Annotation
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Added by
Luciano Barbosa
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Automatically identifying data types of web structured data is a key step in the process of web data integration. Web structured data is usually associated with entities or objects in a particular domain. In this paper, we aim to map attributes of an entity in a given domain to pre-specified classes of attributes in the same domain based on their values. To perform this task, we propose a hybrid deep learning network that relies on the format of the attributes values. It does so without any pre-processing or using pre-defined hand-crafted features. The hybrid network combines sequence-based neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), to learn the sequence structure of attributes values. The CNN captures short-distance dependencies in these sequences through a sliding window approach, and the RNN captures long-distance dependencies by storing information of previous characters. These networks create different vector representations of the input sequence which are combined using a pooling layer. This layer applies a specific operation on these vectors in order to capture their most useful patterns for the task. Finally, on top of the pooling layer, a softmax function predicts the label of a given attribute value. We evaluate our strategy in four different web domains. The results show that the pooling network outperforms previous approaches, which use some kind of input pre-processing, in all domains.
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A challenge for data imputation is the lack of knowledge. In this paper, we attempt to address this challenge by involving extra knowledge from web. To achieve high-performance web-based imputation, we use the dependency, i.e.FDs and CFDs, to impute as many as possible values automatically and fill in the other missing values with the minimal access of web, whose cost is relatively large. To make sufficient use of dependencies, We model the dependency set on the data as a graph and perform automatical imputation and keywords generation for web-based imputation based on such graph model. With the generated keywords, we design two algorithms to extract values for imputation from the search results. Extensive experimental results based on real-world data collections show that the proposed approach could impute missing values efficiently and effectively compared to existing approach.
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