# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 5 Use Case and Experimental Detail
### Data Quality Processing and Learning for Semantic Data Applications
#### 5.2.2 Deep Learning Model Description  
To validate the WoO based contextual data quality assessment model, various deep learning models such as Multi Layer Perceptron (MLP), Recurrent Neural Networks (RNNs), and Convolution Neural Network (CNN) are investigated, because the researchers have been using these models for decades for time series data, textual data such as text generation, sentiment classification, invalid text detection, etc. All these models need a large amount of data to perform the designated task, except the MLP because it can work with a small amount of data. However, MLP takes constant input and performs the analysis on a current instance only. Another deficiency in this model is that it does not supports the contextual information and variable input sequence length. The CNN model uses the fixed size of a window; which moves over the time series semantic data and ontologies in order to extract the relevant features. However, it is insufficient to extract the contextual features from the ontologies and time series data. Authors in studies [55; 56; 57] stressed that the RNNs based models are more suitable for the textual data analytics tasks, time series data processing, sequence modeling, machine translation, error detection, and action recognition. Long Short-Term Memory (LSTM) is based  
Figure 21: Semantic dataset A ontology. Figure 22: Semantic dataset B ontology. on the RNNs architecture that relatively has better support for sequence and time dependent data and it is an artificial neural network with feed forward connections [58]. These features are more suitable for contextual data quality assessment of semantic data applications [59]. We choose the bidirectional LSTM model along with autoencoders in order to extract contextual features from the history and current data in an unsupervised mode. The other reason to use bidirectional LSTM-autoencoder is that the datasets A and B contain time series data, that have been used along with the VOs and CVOs ontologies for WoO enabled services [60]. Therefore, the training and testing data contains numeric data with temporal features and ontologies as textual data. When the data includes numeric and textual input, the bidirectional LSTM-autoencoder could be used with different modalities. The VOs and CVO data has been given as input in a vector format to the model input layer. The designed bidirectional LSTM architecture contains two parts, the autoencoder, and LSTM. Therefore, first, we briefly explain the autoencoder design and then we cover the LSTM part. In the LSTM-autoencoder model, the input data are the bags of words that have been extracted from the ASC and TMC application VOs and CVOs ontologies. The design model supports the ability to learn the data quality patterns without any labeled data, because we used autoencoders with the LSTM, as the autoencoder enabled self-supervised learning from the given data representation of VOs and CVOs. The functional feature of designed autoencoder learning is represented mathematically as in Equation (1). The autoencoder supports two parts, the encoder Equation (2) and the decoder Equation (3). The encoders perform the conversion of data to the concise representation and the decoder inverse the concise representation in order to convert the input data to the original data. The transition from the encoder Equation (2) to a decoder Equation (3) is represented as a function shown in Equation (4). In a single hidden layer case, autoencoder receives the input VOs and CVOs data as mathematically represented in Equation (5) and maps it to the function as in Equation (6); where the value of \(z\) is represented as in Equation (7), and at the construction side, it is represented as Equation (8). In Equations (7) and (8) formulation \(\sigma\) is an activation function (i.e., ReLU or sigmoid), \(z\) is a latent variable, \(b\) is the bias vector. The latent variable could not be observed directly but it can be deduced from the related variables. The basic unit of LSTM is illustrated in Figure 23 [59].