# DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
## 3 Proposed Approach
CrisisLexT26 ([[https://crisislex.org/data-collections.html#CrisisLexT26](https://crisislex.org/data-collections.html#CrisisLexT26)]([https://crisislex.org/data-collections.html#CrisisLexT26](https://crisislex.org/data-collections.html#CrisisLexT26))). Although they do not have the same data, as a set, they have English tweets across more than 30 large disasters which took place between 2012 and 2013. These include, for the sake of example, the 2012 Colorado Wildfires and the famous 2013 Boston Marathon Bombings. On top of this, to better comprehend the domain where they are being applied, our classification models use the 200d Glove [44] Twitter embeddings ([[https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/)]([https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/))). These pretrained embeddings have explicitly been trained from more than 2 billion tweets, with about 30 billion tokens, and more than 1.2 million unique vocabulary, where each word is represented as a 200-dimensional vector. The usage of these embeddings is a huge advantage in tweet information extraction, as most pretrained embeddings are either trained with Wikipedia data, web crawling data, or news. As shown in Figure 3, our CNNs are fed integer encoded tweets, which have formerly been padded to 26 words to guarantee homogeneity. The embedding layer is responsible for converting these integer vectors into 26 by 200 matrices, where 200 represents the vectorial depiction of each tweet's word, according to the embeddings we have used. Following the embedding layer, the CNNs have a convolutional layer with a Rectified Linear Unit (ReLU) activation where 128 feature detectors slide through the data in one dimension--hence being a 1D convolutional layer--for 5 steps. The global max pooling layer follows, which is responsible for the dimensionality reduction of the data. The network then has a set of three dense layers--that also use ReLU activation--with decreasing number of nodes and dropout rates, which help us prevent overfitting our data. This is extremely important because we are dealing with data relating to particular scenarios/disasters, so we want to keep our models from overfitting specific data, which can be done, to some extent, by ignoring random neurons during the training phase. The network's output is then yielded through a dense layer, which varies from having one node and a sigmoid activation in the relatedness classification CNN and having five nodes and a softmax activation in the type classification CNN. 1. **Threat Object Generation:** The second to last step of our pipeline consists of joining all of the information extracted from the previous steps. That is, the tweet information extracted by the Tweet Extraction and Filtering module, all of the entities extracted by the Named Entity Recognition module, and the threat type classified by the Disaster Extraction, are all combined to form a disaster object. This step is essential, as it creates a sense of identity for the detected disasters, joining their occurrences to all of the information that define them. We consider the location, date, and type of threat to be the minimum necessary information for creating and comparing a threat. Therefore, this module also serves as an extra filtering phase, as it discards any threats the system has not been able to extract enough information from. 2. **Knowledge Base Integration:** Lastly, the detected disasters must be included in our knowledge graph. This step involves all validations regarding the previous existence of our gathered data, as well as the creation of new nodes and relationships between them, to express the identity of  
Figure 3: Threat type classification convolutional neural network (CNN). the extracted disasters in a way that facilitate human consumption and future system integrations. To further deal with social media unreliability, all disaster nodes have an attribute which is incremented when the same disaster is detected from different tweets.