Knowledge graph is a kind of knowledge representation, which captures information about entities, entity attributes and relationships between entities in a structured way, and is widely used in intelligent retrieval, recommendation systems, intelligent question answering, etc. Knowledge map is a graphical representation of the relationship between different concepts and topics in a specific field, while BERT is a most advanced language model that can understand the context and meaning of words in text. By combining the two methods of medical knowledge graph (MKG) and the bidirectional encoder representation of BERT model, it shows hope in improving medical information retrieval and decision-making, and can create a more comprehensive and accurate representation of medical knowledge, which can be used to guide clinical decision-making and improve the prognosis of patients, and ultimately improve the effect of BERT in the medical field.
KEYWORDS: Environmental sensing, Feature extraction, Target detection, Information visualization, Education and training, Visualization, Adversarial training, Web 2.0 technologies, Visual process modeling, Systems modeling
Automatic fake news detection is crucial for society. Existing methods mainly focus on the post's content or taking advantage of external sources to make a decision. Recently a new approach called NEP has been proposed, it constructed out news environment for each news to capture popularity and novelty as other evidence. However, NEP neglects the changeable environment and it is weak when news breaks out or is published several times. Commonsense knowledge related to a post has sufficient reliability compared with the news environment or external news evidence, and it can bring a stable benefit for distinguishing fake news. Based on this, we search out commonsense knowledge for each post and propose the News Environment-Knowledge Perception (NEKP) based on NEP. For each post, we search out the related knowledge items on Wikimedia. Then we fuse this knowledge through an existing fake news detector–DeClarE. Finally, we fuse the news environment, knowledge, and the news itself to make a detection. Experiments on NEP datasets show that commonsense knowledge is another helpful piece of evidence.
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