The digital generation is used to accessing the information from the Web, however, it usually contains a large amount of content that may possibly incorrect. Therefore, how to extract and organize the unstructured text content into the represented knowledge is a very challenging difficulty, especially the medical healthcare domain always involves domain-specific knowledge. So far no Chinese knowledge base for medical healthcare domain exists. There are still three issues being addressed. First, it is worth exploring the methods to identify domain-specific named entities. Second, no research focuses on domain-specific entity relation extraction. Third, few intelligent medicine applications based on knowledge representation and reasoning.A three-year project that focuses on Chinese knowledge base construction for medical healthcare domain and its application of question answering are proposed to address the above three issues. In the first year, we integrate deep neural networks with sequential labeling models to recognize domain-specific named entities. In the second year, we propose a deep neural network model to extract relations based on the recognized entities in the first year, and then integrate all extracted entity-relationships into a knowledge base. In the third year, we link our constructed knowledge base to BabelNet for enriching as a multilingual lexical-semantic network, visualize it as knowledge graphs, and build a question answering system for medical healthcare domain through reasoning on our Chinese knowledge base and graphs.
|Effective start/end date||1/05/21 → 30/04/22|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Medical healthcare
- named entity recognition
- entity relation extraction
- knowledge base
- knowledge graph
- question answering system
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