Entity Relation Aware Graph Neural Ranking for Biomedical Information Retrieval

The performance of biomedical information retrieval greatly depends on biomedical knowledge; however the knowledge of available medical knowledge base is often incomplete and out-of-dated. To solve the problem that incomplete knowledge bases cannot provide the medical knowledge required for biomedical information retrieval, the paper proposes an Entity Relation Aware Graph Neural Ranking model (ERAGNR), aiming to fully leverage the internal knowledge of the document to alleviate the problem caused by incomplete external knowledge bases. ERAGNR mines the relationships between biomedical entities in the document through entity relation extraction and combines them with external knowledge. It increases the semantic association and reduces the semantic gap between the query and the document. The method first constructs a knowledge-query graph and a document-entity graph, and then fuses the two graphs to obtain a knowledge-query-document-entity graph. In a multi-task learning framework that combines text retrieval and relation extraction tasks, ERAGNR employs a shared text encoder and a graph neural network. This enables ERAGNR to learn semantic matching patterns between queries and documents and recognize relationships between entities in the documents. As a result, the model can capture semantic matching signals between entity relationships in the context and queries. The experimental results show that ERAGNR outperforms the state-of-the-art models. Through biomedical relation extraction task, the model can learn the ability to capture the context of the entity relations in the document, so that the model can more accurately match the semantics between the query and the document.