The vast amount of available biomedical data has become a massive treasure trove of knowledge, offering significant potential for extracting valuable information. Extracting large-scale medical entities and relations from biomedical data is of great significance for constructing medical knowledge graphs, facilitating intelligent assistant diagnosis in the medical field, and enabling various other applications. Recent methods have achieved considerable progress, but there are still inherent limitations. First, these approaches extract entities or entity pairs without considering the relations between entities, which may lead to extracting redundant and/or incorrect entities. Second, they are unable to handle nested entities caused by the two-pointer method used for entity pair extraction. This is more challenging when dealing with Chinese medical records in which the occurrence of nested entities is more popular. Third, they encounter difficulties when subjects and objects are overlapping between different relations in a sentence. To tackle the above challenges, this paper proposes a novel relation extraction approach named CRE-RFGP. Specifically, it decomposes the relation extraction task into three components, including relation-first decoder, global entity extraction, and subject−object alignment. The decoder is utilized to predict and filter relations and restrict entity extraction based on predicted relations. A relation-specific attention mechanism and a global pointer network are employed to effectively handle the problem of information overlapping and object/subject nesting. An entity correspondence matrix is introduced to align subjects, objects, and their relations into triples. The matrix not only reduces the complexity of relation extraction but also plays an important part in improving the precision of extracted triples. Comprehensive evaluations are conducted experimentally based on two Chinese medical datasets. Comprehensive experiments on two Chinese medical datasets demonstrate that CRE-RFGP outperforms eight state-of-the-art approaches.

In the 5G environment, the edge computing paradigm enables service providers to deploy their service instances on distributed edge servers to serve nearby end users with extremely low latency. This boosts the emergence of modern applications, like AR/VR, online gaming, and autonomous vehicles. Existing approaches find service provision strategies under the assumption that all the user requirements are known. However, this assumption may not be true in practice and thus the effectiveness of existing approaches could be undermined. Inspired by the great success of recommender systems in various fields, we can mine users’ interests in new services based on their similarities in terms of current service usage. Then, new service instances can be provisioned accordingly to better fulfil users’ requirements. We formulate the problem studied in this paper as a Cost-aware Recommendation-oriented Edge Service Provision (CRESP) problem. Then, we formally model the CRESP problem as a Constrained Optimization Problem (COP). Next, we propose CRESP-O to find optimal solutions to small-scale CRESP problems. Besides, to solve large-scale CRESP problems efficiently, we propose an approximation approach named CRESP-A, which has a theoretical performance guarantee. Finally, we experimentally evaluate the performance of both CRESP-O and CRESP-A against several state-of-the-art approaches on a public testbed.