ABSTRACT Existing datasets for move recognition, such as PubMed 200k RCT, exhibit several problems that significantly impact recognition performance, especially for Background and Objective labels. In order to improve the move recognition performance, we introduce a method and construct a refined corpus based on PubMed, named RCMR 280k. This corpus comprises approximately 280,000 structured abstracts, totaling 3,386,008 sentences, each sentence is labeled with one of five categories: Background, Objective, Method, Result, or Conclusion. We also construct a subset of RCMR, named RCMR_RCT, corresponding to medical subdomain of RCTs. We conduct comparison experiments using our RCMR, RCMR_RCT with PubMed 380k and PubMed 200k RCT, respectively. The best results, obtained using the MSMBERT model, show that: (1) our RCMR outperforms PubMed 380k by 0.82%, while our RCMR_RCT outperforms PubMed 200k RCT by 9.35%; (2) compared with PubMed 380k, our corpus achieve better improvement on the Results and Conclusions categories, with average F1 performance improves 1% and 0.82%, respectively; (3) compared with PubMed 200k RCT, our corpus significantly improves the performance in the Background and Objective categories, with average F1 scores improves 28.31% and 37.22%, respectively. To the best of our knowledge, our RCMR is among the rarely high-quality, resource-rich refined PubMed corpora available. Our work in this paper has been applied in the SciAIEngine, which is openly accessible for researchers to conduct move recognition task.

The early concept of knowledge graph originates from the idea of the semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, Chinese Information Processing Society of China (CIPS) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, and the on-chain test of OpenKG's data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered to be a measurement of knowledge value and user value. 1,033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, we have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the data sets and tool sets at OpenKG.CN, as well as the triplets at OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.