论文标题
使用混合解决方案和碳表API创建优异知识的碳人物
Carbon Figures of Merit Knowledge Creation with a Hybrid Solution and Carbon Tables API
论文作者
论文摘要
如今，正在创建算法，方法和平台，以加速能够吸收或吸附的材料，例如在大气中或在发电厂中燃烧期间的co_2 $ co_2 $分子。在这项工作中，描述了一种异步REST API，以加速创建优异知识的碳人物，称为碳表，因为知识是由科学PDF文档中的表创建的，并存储在知识图中。优秀知识创建解决方案的数字采用了混合方法，其中启发式方法和机器学习是其中的一部分。结果，人们可以使用成熟而复杂的认知工具搜索知识，并就功绩碳人物形象创造更多的知识。
Nowadays there are algorithms, methods, and platforms that are being created to accelerate the discovery of materials that are able to absorb or adsorb $CO_2$ molecules that are in the atmosphere or during the combustion in power plants, for instance. In this work an asynchronous REST API is described to accelerate the creation of Carbon figures of merit knowledge, called Carbon Tables, because the knowledge is created from tables in scientific PDF documents and stored in knowledge graphs. The figures of merit knowledge creation solution uses a hybrid approach, in which heuristics and machine learning are part of. As a result, one can search the knowledge with mature and sophisticated cognitive tools, and create more with regards to Carbon figures of merit.
