2022/05/18 by Maíra Gatti de Bayser, de Bayser, Maira Gatti
Computer Science · #Artificial Intelligence (cs.AI) #Chemical Physics (physics.chem-ph) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Physical sciences #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2205.09175
openalex publication_date 2022/05/18 · openalex created_date 2022/05/23 · openalex updated_date 2026/07/28
Nowadays there are algorithms, methods, and platforms that are being created to accelerate the discovery of materials that are able to absorb or adsorb CO2 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.