Carbon Figures of Merit Knowledge Creation with a Hybrid Solution and Carbon Tables API

TL;DR

This paper presents a hybrid approach combining heuristics and machine learning in an asynchronous REST API to extract and organize carbon-related figures of merit from scientific PDFs into knowledge graphs, enhancing discovery tools.

Contribution

It introduces a novel hybrid method and an API for automated extraction and organization of carbon figures of merit from scientific documents into knowledge graphs.

Findings

Efficient extraction of carbon figures of merit from PDFs.

Integration of heuristics and machine learning improves accuracy.

Enhanced knowledge graph creation for carbon research.

Abstract

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.