Implementation of an open chemistry knowledge base with a Semantic Wiki
Charlotte Neidiger, Tarek Saier, Kai Kühn, Victor Larignon, Michael Färber, Claudia Bizzarri, Helena Šimek Tosino, Laura Holzhauer, Michael Erdmann, An Nguyen, Dean Harvey, Pierre Tremouilhac, Claudia Kramer, Daniel Hansch, Fabian Schönle, Jana Alpin, Maximilian Hartmann

TL;DR
This paper introduces a collaborative platform for chemists to collect and organize research data using a Semantic Wiki, enhancing data sharing and analysis.
Contribution
The paper introduces a cheminformatics-enabled Semantic MediaWiki for collaborative chemical knowledge management, integrating chemical structures and research data.
Findings
A Semantic MediaWiki was enhanced to support chemical data collection and structured summaries.
Tools for capturing chemical structures in machine-readable formats were implemented.
The platform's effectiveness was demonstrated in organizing research on CO2 reduction in photocatalytic systems.
Abstract
In this work, a concept for an open chemistry knowledge base was developed to integrate chemical research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) to support the collection and structured summary of chemical data contained in publications. We implemented tools for capturing chemical structures in machine-readable formats and designed data forms along with a data model to ensure standardized input and organization of research results. These enhancements allow for effective data comparison and contextual analysis within an expandable Wiki environment. The use of the platform was specifically demonstrated by organizing and comparing research in the area of “CO2 reduction in homogeneous photocatalytic systems,” showcasing its potential to significantly enhance the collaborative collection of research outcomes. Scientific…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsScientific Computing and Data Management · Machine Learning in Materials Science · Biomedical Text Mining and Ontologies
