ArticleJournal of chemical information and modeling2023
Chemical Species Ontology for Data Integration and Knowledge Discovery.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.Journal of chemical information and modeling · 2026Article
- Inverse Design of Metal-Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation.Journal of chemical information and modeling · 2026Article
- A knowledge graph framework for digital twins of chemical processes.Nature chemical engineering · 2026Article
- CPIExtract: A software package to collect and harmonize small molecule and protein interactions.bioRxiv : the preprint server for biology · 2024Article
- The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies.Globalization and health · 2024Review
- Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry.ACS omega · 2024Article
- IUPHAR Review: New strategies for medications to treat substance use disorders.Pharmacological research · 2024Review
- A dynamic knowledge graph approach to distributed self-driving laboratories.Nature communications · 2024Article
- Marie and BERT-A Knowledge Graph Embedding Based Question Answering System for Chemistry.ACS omega · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Web ontologies are important tools in modern scientific research because they provide a standardized way to represent and manage web-scale amounts of complex data. In chemistry, a semantic database for chemical species is indispensable for its ability to interrelate and infer relationships, enabling a more precise analysis and prediction of chemical behavior. This paper presents OntoSpecies, a web ontology designed to represent chemical species and their properties. The ontology serves as a core component of The World Avatar knowledge graph chemistry domain and includes a wide range of identifiers, chemical and physical properties, chemical classifications and applications, and spectral information associated with each species. The ontology includes provenance and attribution metadata, ensuring the reliability and traceability of data. Most of the information about chemical species are sourced from PubChem and ChEBI data on the respective compound Web pages using a software agent, making OntoSpecies a comprehensive semantic database of chemical species able to solve novel types of problems in the field. Access to this reliable source of chemical data is provided through a SPARQL end point. The paper presents example use cases to demonstrate the contribution of OntoSpecies in solving complex tasks that require integrated semantically searchable chemical data. The approach presented in this paper represents a significant advancement in the field of chemical data management, offering a powerful tool for representing, navigating, and analyzing chemical information to support scientific research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.