Chemical Species Ontology for Data Integration and Knowledge Discovery

Pascazio, Laura; Rihm, Simon; Naseri, Ali; Mosbach, Sebastian; Akroyd, Jethro; Kraft, Markus

doi:10.1021/acs.jcim.3c00820

Cited by 19 publications

(10 citation statements)

References 75 publications

(170 reference statements)

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“…OntoSpecies KG was designed to permit complex queries and easy data analysis and processing in the general chemistry domain. The information reported on chemical species can be used to compare chemical properties of similar compounds, find compounds with required characteristics, as well as automate laborious data gathering from research activities . In this section, we showcase how Marie can be used for some of these tasks without the requirement of SPARQL knowledge and KG schema knowledge.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to mention that each entry in the table is displayed at least twice due to every compound in the KG having two distinct IUPAC names. In some cases, the redundancy extends beyond this because of the manner in which OntoSpecies KG sources its data from PubChem. , Given that PubChem aggregates properties from a variety of sources, it is not uncommon to encounter multiple instances of the same property. A case in point is 1-propyn-1-ol, which is listed in Figure with two separate boiling point values, each attributable to a distinct source.…”

Section: Resultsmentioning

confidence: 99%

“…The TWA KG is a comprehensive, cross-domain, and dynamic KG adhering to linked data principles. It seamlessly integrates various ontologies, such as OntoSpecies, 47 OntoKin, 48 OntoCompChem, 49 OntoPESScan, 50 and OntoMOPs, 7 all tailored for representing chemical information. OntoSpecies is a fundamental chemistry ontology within TWA KG.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry

Tran,

Pascazio,

Akroyd

et al. 2024

ACS Omega

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In this study, we present a question answering (QA) system for chemistry, named Marie, with the use of a text-to-text pretrained language model to attain accurate data retrieval. The underlying data store is "The World Avatar" (TWA), a general world model consisting of a knowledge graph that evolves over time. TWA includes information about chemical species such as their chemical and physical properties, applications, and chemical classifications. Building upon our previous work on KGQA for chemistry, this advanced version of Marie leverages a fine-tuned Flan-T5 model to seamlessly translate natural language questions into SPARQL queries with no separate components for entity and relation linking. The developed QA system demonstrates competence in providing accurate results for complex queries that involve many relation hops as well as showcasing the ability to balance correctness and speed for real-world usage. This new approach offers significant advantages over the prior implementation that relied on knowledge graph embedding. Specifically, the updated system boasts high accuracy and great flexibility in accommodating changes and evolution of the data stored in the knowledge graph without necessitating retraining. Our evaluation results underscore the efficacy of the improved system, highlighting its superior accuracy and the ability in answering complex questions compared to its predecessor.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry

Tran,

Pascazio,

Akroyd

et al. 2024

ACS Omega

Self Cite

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show abstract

“…The integration of chemical knowledge from PubChem, represented by OntoSpecies for unique species identification 55 , serves as a critical link between these facets of chemicals. It enables the identification of potential input chemicals based on the reactant and solvent during DoE and allows for the selection of appropriate sources of starting materials from multiple chemical containers (see Supplementary Information section A.2) .…”

Section: Resultsmentioning

confidence: 99%

A dynamic knowledge graph approach to distributed self-driving laboratories

Bai,

Mosbach,

Taylor

et al. 2024

Nat Commun

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The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist’s research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

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“…TWA KG is a large-scale, cross-domain, and dKG that follows linked data principles and integrates ontologies from various domains, including chemistry. TWA integrated and interconnected a number of ontologies specifically developed for representing chemical data such as OntoKin, 14 OntoCompChem, 15 OntoSpecies, 16 and OntoMOPs 17 ontologies.…”

Section: Related Workmentioning

confidence: 99%

Marie and BERT─A Knowledge Graph Embedding Based Question Answering System for Chemistry

Zhou,

Zhang,

Agarwal

et al. 2023

ACS Omega

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This paper presents a novel knowledge graph question answering (KGQA) system for chemistry, which is implemented on hybrid knowledge graph embeddings, aiming to provide fact-oriented information retrieval for chemistry-related research and industrial applications. Unlike other existing designs, the system operates on multiple embedding spaces, which use various embedding methods and queries the embedding spaces in parallel. With the answers returned from multiple embedding spaces, the system leverages a score alignment model to adjust the answer scores and rerank the answers. Further, the system implements an algorithm to derive implicit multihop relations to handle the complexities of deep ontologies and improve multihop question answering. The system also implements a BERT-based bidirectional entity-linking model to enhance the robustness and accuracy of the entity-linking module. The system uses a joint numerical embedding model to efficiently handle numerical filtering questions. Further, it can invoke semantic agents to perform dynamic calculations autonomously. Finally, the KGQA system handles numerous chemical reaction mechanisms using semantic parsing supported by a Linked Data Fragment server. This paper evaluates the accuracy of each module within the KGQA system with a chemistry question data set.

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Chemical Species Ontology for Data Integration and Knowledge Discovery

Cited by 19 publications

References 75 publications

Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry

Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry

A dynamic knowledge graph approach to distributed self-driving laboratories

Marie and BERT─A Knowledge Graph Embedding Based Question Answering System for Chemistry

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