Materials information extraction via automatically generated corpus

Yan, Rongen; Jiang, Xue; Wang, Weiren; Dang, Depeng; Su, Yanjing

doi:10.1038/s41597-022-01492-2

Cited by 13 publications

(6 citation statements)

References 27 publications

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“… Automated Literature Mining—A fundamental challenge in EBT is comprehensive evidence gathering from enormous toxicology literature. AI text mining uses natural language processing (NLP) to extract facts, relationships, and reported findings from papers to populate structured databases (Yan et al 2022 ). Toxicological ontology mapping further enables mining context-specific information (Foster et al 2024 ).…”

Section: Ai To Accelerate Evidence-based Toxicologymentioning

confidence: 99%

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer,

Hartung

2024

Arch Toxicol

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The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured—a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

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Section: Ai To Accelerate Evidence-based Toxicologymentioning

confidence: 99%

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer,

Hartung

2024

Arch Toxicol

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“…Traditionally, data has been shared through plots and tables in manuscripts, with important context embedded in manuscript text. While recent efforts make it possible to extract information from these documents using natural language processing methods, [47][48][49][50] traditional publications are not an efficient way of transmitting data. Fortunately, a slow shi towards more open data sharing is underway.…”

Section: Community Scale Data Managementmentioning

confidence: 99%

The laboratory of Babel: highlighting community needs for integrated materials data management

Pelkie

Pozzo

2023

Digital Discovery

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“…Here, the objective in analyzing textual ER data is to move away from supervised/semisupervised ML model analysis tools [10][11][12][13] and to instead automate the extraction of quantitative knowledge from textual data in order to assist system engineers in assessing SSC health trends and identify SSC anomalous behaviors. Knowledge extraction [20][21][22][23][24] is a very broad concept whose definition may vary depending on the application context. When applied to NPP ER textual data (i.e., IRs or WOs), the knowledge extraction approach described herein is designed to extract its syntactic and semantic elements.…”

Section: Introductionmentioning

confidence: 99%

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Wang,

Mandelli,

Cogliati

2024

Energies

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Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

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Materials information extraction via automatically generated corpus

Cited by 13 publications

References 27 publications

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

The laboratory of Babel: highlighting community needs for integrated materials data management

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

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