NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Abstract

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades, the most dramatic advances in machine reading have followed in the wake of critical corpus development. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet was fundamental for developing computer vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: An Ontology Development and Clustering Approach

“…In engineering design, there is some precedent for using Failure Modes and Effects Analysis (FMEA)-style labels . Cause, failure, effect, and other common labels from an FMEA-style analysis are useful in many contexts for organizing common failures; however, ontologies in a style more similar to that of Wang et al (2021), which expresses common sources of failure causes such as those related to the organization, development process or operator, may be needed for to characterize non-technical factors that contribute to failure.…”

“…generalize well, but any application that uses domain-dependent entities must often be carefully defined and developed. The biomedical domain especially is increasingly recognizing the importance of developing appropriate ontologies for such tasks (Wang et al, 2021), in addition to increasing recognition in software-specific named entity recognition (Ye et al, 2016). In engineering design, there is some precedent for using Failure Modes and Effects Analysis (FMEA)-style labels .…”