CAREER:Ontology-powered Named Entity Recognition and Robust Semantic Similarity Metrics

职业：本体驱动的命名实体识别和鲁棒语义相似性度量

• 批准号: 1942727
• 负责人: Prashanti Manda
• 金额: $ 50.05万
• 依托单位: University of North Carolina Greensboro
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942727&HistoricalAwards=false
• 关键词: CAREER; Ontology; powered; Named; Entity

The goal of this project is to develop novel methods (i.e. natural language processing methods) for recognizing groups of terms (i.e. ontology concepts) in published biology research. Ontologies are a system to organize information (e.g. biology terms and relationships of those terms). Ontologies can be easily analyzed by computers, making large scale biological analyses possible. Currently, the translation of knowledge from scientific publications to an ontology-powered format still relies largely on manual curation of literature; this process is unscalable and tedious. Advances pioneered by this new project will enable knowledge discovery from scientific literature and will power computational analyses to answer complex biological questions. Educational impacts of this project will involve the development of new courses on cutting edge natural language processing methods to train the next generation of interdisciplinary computer scientists. The research will advance the state of art in development of machine learning models in the domain of automated curation and knowledge discovery in scientific literature. The research aims to advance and establish new paradigms in the area of deep learning methods/techniques for biological literature, towards two specific research outcomes- 1) development of novel deep learning models for ontology-based named entity recognition, and 2) development of robust semantic similarity metrics for evaluating the above deep learning models. Robust semantic similarity metrics are essential to accurately evaluate the performance of the deep learning models that will be developed here. These similarity metrics will be broadly applicable in several areas of biology such as hypothesizing candidate genes for evolutionary transitions, rare human disease diagnosis, etc. For further information visit the project website at https://prashanti.github.io/deeplearningNER/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是开发新的方法（即自然语言处理方法），用于识别已发表的生物学研究中的术语组（即本体概念）。本体是一个组织信息的系统（例如生物学术语和这些术语之间的关系）。 本体可以很容易地被计算机分析，使得大规模的生物分析成为可能。目前，从科学出版物到本体驱动格式的知识翻译仍然主要依赖于文献的手动管理;这个过程是不可扩展和繁琐的。这个新项目开创的进步将使科学文献中的知识发现成为可能，并将为计算分析提供动力，以回答复杂的生物学问题。该项目的教育影响将涉及开发关于尖端自然语言处理方法的新课程，以培养下一代跨学科计算机科学家。该研究将推进机器学习模型在科学文献中的自动化策展和知识发现领域的发展。该研究旨在推进和建立生物文献深度学习方法/技术领域的新范式，实现两个具体的研究成果- 1）开发基于本体的命名实体识别的新型深度学习模型，以及2）开发用于评估上述深度学习模型的强大语义相似性度量。强大的语义相似性度量对于准确评估本文将开发的深度学习模型的性能至关重要。这些相似性指标将广泛适用于生物学的几个领域，如假设候选基因的进化过渡，罕见的人类疾病诊断等，如需进一步信息，请访问项目网站https://prashanti.github.io/deeplearningNER/.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Knowledge of the Ancestors: Intelligent Ontology-aware Annotation of Biological Literature using Semantic Similarity

• 发表时间: 2022
• 作者: Pratik Devkota;Somya Mohanty