Collaborative Research: ABI Innovation: Enabling machine-actionable semantics for comparative analyses of trait evolution

合作研究：ABI 创新：启用机器可操作的语义以进行特征进化的比较分析

• 批准号: 2048296
• 负责人: Wasila Dahdul
• 金额: $ 7.35万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-22 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048296&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Enabling

The millions of species that inhabit the planet all have distinct biological traits that enable them to successfully compete in or adapt to their ecological niches. Determining accurately how these traits evolved is thus fundamental to understanding earth's biodiversity, and to predicting how it might change in the future in response to changes in ecosystems. Although sophisticated analytical methods and tools exist for analyzing traits comparatively, applying their full power to the myriad of trait observations recorded in the form of natural language descriptions has been hindered by the difficulty of allowing these tools to understand even the most basic facts implied by an unstructured free-text statement made by a human observer. The technological arsenal needed to overcome this challenge is now in principle available, thanks to a number of recent breakthroughs in the areas of knowledge representation and machine reasoning, but these technologies are challenging enough to deploy, orchestrate, and use that the barriers to effectively exploit them remains far too high for most tools. This project will create infrastructure that will dramatically reduce this barrier, with the goal of providing comparative trait analysis tools easy access to algorithms powered by machines reasoning with and making inferences from the meaning of trait descriptions. Similar to how Google, IBM Watson, and others have enabled developers of smartphone apps to incorporate, with only a few lines of code, complex machine-learning and artificial intelligence capabilities such as sentiment analysis, this project will demonstrate how easy access to knowledge computing opens up new opportunities for analysis, tools, and research. It will do this by addressing three long-standing limitations in comparative studies of trait evolution: recombining trait data, modeling trait evolution, and generating testable hypotheses for the drivers of trait adaptation.The treasure trove of morphological data published in the literature holds one of the keys to understanding the biodiversity of phenotypes, but exploiting the data in full through modern computational data science analytics remains severely hampered by the steep barriers to connecting the data with the accumulated body of morphological knowledge in a form that machines can readily act on. This project aims to address this barrier by creating a centralized computational infrastructure that affords comparative analysis tools the ability to compute with morphological knowledge through scalable online application programming interfaces (APIs), enabling developers of comparative analysis tools, and therefore their users, to tap into machine reasoning-powered capabilities and data with machine-actionable semantics. By shifting all the heavy-lifting to this infrastructure, tools can programmatically obtain answers to knowledge-based questions that would otherwise require careful study by a human export, such as objectively and reproducibly assessing the relatedness, independence, and distinctness of characters and character states, with only a few lines of code. To accomplish this, the project will adapt key products and know-how developed by the Phenoscape project, including an integrative knowledgebase of ontology-linked phenotype data, metrics for quantifying the semantic similarity of phenotype descriptions, and algorithms for synthesizing morphological data from published trait descriptions. To drive development of the computational infrastructure and to demonstrate its enabling value, the project's objectives focus on addressing three concrete long-standing needs for which the difficulty of computing with domain knowledge is the major impediment: (1) computationally synthesizing, calibrating, and assessing morphological trait matrices from across studies; (2) objectively and reproducibly incorporating morphological domain knowledge provided by ontologies into evolutionary models of trait evolution; and (3) generating testable hypotheses for adaptive diversification by incorporating semantic phenotypes into ancestral state reconstruction and identifying domain ontology concepts linked to evolutionary changes in a branch or clade more frequently than expected by chance. In addition, to better prepare evolutionary biologist users and developers of comparative analysis tools for adopting these new capabilities, a domain-tailored short-course on requisite knowledge representation and computational inference technologies will be developed and taught. More information on this project can be found at http://cate.phenoscape.org/.

居住在地球上的数百万物种都具有独特的生物学特征，使它们能够成功地竞争或适应自己的生态位。因此，准确地确定这些特征是如何演变的，对于了解地球的生物多样性，以及预测未来可能如何应对生态系统的变化是至关重要的。尽管存在比较先进的分析方法和工具来分析特征，但将它们的全部功能用于以自然语言描述的形式记录的无数特征观察，由于难以让这些工具理解人类观察者所作的无结构自由文本陈述所暗示的最基本的事实，因此受到了阻碍。由于最近在知识表示和机器推理领域取得了一些突破，克服这一挑战所需的技术武器库原则上是可用的，但这些技术在部署、协调和使用方面具有足够的挑战性，因此有效利用它们的障碍对于大多数工具来说仍然太高了。该项目将创建将极大地减少这一障碍的基础设施，目标是提供比较特征分析工具，以便轻松访问由机器支持的算法，通过特征描述的含义进行推理和推断。类似于谷歌、IBM Watson和其他公司如何使智能手机应用程序的开发人员只需几行代码、复杂的机器学习和情感分析等人工智能功能，该项目将展示轻松获取知识计算如何为分析、工具和研究打开新的机会。它将通过解决特征进化比较研究中的三个长期存在的限制来做到这一点：重组特征数据，对特征进化建模，以及为特征适应的驱动因素生成可测试的假设。文献中发表的形态数据宝库拥有理解表型生物多样性的关键之一，但通过现代计算数据科学分析充分利用这些数据仍然严重阻碍了将数据与积累的形态知识以机器可以容易地采取行动的形式连接起来的陡峭障碍。该项目旨在通过创建一个集中式计算基础设施来解决这一障碍，该基础设施为比较分析工具提供了通过可扩展的在线应用程序编程接口(API)利用形态知识进行计算的能力，使比较分析工具的开发人员以及他们的用户能够利用机器推理的能力和具有机器可操作语义的数据。通过将所有重担转移到此基础设施，工具可以编程方式获得基于知识的问题的答案，否则需要人工输出仔细研究，例如，只需几行代码，就可以客观地、可重复地评估字符和字符状态的相关性、独立性和清晰度。为了实现这一目标，该项目将采用Phenoscape项目开发的关键产品和技术，包括与本体相关的表型数据的综合知识库，用于量化表型描述的语义相似性的度量标准，以及从已发表的特征描述中合成形态数据的算法。为了推动计算基础设施的开发并展示其使能价值，该项目的目标集中于解决三个长期存在的具体需求，其中使用领域知识进行计算的难度是主要障碍：(1)通过计算综合、校准和评估来自不同研究的形态特征矩阵；(2)客观和可重复地将本体提供的形态领域知识纳入特征进化的进化模型；以及(3)通过将语义表型纳入祖先状态重建中并确定与分支或分支中的进化变化相关联的领域本体概念，从而生成可测试的多样化假设。此外，为了使进化生物学家用户和比较分析工具的开发人员更好地准备采用这些新能力，将开发和教授关于必要知识表示和计算推理技术的专门领域的短期课程。有关该项目的更多信息，请访问http://cate.phenoscape.org/.。