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: 1661529
• 负责人: Wasila Dahdul
• 金额: $ 33.65万
• 依托单位: University of South Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661529&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个物种都有不同的生物学特征，使它们能够成功地竞争或适应其生态壁ches。因此，准确地确定这些特征如何发展对于理解地球的生物多样性以及预测其未来可能如何改变生态系统的变化的基础。尽管存在相对分析性状的复杂分析方法和工具，但将其全部力量应用于以自然语言描述的形式记录的无数性状观察，这是由于难以理解这些工具甚至理解的最基本事实所暗示的最基本的事实而受到了阻碍。由于最近在知识代表和机器推理领域的许多突破，现在原则上可以使用来克服这一挑战所需的技术武器库，但是这些技术足以挑战，可以在大多数工具上进行有效利用它们的障碍，并利用有效利用它们的障碍。该项目将创建基础架构，该基础架构将大大降低此障碍，目的是提供比较性状分析工具轻松访问由机器推理驱动的算法，并根据特质描述的含义提出推断。类似于Google，IBM Watson和其他人如何使智能手机应用程序的开发人员仅使用几行代码，复杂的机器学习和人工智能功能，例如情感分析，该项目将如何轻松访问知识计算如何打开新的分析，工具，工具和研究的机会。它将通过解决特质进化比较研究中的三个长期局限性来实现这一目标：重新组合性状数据，建模性状进化并为特质适应的驱动因素生成可检验的假设。形态学的宝藏所发表的文献中发表的形态学数据的宝库是文献中的一项钥匙，可以通过了解现代的数据来确定陡峭的急性，以使现代化的数据进行整体计算，以实现现代化的数据，以实现现代化的数据，以实现现代化的数据，以实现现代化的数据，以实现现代化的数据，以实现现代化的数据。将数据与累积的形态知识体系相关联的障碍，以机器可以轻松作用的形式。该项目的目的是通过创建一个集中的计算基础架构来解决这一障碍，该基础架构提供了比较分析工具通过可扩展的在线应用程序编程界面（API）来计算形态知识的能力，从而使比较分析工具的开发人员能够利用其用户，以利用机器推理功能的功能和可与机器可用的可与机器相关的语义相关的数据。通过将所有重型提升转移到该基础架构上，工具可以通过编程方式获得基于知识的问题的答案，这些问题否则需要通过人类出口进行仔细研究，例如客观和可重复地评估角色和角色状态的相关性，独立性和独特性，只有几行代码。为此，该项目将适应Phenoscape项目开发的关键产品和专有技术，包括与本体学链接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指标以及用于从公开特征描述中合成形态学数据的算法。为了推动计算基础架构的发展并证明其有助于的价值，该项目的目标着重于满足三种具体的长期需求，这些需求与域知识相关的计算困难是主要的障碍：（1）计算合成，校准，校准和评估整个研究中的形态学性质矩阵； （2）将本体论提供的形态领域知识纳入特质进化的进化模型； （3）通过将语义表型纳入祖先状态重建并识别与分支机构或分支机构的进化变化相关的域本体概念来生成可自适应多样化的可检验假设，或者比偶然的频率更高。此外，为了更好地准备采用这些新功能的比较分析工具的进化生物学家和开发人员，将开发和讲授有关必要的知识表示和计算推理技术的领域范围的短途短篇小说。有关此项目的更多信息，请访问http://cate.phenoscape.org/。