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

居住在地球上的数百万种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其生态位。因此，准确地确定这些特征是如何进化的，对于理解地球的生物多样性，以及预测它在未来如何响应生态系统的变化至关重要。虽然存在复杂的分析方法和工具来比较分析特质，但将其全部力量应用于以自然语言描述形式记录的无数特质观察结果，却受到了阻碍，因为这些工具很难理解人类观察者所做的非结构化自由文本陈述所暗示的最基本的事实。克服这一挑战所需的技术武器库现在原则上是可用的，这要归功于最近在知识表示和机器推理领域的一些突破，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。该项目将创建基础设施，大大减少这一障碍，目标是提供比较特质分析工具，方便使用机器推理的算法，并从特质描述的意义中进行推断。类似于谷歌、IBM沃森和其他公司如何使智能手机应用程序的开发人员仅用几行代码就能整合复杂的机器学习和人工智能功能（如情感分析），该项目将展示如何轻松访问知识计算，为分析、工具和研究开辟新的机会。它将通过解决性状进化比较研究中的三个长期存在的局限性来做到这一点：重组性状数据，模拟性状进化，并为性状适应的驱动因素产生可检验的假设。文献中发表的形态学数据宝库是理解表型生物多样性的关键之一，但是，通过现代计算数据科学分析充分利用数据仍然受到严重阻碍，因为将数据与积累的形态学知识以机器可以轻松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，该基础设施通过可扩展的在线应用程序编程接口（API）为比较分析工具提供使用形态学知识进行计算的能力，使比较分析工具的开发人员及其用户能够利用机器推理驱动的功能和具有机器可操作语义的数据。通过将所有繁重的工作转移到这个基础设施上，工具可以通过编程方式获得基于知识的问题的答案，否则这些问题需要人工输出进行仔细研究，例如客观和可重复地评估字符和字符状态的相关性，独立性和独特性，只需几行代码。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专有技术，包括与本体相关的表型数据的综合知识库，量化表型描述语义相似性的指标，以及从已发表的性状描述中合成形态数据的算法。为了推动计算基础设施的发展，并展示其使能价值，该项目的目标集中在解决三个具体的长期需求，其中领域知识计算的困难是主要障碍：（1）计算合成，校准和评估跨研究的形态特征矩阵;（2）将本体提供的形态学领域知识客观地、可再现地融入性状进化的进化模型中;以及（3）通过将语义表型结合到祖先状态重建中并识别与分支或进化枝比偶然预期的更频繁。此外，为了更好地为进化生物学家用户和比较分析工具的开发人员做好准备，以采用这些新功能，将开发和教授针对特定领域的必要知识表示和计算推理技术短期课程。有关该项目的更多信息，请访问http://cate.phenoscape.org/。

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/.