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

地球上数以百万计的物种都有独特的生物特征，使它们能够成功地在生态位中竞争或适应生态位。因此，准确地确定这些特征是如何进化的，是理解地球生物多样性的基础，也是预测地球生物多样性在未来如何随着生态系统的变化而变化的基础。尽管存在比较复杂的分析方法和工具来分析特征，但将它们的全部功能应用于以自然语言描述形式记录的无数特征观察一直受到阻碍，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隐含的最基本事实。由于最近在知识表示和机器推理领域的一些突破，克服这一挑战所需的技术库现在原则上是可用的，但这些技术在部署、编排和使用方面具有足够的挑战性，对大多数工具来说，有效利用它们的障碍仍然太高。该项目将创建基础设施，大大减少这一障碍，其目标是提供比较特征分析工具，使其易于访问由机器推理和从特征描述的含义推断的算法。就像b谷歌、IBM Watson和其他公司让智能手机应用开发者只需要几行代码就能将复杂的机器学习和人工智能功能（如情感分析）整合在一起一样，这个项目将展示知识计算如何为分析、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三个长期存在的局限性来做到这一点：重组性状数据，建立性状进化模型，并为性状适应的驱动因素产生可测试的假设。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，但通过现代计算数据科学分析充分利用这些数据仍然受到将数据与积累的形态学知识体以机器可以随时采取行动的形式连接起来的陡峭障碍的严重阻碍。该项目旨在通过创建一个集中的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，从而使比较分析工具的开发人员及其用户能够利用机器推理能力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，工具可以通过编程获得基于知识的问题的答案，否则这些问题需要人工仔细研究，例如，仅用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、独立性和独特性。为了实现这一目标，该项目将采用phenscape项目开发的关键产品和专有技术，包括本体关联表型数据的综合知识库，用于量化表型描述语义相似性的度量标准，以及用于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实现价值，该项目的目标侧重于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，校准和评估来自不同研究的形态特征矩阵；(2)客观、可复制地将本体提供的形态领域知识纳入性状进化模型；(3)通过将语义表型整合到祖先状态重建中，并识别与分支或分支的进化变化相关的领域本体概念，为适应性多样化产生可测试的假设。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准备采用这些新功能，将开发和教授一门针对必要知识表示和计算推理技术的领域定制短期课程。关于这个项目的更多信息可以在http://cate.phenoscape.org/上找到。

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/.