CRII: III: Knowledge Graph Completion with Transferable Representation Learning

知识图(KGs)提供了开放世界和特定领域的知识表示，这些知识表示是许多人工智能(AI)系统不可或缺的。然而，建造KGS通常非常昂贵，需要大量的人力。虽然表示学习通过自动推断嵌入空间中缺失的知识来提供解决方案，但知识表示是独立构建的，从而丢失了在其他知识表示中可用的补充知识。事实上，独立创造的知识往往是跨不同视角相互关联的。例如，在英文KG中，关于《源氏物语》(最古老的日本小说)的知识可能会被日语KG中容易获得的补充知识所丰富；在蛋白质组学领域，对蛋白质相互作用的验证也与对由基因本体论注释的蛋白质的基因组功能的研究平行。特别是，本项目研究了知识群体的可转移表征学习的新方向，它试图利用最少的监督将来自不同孤立来源的相互关联的知识关联到共同的嵌入方案中，并允许互补的知识在不同的知识群体之间轻松迁移。该项目的成果将为不同来源、领域和语言创建通用的知识表示，从而支持跨域或低资源决策的应用程序。这一成果将广泛提高知识在计算研究中的效用，包括人工智能、自然语言理解(NLU)和推荐系统，以及生物学、药理学和社会科学的跨学科研究。该项目的目标是开发新的数据驱动的机器学习方法，用于自动完成KG。这种方法将能够自动组合孤立的KG，允许互补的知识在它们之间转移，并基于已知的知识有效地推断全局缺失的知识。该项目将系统地解决利用附带和辅助监督信号捕获各种类型的知识关联的几个关键技术挑战。此外，它将开发技术，以支持具有噪声意识的元学习和受限推理的多源知识转移环境中的稳健推理，特别是为低资源领域或语言精选的知识。所调查的技术将在各种KG完井任务以及NLU、生物信息学和医学信息学领域的几项下游任务中进行评估。该项目的成果将通过发表论文、发布开源软件和学习资源、组织教程和研讨会以及创建关于知识库建设和自然语言处理的新课程来传播。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Knowledge graphs (KGs) provide both open-world and domain-specific knowledge representations that are integral to many artificial intelligence (AI) systems. However, constructing KGs is usually very costly and requires extensive human effort. While representation learning offers a solution by automatically inferring missing knowledge in the embedding space, KGs are constructed independently thereby missing complementary knowledge available in other KGs. In fact, the independently created knowledge is often interrelated across different perspectives. For example, knowledge about The Tale of Genji (the oldest Japanese novel) in an English KG may be enriched with complementary knowledge readily available in a Japanese KG; in the proteomics domain, verification of Protein-protein interaction is also in parallel to the study of the genomic functions of the proteins that are annotated by the Gene ontologies. In particular, this project studies a novel direction of transferable representation learning for KGs, which seeks to associate the interrelated knowledge from different isolated sources in a common embedding scheme using minimal supervision, and allowing complementary knowledge to easily migrate across different KGs. The outcome of this project will create universal knowledge representation for different sources, fields and languages, therefore supporting applications with cross-domain or low-resource decision making. The outcome will broadly improve the utility of the knowledge in computational research including AI, natural language understanding (NLU) and recommender systems, as well as interdisciplinary research in biology, pharmacology and social sciences.The goals of this project are to develop new data-driven machine learning methods for automatic KG completion. Such a method will be able to automatically combine isolated KGs, allow complementary knowledge to transfer across them, and efficiently infer globally missing knowledge based on what is known. This project will systematically solve several key technical challenges of leveraging incidental and auxiliary supervision signals to capture various types of knowledge association. Furthermore, it will develop technologies to support robust inference in a multi-source knowledge transfer setting with noise-aware meta-learning and constrained inference, particularly for knowledge curated for low-resource domains or languages. The investigated technologies will be evaluated on various KG completion tasks, and several downstream tasks in areas of NLU, bioinformatics and medical informatics. The results of the project will be disseminated by publishing papers, releasing open-source software and learning resources, organizing tutorials and workshops, and creating new courses on knowledge base construction and natural language processing.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.