III: Small: Geometric Constraint based Concept Keyword Embedding for Domain-neutral Knowledge Graph Construction

许多知识驱动的应用程序，包括问答系统和基于人工智能的智能个人助理，都严重依赖于高质量知识图的可用性。然而，现有的知识图谱存在许多局限性，包括缺乏完整性和过度需要人工干预。该建议的目标是通过将文本文档中实体的句子上下文与现有知识库中实体的关系上下文相结合，通过学习实体和关系的有意义表示向量来克服这些限制。另一个目标是开发从自然语言文本中自动提取实体对及其关系的方法。所提出的方法的主要智力优点是表征学习方法的设计，该方法可以对嵌入向量施加几何约束以捕获底层语义；换句话说，使用所提出的方法，学习向量的配置将捕获实体之间的各种语义关系，包括层次关系、组合关系和顺序关系，从而为实体产生高质量的表示向量，从而产生高质量的知识图。所提出的方法的验证将通过建立所有计算机科学的活知识图来执行。就更广泛的影响而言，所提出的方法将有利于任何信息量大的学科，对这些学科来说，知识图谱的构造和时间演化是至关重要的。此外，这对计算机科学作为一门学科也有直接的好处，因为在一个活的、百科全书式的知识图谱中组织和总结计算机科学中所有已知和新兴的概念，将有助于更好地组织新知识，并从现有文献中更好地搜索相关作品。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many knowledge-driven applications, including question answering systems and AI based intelligent personal assistants depend critically on the availability of high quality knowledge graphs. However, existing knowledge graphs suffer from many limitations, including lack of completeness, and excessive need for human intervention. The objective of this proposal is to overcome these limitations by learning meaningful representation vectors for the entities and the relationships by combining sentential context of entities from text documents with the relational context of entities from existing knowledge bases. Another objective is to develop methodologies for automatic extraction of entity-pairs and their relationships from natural language text.The main intellectual merit of the proposed methodologies is the design of representation learning methodologies which can impose geometric constraints on the embedding vectors for capturing the underlying semantics; in other words, using the proposed method, the disposition of the learned vectors would capture various semantic relationships among the entities, including hierarchical, compositional, and sequential, which would yield high quality representation vectors for the entities, leading to high-quality knowledge- graphs. Validation of the proposed methods will be performed by building a living knowledge graph of all of computer science. In terms of broader impact, the proposed methods would benefit any information-heavy disciplines, for which, the construct and temporal evolution of a knowledge graph are critical. Also, there is a direct benefit to computer science as a discipline, since organizing and summarizing all the known and emerging concepts in computer science within a living and encyclopedic knowledge graph will enable better organization of new knowledge, and better search of related works from the existing literature.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.