This paper describes the use of continuous vector space models for reasoning with a formal knowledge base. The practical significance of these models is that they support fast, approximate but robust inference and hypothesis generation, which is complementary to the slow, exact, but sometimes brittle behavior of more traditional deduction engines such as theorem provers. The paper explains the way logical connectives can be used in semantic vector models, and summarizes the development of Predication-based Semantic Indexing, which involves the use of Vector Symbolic Architectures to represent the concepts and relationships from a knowledge base of subject-predicate-object triples. Experiments show that the use of continuous models for formal reasoning is not only possible, but already demonstrably effective for some recognized informatics tasks, and showing promise in other traditional problem areas. Examples described in this paper include: predicting new uses for existing drugs in biomedical informatics; removing unwanted meanings from search results in information retrieval and concept navigation; type-inference from attributes; comparing words based on their orthography; and representing tabular data, including modelling numerical values. The algorithms and techniques described in this paper are all publicly released and freely available in the Semantic Vectors open-source software package.

中文翻译：

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使用向量推理：快速鲁棒推理的连续模型。


本文描述了使用连续向量空间模型通过形式知识库进行推理。这些模型的实际意义在于，它们支持快速、近似但稳健的推理和假设生成，这与定理证明器等更传统的演绎引擎的缓慢、精确但有时脆弱的行为是互补的。本文解释了逻辑连接词在语义向量模型中的使用方式，并总结了基于谓词的语义索引的发展，其中涉及使用向量符号架构来表示主谓宾知识库中的概念和关系三倍。实验表明，使用连续模型进行形式推理不仅是可能的，而且对于一些公认的信息学任务已经证明是有效的，并且在其他传统问题领域也显示出了前景。本文描述的例子包括：预测现有药物在生物医学信息学中的新用途；从信息检索和概念导航的搜索结果中删除不需要的含义；从属性进行类型推断；根据拼字法比较单词；并表示表格数据，包括建模数值。本文描述的算法和技术均已公开发布，并可在 Semantic Vectors 开源软件包中免费获取。