一种面向大规模数据的张量化逻辑程序设计语言
A tensorized logic programming language for large-scale data
摘要：
介绍了一种基于张量嵌入的逻辑程序设计语言T-PRISM。我们的嵌入方案是对最先进的概率逻辑程序设计语言之一PRISM中的分布语义的修改，通过用多维数组替换分布函数，即，张量T-PRISM由逻辑程序设计和数值计算两部分组成。前者在一阶逻辑级别提供灵活和可解释的建模，后者利用GPU的并行化和硬件加速提供可扩展的计算。结合这两部分提供了从符号推理到深度学习的非常广泛的高级声明性建模。为了体现这种程序设计语言，我们还介绍了一种新的语义，称为张量语义，它结合了传统的最小模型语义在逻辑编程与张量的嵌入。在T-PRISM中，我们首先使用逻辑推理从给定的程序中导出一组与张量相关的方程，即，在符号空间中执行Prolog，然后在连续空间中通过TensorFlow求解导出的方程。使用我们的初步实施T-PRISM，我们已经成功地处理了广泛的建模。我们已经成功地处理了真正的大规模数据的声明式建模。本文提出了一种基于FB 15 k和WN 18数据集的知识图谱DistMult模型。
英文摘要：
We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of-the-art probabilistic logic programming languages, by replacing distribution functions with multidimensional arrays, i.e., tensors. T-PRISM consists of two parts: logic programming part and numerical computation part. The former provides flexible and interpretable modeling at the level of first order logic, and the latter part provides scalable computation utilizing parallelization and hardware acceleration with GPUs. Combing these two parts provides a remarkably wide range of high-level declarative modeling from symbolic reasoning to deep learning. To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors. In T-PRISM, we first derive a set of equations related to tensors from a given program using logical inference, i.e., Prolog execution in a symbolic space and then solve the derived equations in a continuous space by TensorFlow. Using our preliminary implementation of T-PRISM, we have successfully dealt with a wide range of modeling. We have succeeded in dealing with real large-scale data in the declarative modeling. This paper presents a DistMult model for knowledge graphs using the FB15k and WN18 datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
