论文标题
探索SPARQL模式组成的顺序到序列模型
Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition
论文作者
论文摘要
作为结构化和非结构化的数据，不断地将大量信息添加到Internet中，供应知识库，例如DBPEDIA和WIKIDATA，并描述了数十亿个陈述，描述了数百万个实体。问答系统的目的是允许外行用户使用自然语言访问此类数据，而无需编写正式查询。但是，用户通常会提交复杂的问题，需要一定程度的抽象和推理将其分解为基本的图形模式。在这篇简短的论文中，我们探讨了基于神经机器翻译的架构的使用，称为神经Sparql机器来学习模式组成。我们表明，序列到序列模型是将长话语转换为复杂的SPARQL查询的可行且有前途的选择。
A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. However, users often submit questions that are complex and require a certain level of abstraction and reasoning to decompose them into basic graph patterns. In this short paper, we explore the use of architectures based on Neural Machine Translation called Neural SPARQL Machines to learn pattern compositions. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries.
