论文标题
利用语义解析以通过知识基础链接的关系
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
论文作者
论文摘要
知识库问答系统在很大程度上取决于关系提取和链接模块。但是，提取和联系从文本与知识基础联系的任务面临两个主要挑战。自然语言的歧义和缺乏培训数据。为了克服这些挑战，我们提出了吊索，这是一个关系链接框架，该框架利用抽象含义表示（AMR）和遥远的监督来利用语义解析。吊索整合了多种关系链接方法，这些方法捕获了互补信号，例如语言提示，丰富的语义表示以及来自知识基础的信息。使用三个KBQA数据集链接的关系实验； Qald-7，Qald-9和LC-Quad 1.0表明，所提出的方法在所有基准测试中都达到了最先进的性能。
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowledgebases faces two primary challenges; the ambiguity of natural language and lack of training data. To overcome these challenges, we present SLING, a relation linking framework which leverages semantic parsing using Abstract Meaning Representation (AMR) and distant supervision. SLING integrates multiple relation linking approaches that capture complementary signals such as linguistic cues, rich semantic representation, and information from the knowledgebase. The experiments on relation linking using three KBQA datasets; QALD-7, QALD-9, and LC-QuAD 1.0 demonstrate that the proposed approach achieves state-of-the-art performance on all benchmarks.
