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.

中文翻译：

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利用语义解析在知识库上建立关系链接

知识库问答系统严重依赖关系提取和链接模块。然而，从文本到知识库提取和链接关系的任务面临两个主要挑战：自然语言的歧义和缺乏训练数据。为了克服这些挑战，我们提出了 SLING，这是一种关系链接框架，它利用抽象含义表示 (AMR) 和远程监督来利用语义解析。SLING 集成了多种关系链接方法，可捕获互补信号，例如语言线索、丰富的语义表示和来自知识库的信息。使用三个KBQA数据集进行关系链接的实验；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。