  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.
