Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
This work addresses relation linking for knowledge base question answering systems, offering a novel integration of methods to improve accuracy, though it is incremental in combining existing techniques.
The paper tackles the challenges of relation linking in knowledge base question answering, such as natural language ambiguity and limited training data, by proposing SLING, a framework that uses semantic parsing with AMR and distant supervision, achieving state-of-the-art performance on QALD-7, QALD-9, and LC-QuAD 1.0 benchmarks.
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.