Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

• URL: http://arxiv.org/abs/2009.07726v1
• Date: Wed, 16 Sep 2020 14:56:11 GMT
• Title: Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
• Authors: Nandana Mihindukulasooriya, Gaetano Rossiello, Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Mo Yu, Alfio Gliozzo, Salim Roukos and Alexander Gray
• Abstract summary: We present SLING, a relation linking framework which leverages semantic parsing using 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. 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.
• Score: 80.99588366232075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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