A Two-Stage Approach towards Generalization in Knowledge Base Question Answering

• URL: http://arxiv.org/abs/2111.05825v1
• Date: Wed, 10 Nov 2021 17:45:33 GMT
• Title: A Two-Stage Approach towards Generalization in Knowledge Base Question Answering
• Authors: Srinivas Ravishankar, June Thai, Ibrahim Abdelaziz, Nandana Mihidukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossilleo, Achille Fokoue
• Abstract summary: We introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG)
• Score: 4.802205743713997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).

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