A Simple Approach to Case-Based Reasoning in Knowledge BasesDownload PDF

Feb 14, 2020 (edited Jul 19, 2020)AKBC 2020 Conference Blind SubmissionReaders: Everyone
  • Keywords: case based reasoning, non-parametric reasoning, knowledge base completion
  • TL;DR: Learn to answer a query about an entity by gathering reasoning paths from other similar entities in the Knowledge Base
  • Subject Areas: Knowledge Representation, Semantic Web and Search, QuestionAnswering and Reasoning, Relational AI
  • Archival Status: Archival
  • Abstract: We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI). Consider the task of finding a target entity given a source entity and a binary relation. Our approach finds multiple \textit{graph path patterns} that connect similar source entities through the given relation, and looks for pattern matches starting from the query source. Using our method, we obtain new state-of-the-art accuracy, outperforming all previous models, on NELL-995 and FB-122. We also demonstrate that our model is robust in low data settings, outperforming recently proposed meta-learning approaches.
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