Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

Abstract

Question answering (QA) over real-world knowledge bases (KBs) is challenging because of the diverse (essentially unbounded) types of reasoning patterns needed. However, we hypothesize in a large KB, reasoning patterns required to answer a query type reoccur for various entities in their respective subgraph neigborhoods. Leveraging this structural similarity between local neighborhoods of different subgraphs, we introduce a semiparametric model with (i) a nonparametric component that for each query, dynamically retrieves other similar k-nearest neighbor (KNN) training queries along with query-specific subgraphs and (ii) a parametric component that is trained to identify the (latent) reasoning patterns from the subgraphs of KNN queries and then apply it to the subgraph of the target query. We also propose a novel algorithm to select a query-specific compact subgraph from within the massive knowledge graph (KG), allowing us to scale to full Freebase KG containing billions of edges. We show that our model answers queries requiring complex reasoning patterns more effectively than existing KG completion algorithms. The proposed model outperforms or performs competitively with state-of-the-art models on several KBQA benchmarks.

Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning

“…Case-based reasoning mechanisms can be effectively applied in product redesign activities by retrieving the historical design solutions most similar to new design requirements [1]. The retrieved solutions can then be further modified to fit to the new design requirements [2].…”