Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases (2020)

Abstract: Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths. The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over few-shot relations (with limited triples) which are common in knowledge base. To this end, we propose FIRE, a novel few-shot multi-hop relation learning model. FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning. The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations. Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.

## introduction nowadays large scale knowledge bases (kb), e.g., nell @xcite or freebase @xcite , are serving as useful resources for many natural language processing applications such as semantic search or question answering. due to the nature of incompleteness @xcite , it is essential to automate the kb completion. one typical problem is fact (triple) prediction. for example, given a query "what is the nationality of barack obama?" denoted as (barack obama, nationality, ?), the task is to infer usa as the answer. there have been a lot of work for solving this problem by embedding learning approaches @xcite @xcite or deep learning models @xcite . the fact prediction ignores the compositional relations in kb and results answer that lacks of interpretation. accordingly, an alternative problem, multi-hop relation reasoning, was presented. the task is to infer facts using multi-hop reasoning paths, e.g., (barack obama, bornin, hawaii) ∧ (hawaii, locatein, usa) → (barack obama, nationality, usa). a number of recent models @xcite @xcite formulate the problem as sequential decision process and leverage reinforcement learning to achieve considerable performance. most current multi-hop relation reasoning models require a good amount of training data (fact triples) for each query relation. however, the relation frequency distribution in kb is usually longtail @xcite , showing that a large portion of relations only have few-shot fact triples for model training. despite that some few-shot relation learning methods @xcite @xcite have been proposed recently, they target at fact prediction only or their performance is suboptimal due to deficiency in capturing heterogeneous structural information and pruning search space in kb. in this work, we aim at addressing the fewshot challenge and improving relation reasoning performance. in particular, we propose a novel model called fire for few-shot multi-hop relation learning over kb. fire utilizes on-policy reinforcement learning to model the sequential steps of multi-hop reasoning, encodes entity embedding using heterogeneous structural information, and prunes the reasoning search space using knowledge graph embedding. the meta-learning based optimization procedure is further employed to learn model parameters that could be fast adapted for few-shot relations. to summarize, our main contributions are: (1) we study the problem of few-shot multi-hop relation reasoning over kb, which is new and important; (2) we propose a novel model called fire to solve the problem by exploring several beneficial components; (3) we conduct experiments on two datasets and the evaluation results demonstrate the superior performance of fire over state-of-theart methods. ## approach in this section, we first define the problem of fewshot multi-hop relation reasoning in knowledge bases, then present the fire model to solve it. ## experiments in this section, we conduct experiments on different datasets to show model performance and related analytic study. ## related work this work is closely related to relation reasoning in knowledge bases and few-shot learning.