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Multi-hop knowledge graph question answering (KGQA) targets at pinpointing the answer entities to a natural language question by reasoning across multiple triples in knowledge graphs (KGs). When faced with multi-hop questions, existing methods take the whole relation paths into consideration, whereas the number of candidate paths grows exponentially with the increasement of path length, resulting in high search space complexity. Meanwhile, due to the complex semantic information, it is important to focus on different parts of the question at different steps. Moreover, previous studies only give the predicted answers but lack a relational path to indicate the reasoning process. To address these challenges, this paper proposes an interpretable neural model for multi-hop KGQA, namely Dynamic Reasoning Network (DRN). Inspired by human's hop-by-hop reasoning behavior, DRN employs an interpretable, stepwise reasoning process to predict a relation at each step, all the intermediate relations form a traceable reasoning path. With effectively stepwise path search over KGs, DRN reduces the search space significantly. Furthermore, to facilitate semantic parsing, DRN dynamically updates the representation of relations and questions for each step based on attention mechanism. Extensive experiments conducted over four benchmark datasets from football, movie and general domain well demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Entity alignment refers to matching entities with the same realistic meaning in different knowledge graphs. The structure of a knowledge graph provides the global signal for entity alignment. But in the real world, a knowledge graph provides insufficient structural information in general. Moreover, the problem of knowledge graph heterogeneity is common. The semantic and string information can alleviate the problems caused by the sparse and heterogeneous nature of knowledge graphs, yet both of them have not been fully utilized by most existing work. Therefore, we propose an e ntity a lignment model based on m ultiple i nformation (EAMI), which employs structural, semantic and string information. EAMI learns the structural representation of a knowledge graph by using multi-layer graph convolutional networks. To acquire more accurate entity vector representation, we incorporate the attribute semantic representation into the structural representation. In addition, to further improve entity alignment, we study the entity name string information. There is no training required to calculate the similarity of entity names. Our model is tested on publicly available cross-lingual datasets and cross-resource datasets, and the experimental results demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Multi-hop knowledge graph question answering targets at pinpointing the answer entities by inferring across multiple triples in knowledge graphs. To enhance model interpretability, path-based methods are proposed. Specifically, with the advances of deep reinforcement learning (DRL), this paper explores to extend the line of RL-based approaches. However, existing solutions suffer from the issue of spurious paths. A major reason lies in that the agent takes an opportunistic way to explicitly pursue the predictive accuracy of answer entities instead of considering correct reasoning paths. To overcome this challenge, our idea is inspired by adversarial learning and we expect that a discriminator could effectively distinguish whether the reasoning chain is correct or not. To this end, we propose an interpretable reasoning method based on adversarial reinforcement learning for multi-hop KGQA, namely A dversarial R einforcement R easoning N etwork (AR2N). AR2N consists of two crucial components: an answer generator (i.e., policy network of RL) and a path discriminator. By alternately updating two components in an adversarial manner, the generator is able to infer answer entities by following the correct reasoning chain, the discriminator is capable of evaluating the plausibility of reasoning paths. Extensive experiments conducted over three benchmark datasets well demonstrate the effectiveness of our method. 1 1 For reproducibility, we publicly release the code at https://github.com/iDylanCui/AR2N. • The agent takes an opportunistic way to explicitly pursue predictive accuracy. • The generator infers answer entities by following the correct reasoning chain. • The discriminator is capable of evaluating the plausibility of reasoning paths. • RL-based models enhance the interpretability of reasoning process. [ABSTRACT FROM AUTHOR]