Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion.Most previous reasoning methods are designed for dense KGs with enough paths between entities, but cannot work well on those sparse KGs that only contain sparse paths for reasoning.On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths.On the other hand, the lack of evidential paths to target entities also makes the reasoning process difficult.To solve these problems, we propose a multi-hop reasoning model named DacKGR over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embeddingbased models to make our model perform more potential path search over sparse KGs.(2) Based on the anticipation information, the completion strategy dynamically adds edges as additional actions during the path search, which further alleviates the sparseness problem of KGs.The experimental results on five datasets sampled from Freebase, NELL and Wikidata show that our method outperforms state-of-the-art baselines.Our codes and datasets can be obtained from https:// github.com/THU-KEG/DacKGR.

Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object. To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit the global KG structure. Afterward, the aligned entities are identified based on their distances. In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment. Specifically, a global structure and local semantics preserving network is proposed to learn entity representations in a coarse-to-fine manner. Experiments on several real-world datasets show that our method significantly outperforms other entity alignment approaches and achieves the new state-of-the-art performance.

Entity Resolution (ER) identifies records from different data sources that refer to the same real-world entity. Conventional ER approaches usually employ a structure matching mechanism, where attributes are aligned, compared and aggregated for ER decision. The structure matching approaches, unfortunately, often suffer from heterogeneous and dirty ER problems. That is, entities from different data sources are described using different schemas, and attribute values may be misplaced, missing, or noisy. In this paper, we propose a deep sequence-to-sequence entity matching model, denoted Seq2SeqMatcher, which can effectively solve the heterogeneous and dirty problems by modeling ER as a token-level sequence-to-sequence matching task. Specifically, we propose an align-compare-aggregate neural network for Seq2Seq entity matching, which can learn the representations of tokens, capture the semantic relevance between tokens, and aggregate matching evidence for accurate ER decisions in an end-to-end manner. Experimental results show that, by comparing entity records in token level and learning all components in an end-to-end manner, our Seq2Seq entity matching model can achieve remarkable performance improvements on 9 standard entity resolution benchmarks.

Entity resolution (ER) aims to identify data records referring to the same real-world entity. Due to the heterogeneity of entity attributes and the diversity of similarity measures, one main challenge of ER is how to select appropriate similarity measures for different attributes. Previous ER methods usually employ heuristic similarity selection algorithms, which are highly specialized to specific ER problems and are hard to be generalized to other situations. Furthermore, previous studies usually perform similarity learning and similarity selection independently, which often result in error propagation and are hard to be optimized globally. To resolve the above problems, this paper proposes an end-to-end multi-perspective entity matching model, which can adaptively select optimal similarity measures for heterogenous attributes by jointly learning and selecting similarity measures in an end-to-end way. Experiments on two real-world datasets show that our method significantly outperforms previous ER methods.