Fuzzy Event Knowledge Graph Embedding Through Event Temporal and Causal Transfer

An event refers to an occurrence that occurs at a specific place, time, and event type and has a significant impact on our society or nature. Uncertain event knowledge also exists widely in the real world. Knowledge graph uses a resource description framework to describe deterministic and static knowledge in the real world. And knowledge graph embedding can embed the nodes and relations in the knowledge graph into vector space to represent more data features. However, real-world knowledge is often dynamic and uncertain, and these knowledge also changes with the state of events. Existing knowledge graph embedding models cannot solve the uncertainty and dynamically changing information of events in this embedding framework. To solve this problem, we study an embedding model for uncertain, dynamic state representation and propose a strongly adaptive fuzzy event embedding model (FERKE). Specifically, we first propose a fuzzy event embedding model that combines coarse-grained and fine-grained fuzziness, which provides an underlying representation framework for FERKE. Then, in the hyperbolic embedding space, the nodes and relations in the fuzzy event graph and the fine-grained fuzziness of each element are, respectively, embedded. FERKE utilizes the information aggregation and transmission of heterogeneous graph neural network to capture the rich interactions between fuzzy events and entity knowledge. We conduct an experimental evaluation of FERKE based on the constructed fuzzy event dataset. The results show that the FERKE proposed in this study is better than existing methods and can handle complex fuzzy event knowledge.