Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies ( $GDD^{x}$ ), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined $GDD^{x}\text{s}$ . Finally, we evaluate our synthetic KG generator and compare with several state-of-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph’s schema.

In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

A knowledge graph serves as a unified and standardized representation for extracting and representing textual information. In the field of knowledge extraction and representation research, named entity recognition and relation extraction provide effective solutions for knowledge graph generation tasks. However, it is a challenge that lies in extracting domain-specific knowledge from the rich and general textual corpora and generating corresponding domain knowledge graphs to support domain-specific reasoning, question-answering, and decision-making tasks. The hierarchical domain knowledge representation model (i.e., domain ontology) provides a solution for this problem. Therefore, we propose an end-to-end approach based on domain ontology embedding and pre-trained language models for domain knowledge graph generation from text, which incorporates domain node recognition and domain relation extraction phases. We evaluated our domain ontology-driven model on the Wikidata-TekGen dataset and the DBpedia-WebNLG dataset, and the results indicate that our approach based on the pre-trained language models with fewer parameters compared with the baseline models has significantly contributed to the domain knowledge graph generation without prompts.