With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

This paper focuses on the application of possibilistic logic in the extension of ontology for uncertain knowledge description. It proposes an uncertain semantic relationship description method based on possibilistic logic and probabilistic statistics. This novel method combines the advantages of subjective valuation and objective statistics, which makes the representation and inference of uncertainty in ontologies more flexible and reasonable. Finally, we apply this quantitative measurement to describe incomplete (uncertain) knowledge and imprecise (vague) information of uncertain knowledge in ontology knowledgebase.

Engineering Education Accreditation has been widely accepted and recognized by most engineering colleges all over the world based on the idea of Outcome Based Education (OBE). However, current implementation and management methods are of low efficiency and heavy workload. The procedure of engineering education accreditation should not be a temporary or assault work, while it should be periodic and continuous along with regular teaching works. Based on modern information management technology, an assistant information management system is requisite in the procedure of