Linked Data (LD) is supplementing the World Wide Web of documents with a Web of data. This is becoming apparent from the number of LD repositories available as part of the Linked Open Data (LOD) cloud. The inference of structure for linked data sources is a crucial task in this context. It enables the efficient querying and integration of data from multiple sources. The structure inference process involves identifying the relationships between entities and attributes in the data, and representing them in a meaningful way. This allows for the creation of a unified view of the data, making it easier to analyze and understand. The structure inference for linked data sources is a complex task, requiring the use of advanced techniques and algorithms. It is an active area of research, with many challenges and opportunities for innovation. The development of efficient and effective structure inference methods is essential for the widespread adoption of linked data technologies.