Analyzing Embedded Semantic With Json-Ld And Microdata For Educational Resources In Large Scale Web Datasets

The use of embedded markup for semantic web annotations has been fostered in the last years to produce structured information and improve visualization of search results. Its use enables major search engines to interpret and exhibit describing data from web content. This paper presents a quantitative analysis of the deployment of widely use markup formats, JSON-LD and Microdata, conducted on datasets from a large web crawliuig corpus of 2018. It is focusing on the use of Schema vocabulary applied to describe educational resources. The results show that Microdata largely predominates over JSON-LD encoding. This fmding was not expected because Microdata is not a W3C recommendation, while, JSON-LD is such since 2014. Further, the analysis reveals a low use of Schema specific properties to describe educational resources, which could indicate a lack of interest in using markup technology in this field.