With the advance of the Semantic Web and the Open Linked Data initiatives, a huge quantity of RDF data is available on the Web. The goal is to make this data readable for humans and machines, adopting special formats and connecting them by using International Resource Identifiers (IRIs), which are abstractions of real resources of the world. As more data is published and shared, sensitive information could be also provided. In consequence, the protection of entities of interest (e.g., people, companies) is a real challenge, requiring adequate techniques to avoid the disclosure/production of sensitive information on the Web. Three main aspects are considered to ensure entity protection: (i) Pre- serving information, by identifying and treating the data that can disclose entities (e.g., identifiers, quasi-identifiers); (ii) Identifying the utility of the data to be published (e.g., statistics, testing, research) to adopt an adequate; and (iii) Modeling background knowledge that can be used for adversaries (e.g., number of relationships, a specific relationship, information of a node) to dis- cover sensitive information. Anonymization is one technique for entity protection that has been successfully applied in practice [RGCGP15]. However, studies regarding anonymization in the context of RDF documents, are really limited, showing practical anonymization approaches for simple scenarios as the use of generalization and suppression operations based on hierarchies. Moreover, the complexity of the RDF structure requires a high interaction of the expert user to identify and select the RDF’s elements to be protected (main entities), and the ones related to them (identifiers, quasi-identifiers, sensitive information, and unsensitive information). Additionally, the similarity among entities to discover similar data in other datasets, is compromised by disjoint similarities (e.g., the similarity be- tween float and double is 0 for literal nodes). In literal nodes, datatypes play an important role, since it has been proven in the literature that the presence of datatypes, constraints, and annotations improves the similarity among XML documents (up to 14%). RDF adopts the datatypes from XML Schema, which are defined by the W3C. Thus, in this context, the contributions are summarized as follows: • An analysis of datatypes in the context of RDF matching/integration documents, its limitations and adequate applicability for the Semantic Web; • An extended version of the W3C datatype hierarchy, where a parent-child relationship expresses subsumption (parent subsumes children); • A new similarity measure for datatypes to take into account several aspects related to the new hierarchical relations among compared datatypes such as: distance and depth among datatypes, similar children; • A new inference datatype approach to deduce simple datatypes based on four steps: (i) an analysis of predicate information, (ii) an analysis of lexical space values, (iii) a semantic analysis of the predicate, and (iv) a generalization of Numeric and Binary datatypes; • A method to reduce the complexity of the RDF structure of the data to be published, simplifying the task of analysis, which is performed by the expert user; • A method to suggest disclosure sources to the expert user, based on a node similarity, reducing the task of data classification; and • A protection method, based on a generalization operation, to decrease the relations among resources from different datasets, to preserve the main objectives of integration and combination of the Semantic Web. The different proposals have been tested through experimentation. Experimental results are satisfactory and show an important improvement in the accuracy and high performance for similarity and inference datatype approaches with respect to the existing works. Our protection approach for RDF data overcomes the related work and decreases the expert user interaction.