removed mention of RML mappings example

docs/modules/ROOT/pages/how-to-map-existing-data-models.adoc

@@ -198,10 +198,9 @@ The technical mapping step is a critical phase in the mapping process, serving a
 
 When it comes to representing these mappings technically, several technology options are available[https://ceur-ws.org/Vol-2489/paper4.pdf[ref]]:  such as XSLT[ref], RML[ref], SPARQLAnything[ref], etc. But RDF Mapping Language (RML) [https://rml.io/specs/rml/[ref]] stands out for its effectiveness and straightforward approach. RML allows for the representation of mappings from heterogeneous data formats like XML, JSON, relational databases and CSV into RDF, supporting the creation of semantically enriched data models. This code can be expressed in Turtle RML or the YARRRML dialect [https://rml.io/yarrrml/spec/[ref]], a user-friendly, text-based format based on YAML, making the mappings accessible to both machines and humans. RML is well-supported by robust implementations such as RMLMapper [https://github.com/RMLio/rmlmapper-java[ref]] and RMLStreamer [https://github.com/RMLio/RMLStreamer[ref]], which provide robust platforms for executing these mappings. RMLMapper is adept at handling batch processing of data, transforming large datasets efficiently. On the other hand, RMLStreamer excels in streaming data scenarios, where data needs to be processed in real-time, providing flexibility and scalability in dynamic environments.
 
-The development of the mapping rules is straightforward due to the preliminary conceptual mapping that is already available. The Conceptual Mapping (CM) aided the understanding to which class and property each XML element be mapped and how. Then, RML mapping statements are created for each class of the target ontology coupled with the property-object mapping statements specific to that class. Furthermore, it is essential to master RML along with XML technologies like XSD, XPath, and XQuery to implement the mappings effectively [https://rml.io/docs/rml/tutorials/xml/[ref]].  +
-An additional step involves deciding on a URI creation policy and designing a uniform scheme for use in the generated data, ensuring consistency and coherence in the data output.
+The development of the mapping rules is straightforward due to the preliminary conceptual mapping that is already available. The Conceptual Mapping (CM) aided the understanding to which class and property each XML element be mapped and how. Then, RML mapping statements are created for each class of the target ontology coupled with the property-object mapping statements specific to that class. Furthermore, it is essential to master RML along with XML technologies like XSD, XPath, and XQuery to implement the mappings effectively [https://rml.io/docs/rml/tutorials/xml/[ref]].
 
-An advanced application example of RML technology is the extensive mapping [https://github.com/OP-TED/ted-rdf-mapping[ref]] of public procurement notices published in the Official Journal of the European Union [https://ted.europa.eu/en/[ref]] to eProcurement Ontology[https://docs.ted.europa.eu/epo-home/index.html[ref]].
+An additional step involves deciding on a URI creation policy and designing a uniform scheme for use in the generated data, ensuring consistency and coherence in the data output.
 
 A viable alternative to RML is XSLT technology, which offers a powerful, but low-level method for defining technical mappings. While this method allows for high expressiveness and complex transformations, it also increases the potential for errors due to its intricate syntax and operational complexity. This technology excels in scenarios requiring detailed manipulation and parameterization of XML documents, surpassing the capabilities of RML in terms of flexibility and depth of transformation rules that can be implemented. However, the detailed control it affords means that developers must have a high level of expertise in semantic technologies and exercise caution and precision to avoid common pitfalls associated with its use.