A method for generating a knowledge data model is provided. The method includes providing at least one initial set of semantic type entities of a specific semantic type. The initial set of semantic type entities is expanded using available mappings between entities of the initial set and entities of unspecified type to generate an extended set of semantic type entities. Entities of a same semantic type are clustered within the extended set of semantic type entities. The method maps semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating a knowledge data model, the method comprising: providing an initial set of semantic type entities of a specific semantic type; generating an extended set of semantic type entities, the generating of the extended set comprising expanding the initial set of semantic type entities using available mappings between entities of the initial set and entities of unspecified type; clustering entities of a same semantic type within the extended set of semantic type entities; and generating, by a processor, the knowledge data model, the generating of the knowledge data model comprising mapping semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities. 2. The method of claim 1 wherein the mappings comprise ontology mappings of ontologies. 3. The method of claim 2 wherein the ontology mappings are relations between entities of different ontologies defining an equivalence between two different entities. 4. The method of claim 1, wherein the entities of unspecified type are extracted from knowledge resources that form part of a linked open data cloud. 5. The method of claim 4, wherein unstructured textual resources containing text-based documents are automatically integrated in the linked open data cloud. 6. The method of claim 5, wherein unstructured text of the textual resources is linguistically and semantically processed using a semantic data model to extract semantic type entities. 7. The method of claim 6, wherein the extracted semantic type entities are mapped on linked open data entities using string matching and transformed into triple formats extended with links to the linked open data cloud. 8. The method of claim 1, wherein the initial set of semantic type entities comprises an initial disease set, an initial symptom set, or an initial disease set and an initial symptom set. 9. The method of claim 1, wherein the generated knowledge data model is output as a knowledge data model graph, is stored in a database for further processing, or is output as a knowledge data model graph and is stored in a database for further processing. 10. An apparatus for automatically generating a knowledge data model, the apparatus comprising: a loading unit configured to load at least one initial set of semantic type entities of a specific semantic type from a database; and a processor configured to expand the at least one loaded initial set of semantic type entities using available mappings between entities of the at least one initial set and entities of unspecified type to generate an extended set of semantic type entities, the processor further configured to cluster entities of a same semantic type within the extended set of semantic type entities, wherein semantic relations between entities of different semantic type are mapped to relations between corresponding clusters containing the entities to generate the knowledge data model. 11. The apparatus of claim 10, wherein the mappings comprise ontology mappings of ontologies stored in the database. 12. The apparatus of claim 10, wherein the entities of unspecified type are extracted from resources forming part of a linked open data cloud connected to the apparatus by a data interface. 13. The apparatus of claim 10, further comprising a graphical user interface, wherein the generated knowledge data model is output as a knowledge data model graph via the graphical user interface, is stored in a database for further processing, or is output as a knowledge data model graph via the graphical user interface and is stored in a database for further processing. 14. A linked open data (LOD) cloud system comprising: a plurality of linked data resources; and an apparatus comprising a processor, the apparatus configured to: provide an initial set of semantic type entities of a specific semantic type; expand the initial set of semantic type entities using available mappings between entities of the initial set and entities of an unspecified type to generate an extended set of semantic type entities; cluster entities of a same semantic type within the extended set of semantic type entities; and map, with the processor, semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model. 15. A model generation software tool for automatically generating a knowledge data model, the model generation tool comprising: program instructions executable by a processor, the program instructions comprising: providing an initial set of semantic type entities of a specific semantic type; expanding the initial set of semantic type entities using available mappings between entities of the initial set and entities of an unspecified type to generate an extended set of semantic type entities; clustering entities of a same semantic type within the extended set of semantic type entities; and mapping semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model. 16. A data carrier configured to store a model generation software tool, the model generation software tool comprising: program instructions executable by a processor, the program instructions comprising: providing an initial set of semantic type entities of a specific semantic type; expanding the initial set of semantic type entities using available mappings between entities of the initial set and entities of an unspecified type to generate an extended set of semantic type entities; clustering entities of a same semantic type within the extended set of semantic type entities; and mapping semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model.