Abstract: 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.

Abstract: A system for extracting relations between measurements within an unstructured text and ontology concepts of at least one domain ontology stored in a domain ontology database is provided. The system includes an annotation unit adapted to process sentences of the unstructured text to derive tokens and measurements within the sentences. The derived tokens are annotated with ontology concepts mapped to the tokens. The system also includes a concept analyzing unit adapted to analyze, for each annotated sentence including at least one derived measurement, the annotated ontology concepts mapped to the derived tokens of the sentence to identify the ontology concepts related to the at least one derived measurement and to rank the identified related ontology concepts according to calculated relation strengths of the relations between the identified related ontology concepts and the respective measurement of the annotated sentence.

Abstract: Concept labels of an ontology are extended. An ontology includes concepts at least partially authored in a source language; a corpus at least partially authored in a target language is provided; said corpus is processed by a linguistical analysis and receiving a list of first terms as a result of said linguistical analysis, said first terms in said list ordered by a linguistical relevancy; within said list, at least one of said first terms is associated with an associated second term, said second term being a translation of said first term into said source language; a retrieval is conducted by using at least one of said second terms for identifying a matching concept within said ontology; and a concept label of said matching concept is extended by a first term associated to said second term of a matching retrieval.

Abstract: A method for processing medical reports includes applying natural language processing methods to perform an initial segmentation of at least parts of textual contents of each medical report into information units, thereby identifying information units, such as text fragments, sentences or text passages, within the medical reports. The information units are classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report may be annotated by assigning the information units to context meta-information determined by the context class, and the context meta-information may be used by other applications, e.g.

Abstract: A method for processing medical reports is disclosed. An initial segmentation textual contents of a medical report into information units is performed using natural language processing methods, thereby addressing the challenge of identifying information units, such as text fragments, sentences or text passages, covered within medical reports that are of relevance for a particular context. The information units are then classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report is then annotated by assigning at least one of said information units to at least one context meta-information determined by said context class. The context meta-information may be then used by other applications.