Abstract: Techniques are described for generating a hierarchical group based on a set of data. In one example, a method includes classifying two or more data items from a set of data with respect to a library of ontological concepts. The method further includes classifying the two or more data items with respect to lexical correlations between the two or more data items. The method further includes generating a hierarchical group in which the two or more data items are partitioned into one or more hierarchical partitions based at least in part on the classifying with respect to the library of ontological concepts and the classifying with respect to the lexical correlations, wherein each of the one or more hierarchical partitions comprises the two or more data items.

Abstract: Techniques are described for generating a hierarchical group based on a set of data. In one example, a method includes classifying two or more data items from a set of data with respect to a library of ontological concepts. The method further includes classifying the two or more data items with respect to lexical correlations between the two or more data items. The method further includes generating a hierarchical group in which the two or more data items are partitioned into one or more hierarchical partitions based at least in part on the classifying with respect to the library of ontological concepts and the classifying with respect to the lexical correlations, wherein each of the one or more hierarchical partitions comprises the two or more data items.

Abstract: Techniques are described for modeling information from a data source. In one example, a method includes receiving a data set. The method further includes defining at least one generic domain that provides a group of default concepts. The method further includes receiving a selection of an indication of at least one domain extension that extends the group of default concepts provided by the at least one generic domain, wherein the at least one domain extension includes concepts for a specific industry. The method further includes generating based on the data set and a combination attic at least one generic domain and the at least one domain extension, a model and a domain.

Abstract: Techniques are described for modeling information from a data source. In one example, a method includes receiving a data set. The method further includes defining at least one generic domain that provides a group of default concepts. The method further includes receiving a selection of an indication of at least one domain extension that extends the group of default concepts provided by the at least one generic domain, wherein the at least one domain extension includes concepts for a specific industry. The method further includes generating based on the data set and a combination of the at least one generic domain and the at least one domain extension, a model and a domain.

Abstract: Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes comparing, with one or more computing devices, a data item heading from the data source with concept keywords in a concept library, the concept library comprising a plurality of concepts and one or more of the concept keywords in at least one language associated with each of one or more of the concepts. The method further includes identifying, with one or more computing devices, one or more matches between the data item heading and one or more concept keywords associated with a particular concept from among the concepts comprised in the concept library. The method further includes identifying, with one or more computing devices, the data item heading as being associated with the particular concept.

Abstract: Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes identifying one or more lexical clues associated with each of one or more data item headings from the data source based on a set of lexical clue detection rules. The method further includes mapping each of one or more of the data item headings to one or more business concepts based on comparing the one or more identified lexical clues associated with each of one or more of the data item headings with a business ontology that comprises a description of the business concepts. The method further includes generating a semantic business intelligence model comprising one or more semantic associations between the one or more data item headings based on the mapping of the data item headings to the one or more of the business concepts.

Abstract: Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes comparing, with one or more computing devices, a data item heading from the data source with concept keywords in a concept library, the concept library comprising a plurality of concepts and one or more of the concept keywords in at least one language associated with each of one or more of the concepts. The method further includes identifying, with one or more computing devices, one or more matches between the data item heading and one or more concept keywords associated with a particular concept from among the concepts comprised in the concept library. The method further includes identifying, with one or more computing devices, the data item heading as being associated with the particular concept.

Abstract: Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes identifying one or more lexical clues associated with each of one or more data item headings from the data source based on a set of lexical clue detection rules. The method further includes mapping each of one or more of the data item headings to one or more business concepts based on comparing the one or more identified lexical clues associated with each of one or more of the data item headings with a business ontology that comprises a description of the business concepts. The method further includes generating a semantic business intelligence model comprising one or more semantic associations between the one or more data item headings based on the mapping of the data item headings to the one or more of the business concepts.