1. A computer implemented method utilizing a processor comprising: representing a data model as a graph, the graph stored at a first memory location, and the graph including an assertion as a reified n-ary relation represented as a node in the graph with edges linking to concepts participating in the relation, the node including a score attribute representing a strength of a relationship among concepts represented in the graph; ingesting the graph, including traversing the graph and flattening one or more node paths and storing the paths, the traversal comprising: following all paths from a current concept to a root concept; following all paths from a category of the current concept to a root category; and following a neighborhood of the current concept to an adjacent concept, wherein neighbor data demonstrates a non-hierarchical relationship of the current concept to a neighboring concept; converting the ingested graph into a set of flat document structures supported by information ranking and retrieval framework, including modeling data represented in the graph into a searchable index, the searchable index representing a structure of the node and edges represented in the graph, and storing the document structure at a second memory location; and conducting a search of the searchable index, including specifying a search concept and the score attribute to the searchable index.
1. A computer implemented method utilizing a processor comprising: representing a data model as a graph, the graph stored at a first memory location, and the graph including an assertion as a reified n-ary relation represented as a node in the graph with edges linking to concepts participating in the relation, the node including a score attribute representing a strength of a relationship among concepts represented in the graph; ingesting the graph, including traversing the graph and flattening one or more node paths and storing the paths, the traversal comprising: following all paths from a current concept to a root concept; following all paths from a category of the current concept to a root category; and following a neighborhood of the current concept to an adjacent concept, wherein neighbor data demonstrates a non-hierarchical relationship of the current concept to a neighboring concept; converting the ingested graph into a set of flat document structures supported by information ranking and retrieval framework, including modeling data represented in the graph into a searchable index, the searchable index representing a structure of the node and edges represented in the graph, and storing the document structure at a second memory location; and conducting a search of the searchable index, including specifying a search concept and the score attribute to the searchable index. 8. The method of claim 1 , further comprising searching the searchable index, including presenting a concept search parameter selected from the group consisting of: concept name, concept synonym, a related concept name, a related concept synonym, category hierarchy, and a concept inheritance hierarchy, and combinations thereof, and an assertion search parameter selected from the group consisting of: a related concept name and a related concept synonym.