14. A computer implemented method comprising: receiving a document containing text or unstructured data, wherein the document is a representation of a physical business object stored in a document storage unit; identifying and extracting a plurality of text segments and structure of the document using a set of tagging and natural language processing techniques; querying the extracted plurality of text segments against a set of inverted indices, wherein the set of inverted indices comprises attributes of a set of predefined data structure entities and associated relationships based on weight values in an entity model structure; for a text segment from the extracted plurality of text segments, storing matching attributes of the set of predefined data structure entities and associated relationships; constructing a set of entity graphs including a plurality of entity nodes connected by a weighted edge representing the matching attributes and associated relationships between the plurality of text segments and the attributes of the set of predefined data structure entities, between the attributes and the set of predefined data structure entities, and between data structure entities in the set of predefined entities; ranking the set of entity graphs based on a sum of the weight values associated with the matching attributes and associated relationships; and selecting higher scored one or more entity graphs of the set of entity graphs based on the ranking.
14. A computer implemented method comprising: receiving a document containing text or unstructured data, wherein the document is a representation of a physical business object stored in a document storage unit; identifying and extracting a plurality of text segments and structure of the document using a set of tagging and natural language processing techniques; querying the extracted plurality of text segments against a set of inverted indices, wherein the set of inverted indices comprises attributes of a set of predefined data structure entities and associated relationships based on weight values in an entity model structure; for a text segment from the extracted plurality of text segments, storing matching attributes of the set of predefined data structure entities and associated relationships; constructing a set of entity graphs including a plurality of entity nodes connected by a weighted edge representing the matching attributes and associated relationships between the plurality of text segments and the attributes of the set of predefined data structure entities, between the attributes and the set of predefined data structure entities, and between data structure entities in the set of predefined entities; ranking the set of entity graphs based on a sum of the weight values associated with the matching attributes and associated relationships; and selecting higher scored one or more entity graphs of the set of entity graphs based on the ranking. 16. The method of claim 14 further comprising: applying text recognition techniques to the document; and in response to applying the text recognition techniques, obtaining the structure of the document.