1. A non-transitory computer-readable storage medium tangibly storing machine-readable instructions thereon, which when executed by the machine, cause the machine to: receive a document containing text or unstructured data, wherein the document is a representation of a physical business object stored in a document storage unit; identify and extract a plurality of text segments and structure of the document using a set of tagging and natural language processing techniques; query 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, store matching attributes of the set of predefined data structure entities and associated relationships; construct 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 data structure entities; rank the set of entity graphs based on a sum of the weight values associated with the matching attributes and associated relationships; and select higher scored one or more entity graphs of the set of entity graphs based on the ranking.
1. A non-transitory computer-readable storage medium tangibly storing machine-readable instructions thereon, which when executed by the machine, cause the machine to: receive a document containing text or unstructured data, wherein the document is a representation of a physical business object stored in a document storage unit; identify and extract a plurality of text segments and structure of the document using a set of tagging and natural language processing techniques; query 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, store matching attributes of the set of predefined data structure entities and associated relationships; construct 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 data structure entities; rank the set of entity graphs based on a sum of the weight values associated with the matching attributes and associated relationships; and select higher scored one or more entity graphs of the set of entity graphs based on the ranking. 2. The non-transitory computer-readable storage medium of claim 1 having instructions that when executed further cause the machine to: parse the document via a parser; and in response to parsing the document, extract the text segment of the document.