Abstract: A method of structuring entity data using a machine learning document profiling model for improved information extraction comprises: training a learning document profiling model by applying document data and identification information on different document types to the learning document profiling model; profiling entity document data using the trained learning document profiling model; generating an entity profile using the trained learning document profiling model based on the profiled document data; selecting a subset of documents comprised in the profiled document data based on the entity profile; and tagging the selected subset of documents for further processing comprising one or more of a topic extraction process, a document processing algorithm selection process, a topic importance rating process, a knowledge graph mapping process, a document signature generation process, a document querying process, and a language derivation process.

Abstract: Domain specific knowledge base (KB) contains all concepts from domain and the semantic relations between concepts. The concepts and the semantic relations are extracted from an existing corpus of content for the domain. The World Wide Web Consortium (W3C) standard SKOS (Simple Knowledge Organization System) can be used and two types of semantic relations can be captured: hierarchal and associative. A Natural Language Processing (NLP) software engine can parse the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links. Concepts automatically identified as most important in this domain can be promoted to another layer, referred to as “Topics.

Abstract: Domain specific knowledge base (KB) contains all concepts from domain and the semantic relations between concepts. The concepts and the semantic relations are extracted from an existing corpus of content for the domain. The World Wide Web Consortium (W3C) standard SKOS (Simple Knowledge Organization System) can be used and two types of semantic relations can be captured: hierarchal and associative. A Natural Language Processing (NLP) software engine can parse the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links. Concepts automatically identified as most important in this domain can be promoted to another layer, referred to as “Topics.

Abstract: Domain specific knowledge base (KB) contains all concepts from domain and the semantic relations between concepts. The concepts and the semantic relations are extracted from an existing corpus of content for the domain. The World Wide Web Consortium (W3C) standard SKOS (Simple Knowledge Organization System) can be used and two types of semantic relations can be captured: hierarchal and associative. A Natural Language Processing (NLP) software engine can parse the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links. Concepts automatically identified as most important in this domain can be promoted to another layer, referred to as “Topics.

Abstract: Domain specific knowledge base (KB) contains all concepts from domain and the semantic relations between concepts. The concepts and the semantic relations are extracted from an existing corpus of content for the domain. The World Wide Web Consortium (W3C) standard SKOS (Simple Knowledge Organization System) can be used and two types of semantic relations can be captured: hierarchal and associative. A Natural Language Processing (NLP) software engine can parse the input text to create a semantic knowledge graph, which is then mapped to a SKOS knowledge model. During the linguistic understanding of the text, relevant domain concepts are identified and connected by semantic links. Concepts automatically identified as most important in this domain can be promoted to another layer, referred to as “Topics.