This paper discusses a method for cross-sentence inference and network representation of semantics encoded in the document. The problem is challenging, as current methods rely predominantly on supervised learning, especially in Relationship Extraction and Named Entity Recognition, which are fundamental in constructing such representations called Knowledge Graphs. Additionally, in many areas of application of the proposed method, such as Risk Analysis, Named Entity Recognition is limited by extreme contextuality that is difficult to address with the current NER technology stack.