Knowledge Graph-Enhanced Natural Language Processing for Semantic Information Retrieval and Document Summarization

The increasing volume of unstructured documents creates substantial challenges for retrieving relevant evidence and producing faithful, coherent summaries. This paper proposes a knowledge graph-enhanced natural language processing architecture that combines contextual text encoders, entity and relation extraction, graph representation learning, hybrid retrieval, and evidence-grounded abstractive summarization. A provenance-aware document graph links entities, concepts, passages, and source documents; graph-neighborhood expansion complements dense semantic similarity and lexical matching. A constrained summarization module conditions generation on retrieved passages and selected graph triples, while a consistency mechanism penalizes unsupported claims. We formulate joint objectives for retrieval relevance, graph alignment, summary quality, and factual consistency, and outline a reproducible evaluation protocol. To demonstrate the reporting format without implying experiments were conducted, numerical comparisons and charts are explicitly illustrative. The proposed framework aims to improve multi-hop retrieval, entity coverage, source traceability, and summary faithfulness, while introducing trade-offs in graph construction cost, latency, and sensitivity to extraction errors. The study provides an implementable blueprint and testable hypotheses for enterprise knowledge management, academic literature discovery, and technical document analysis.