Semantic knowledge graph: Difference between revisions
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A '''semantic knowledge graph''' ('''SKG''') is a structured knowledge representation framework that models real-world entities, concepts, events, and their relationships using graph-based data structures enriched with semantic meaning. Unlike traditional relational databases or hyperlink-based information systems, a semantic knowledge graph represents knowledge as interconnected nodes and edges that are formally defined through ontologies, enabling both humans and machines to understand the meaning of the relationships between data. Semantic knowledge graphs integrate principles from knowledge representation, graph theory, the [[Semantic Web]], artificial intelligence, and linked data to support advanced information retrieval, reasoning, and decision-making. | A '''semantic knowledge graph''' ('''SKG''') is a structured knowledge representation framework that models real-world entities, concepts, events, and their relationships using graph-based data structures enriched with semantic meaning. Unlike traditional relational databases or hyperlink-based information systems, a semantic knowledge graph represents knowledge as interconnected nodes and edges that are formally defined through ontologies, enabling both humans and machines to understand the meaning of the relationships between data. Semantic knowledge graphs integrate principles from knowledge representation, graph theory, the [[Semantic Web]], artificial intelligence, and linked data to support advanced information retrieval, reasoning, and decision-making. | ||
A semantic knowledge graph typically represents knowledge as entities connected through explicitly defined relationships while incorporating standardized vocabularies, taxonomies, and ontologies that describe the meaning of those relationships. The approach enables information originating from multiple heterogeneous sources to be integrated into a unified, machine-readable knowledge network that can support semantic search, question answering, recommendation systems, data integration, and automated reasoning. Semantic knowledge graphs have become an important component of modern search engines, enterprise knowledge management platforms, scientific databases, digital libraries, financial intelligence systems, and | A semantic knowledge graph typically represents knowledge as entities connected through explicitly defined relationships while incorporating standardized vocabularies, taxonomies, and ontologies that describe the meaning of those relationships. The approach enables information originating from multiple heterogeneous sources to be integrated into a unified, machine-readable knowledge network that can support semantic search, question answering, recommendation systems, data integration, and automated reasoning. Semantic knowledge graphs have become an important component of modern search engines, enterprise knowledge management platforms, scientific databases, digital libraries, financial intelligence systems, and [[Large language model]] (LLM) applications. | ||
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