structured data

Unstructured data

Justification not yet generated — showing supporting facts

• Modern Enterprise Resource Planning (ERP) systems, such as those used for finance, procurement, HR, and manufacturing, generate vast volumes of structured and unstructured data across interconnected modules.
• Stardog Voicebox supports any database, document, or API, including structured, semi-structured, and unstructured data.
• Safety RAG supports any database, document, or API, including structured, semi-structured, and unstructured data.
• Enterprise AI platforms require the fusion of Large Language Models (LLMs) and Knowledge Graphs (KGs) to achieve comprehensive recall, where LLMs process unstructured data like documents and KGs process structured and semi-structured data like database records.
• The authors of 'Construction of Knowledge Graphs: State and Challenges' expand the scope of knowledge graph construction research to include non-RDF-based models like the Property Graph Model, the integration of structured and unstructured data, and incremental maintenance.
• Knowledge acquisition, which involves extracting knowledge from structured and unstructured data, is a critical step in generating knowledge graphs.
• Knowledge graphs integrate heterogeneous data from various sources, including unstructured data (text), semi-structured data (pictures, audio), and structured data (databases or other knowledge graphs) in a semantically rich manner.
• Knowledge extraction is typically applied to unstructured data inputs like text and may be unnecessary for structured data sources such as databases or other knowledge graphs.
• Hogan et al. provide a comprehensive introduction to knowledge graphs, covering multiple graph data models, methods for handling unstructured, semi-structured, and structured data, as well as tasks like learning on and publishing knowledge graphs.
• Generic Graph RAG (Retrieval-Augmented Generation) considers structured data but lacks a principled unification of structured, semi-structured, and unstructured data sources across an enterprise.
• Integrating Large Language Models with Knowledge Graphs allows AI systems to answer complex queries, provide sophisticated explanations, and offer verifiable information by drawing on both unstructured and structured data, which improves system accuracy and utility in real-life deployments, as supported by [43] and [51].
• Populating knowledge graphs from semi-structured data is the most common method, while only approximately 50% of the considered solutions or toolsets support importing from unstructured or structured data.

knowledge graphs

Knowledge graphs are fundamentally related to structured data as they are a primary method for organizing and representing such data, as evidenced by their role in enhancing machine learning algorithms [1] and their use in intelligent educational systems [2]. Furthermore, knowledge graphs are explicitly described as a way to integrate and process structured data sources like databases {fact:5, fact:6, fact:10}. — 10 supporting facts

Large Language Models

Large Language Models and structured data are related through research and architectural frameworks that aim to bridge the gap between LLM reasoning and deterministic schemas, as seen in the development of 'StructGPT' [1] and the integration of LLMs with Knowledge Graphs {fact:2, fact:3, fact:4}. While they operate on different principles [2], they are increasingly combined to enhance AI system accuracy and query capabilities {fact:2, fact:3}. — 5 supporting facts

artificial intelligence

Artificial intelligence systems, specifically Large Language Models, utilize structured data from Knowledge Graphs to improve their accuracy, utility, and ability to provide verifiable information as described in [1]. — 1 supporting fact