concept

embeddings

Also known as: embedding representations

Facts (18)

Sources

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 3 facts

referenceReal-time updating of entities and relationships in large-scale Knowledge Graphs can introduce significant computational burdens because it may require recalculating embeddings and connections, according to Liu J. et al. (2024).

referenceBordes et al. (2013) proposed a method for translating embeddings to model multi-relational data in the Advances in Neural Information Processing Systems (NeurIPS) conference proceedings.

claimThe mismatch in tokenization between Large Language Model (LLM) and Knowledge Graph (KG) embeddings can lead to information loss during alignment.

Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org arXiv 2 facts

claimEmbedding-based link prediction methods that rely on shallow embeddings store all embeddings in an entity/relation matrix and retrieve them via a lookup table, which prevents these models from handling unseen entities.

referenceY. Zhao, A. Zhang, R. Xie, K. Liu, and X. Wang proposed a method for connecting embeddings to perform entity typing in knowledge graphs in 2020.

Designing Knowledge Graphs for AI Reasoning, Not Guesswork linkedin.com Piers Fawkes · LinkedIn Jan 14, 2026 2 facts

procedureThe AI agent architecture step 'Memory Consolidation & Learning' selectively persists data across interactions based on relevance and user demand, updating embeddings, summaries, and patterns to enable continuous improvement.

claimAI systems often produce hallucinations because they are forced to infer connections from raw data, loosely related documents, or embeddings at runtime, rather than having that structure provided.

Detect hallucinations for RAG-based systems - AWS aws.amazon.com Amazon Web Services May 16, 2025 2 facts

claimThe Amazon Titan Embeddings model can be used to generate embeddings for context and response text to facilitate semantic similarity analysis.

procedureSemantic similarity-based hallucination detection involves three steps: (1) create embeddings for the answer and the context using an LLM, (2) calculate cosine similarity scores between each sentence in the answer and the context, and (3) tune the decision threshold for a specific dataset to classify hallucinating statements.

Empowering RAG Using Knowledge Graphs: KG+RAG = G-RAG neurons-lab.com Neurons Lab 1 fact

claimVisualizing sub-graphs or embeddings of a knowledge graph allows users to observe how entities and their relationships are organized, which aids in analyzing and interpreting the underlying data structure.

LLM Observability: How to Monitor AI When It Thinks in Tokens | TTMS ttms.com TTMS Feb 10, 2026 1 fact

claimThe Arize platform provides analytics for embeddings and drift, including the ability to automatically highlight if the distribution of prompts changes over time.

Enhancing LLMs with Knowledge Graphs: A Case Study - LinkedIn linkedin.com LinkedIn Nov 7, 2023 1 fact

procedureTo fact-check the LLM, the authors use the Cypher query language to return relevant coverage nodes and their descriptions from the knowledge graph, then perform a similarity match between the LLM response and the retrieved knowledge graph information using embeddings.

RAG Hallucinations: Retrieval Success ≠ Generation Accuracy linkedin.com Sumit Umbardand · LinkedIn Feb 6, 2026 1 fact

perspectiveProduction-grade RAG systems require both embeddings to capture meaning and metadata to enforce constraints.

Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org medRxiv Nov 2, 2025 1 fact

claimPrompt-based strategies and post-hoc calibration techniques, such as temperature scaling or external calibrators, are used to manage LLM confidence and adjust logits or embedding representations.

10 RAG examples and use cases from real companies - Evidently AI evidentlyai.com Evidently AI Feb 13, 2025 1 fact

procedureThomson Reuters' customer service solution uses embeddings to find relevant documents by splitting text into small chunks, embedding each chunk, and storing them in a vector database.

Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv Aug 7, 2025 1 fact

procedureThe GraphRAG retrieval process uses a two-stage strategy: first, a high-recall one-hop graph traversal to identify candidate nodes, followed by a dense vector-based re-ranking step using OpenAI embeddings and cosine similarity to refine the results.

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv Mar 3, 2025 1 fact

procedurePrompt-based strategies encourage Large Language Models (LLMs) to self-assess confidence, while post-hoc calibration techniques like temperature scaling or external calibrators adjust logits or embedding representations (Whitehead et al., 2022; Xie et al., 2024; Tian et al., 2023).

How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Neo4j Jun 18, 2025 1 fact

procedureThe process for preparing documents for retrieval-augmented generation (RAG) involves five steps: (1) Chunk the text by splitting documents into multiple chunks, (2) Generate embeddings by using a text embedding model to create vector representations of the text chunks, (3) Encode the user query by converting the input question into a vector at query time, (4) Perform similarity search by applying algorithms like cosine similarity to compare the distance between the user input vector and the embedded text chunks, and (5) Retrieve top matches by returning the most similar documents to provide context to the large language model.