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Information retrieval: techniques AND Knowledge Graphs

Common descendants

RAG + KG

17 Documents

Enhancing RAG-based application accuracy by constructing and leveraging knowledge graphs

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2024-03-16 About

GraphRAG: Unlocking LLM discovery on narrative private data - Microsoft Research

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2024-03-07 About

Ravi Theja sur X : "𝐀𝐜𝐭𝐢𝐯𝐞𝐑𝐀𝐆: 𝐑𝐞𝐯𝐞𝐚𝐥𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐨𝐟 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐯𝐢𝐚 𝐀𝐜𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠..."

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2024-02-25 About

Knowledge Graph RAG Query Engine - LlamaIndex

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2023-08-17 About

Jerry Liu on X: “Graph RAG” in @llama_index: a new method of augmenting LLMs with context from a graph database

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2023-08-17 About

Knowledge Graphs & LLMs: Fine-Tuning Vs. Retrieval-Augmented Generation | by Tomaz Bratanic | Neo4j Developer Blog (June 6 2023)

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2023-08-13 About

Integrate LLM workflows with Knowledge Graph using Neo4j and APOC | by Tomaz Bratanic | Jun, 2023 | Towards Data Science

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2023-06-09 About

Linking Entities with Knowledge Graphs | by Sigurd Berglann | Strise | Medium

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2021-02-05 About

Exploring The MET Art Collections with Hume #2 | GraphAware

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2021-01-19 About

facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.

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2020-06-09 About

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 About

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 About

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 About

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 About

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 About