Semanlink - Knowledge Graphs AND Information retrieval

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Mots-clés liés

Arxiv Doc - Discuté avec Raphaël - Entities and LM - Entity Retrieval - ESWC - Extractive Search - Facebook FAIR - Frequently cited paper - Geometry of language embeddings - Giovanni Tummarello - GitHub project - Graph database - Graphs+Machine Learning - Graph visualization - Human in the loop - Information retrieval - IRIT - JSON-LD - Knowledge Graph Construction - Knowledge Graph Embeddings - Knowledge Graphs - LangChain - Laure Soulier - LIP6 - LlamaIndex - LLM + KG - LM + KG - LM Fine-tuning - Lynda Tamine - Metropolitan Museum of Art - Microsoft Research - Multi-label classification - Nearest neighbor search - NLP@Microsoft - RAG (Retrieval-Augmented Generation) - RAG + KG - Random walk - schema.org - Search Engines - Semantic Web : introduction - SemTechBiz - Similarity queries / Vector search - Thèse IRIT-Renault: biblio initiale - Tutorial - Tweet - Vector space model - Wikidata - Word2vec - Word sense / Lexical ambiguity - Yoav Goldberg -

Knowledge Graphs AND Information retrieval

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30 Documents

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

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2024-03-16 A propos

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

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2024-03-07 A propos

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

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2024-02-25 A propos

Knowledge Graph RAG Query Engine - LlamaIndex

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2023-08-17 A propos

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 A propos

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 A propos

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 A propos

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 A propos

SPIKE for Knowledge Base Construction

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2022-02-22 A propos

Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

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2022-02-22 A propos

Andrea Volpini sur Twitter : New blog post on #ContentHub for #Semantic #SEO. How to cluster content using Knowledge Graph Embeddings to detect: ✓ Clusters ✓ Sub-topics ✓ Author's topical authority

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2021-11-16 A propos

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

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2021-02-05 A propos

Exploring The MET Art Collections with Hume #2 | GraphAware

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2021-01-19 A propos

Exploration Engines - the koodos collective

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2020-12-10 A propos

Drew Tada sur Twitter : "Officially launching giantgra.ph A search engine for knowledge graphs..."

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2020-12-10 A propos

Knowledge Graphs: An Information Retrieval Perspective

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2020-10-30 A propos

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

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2020-06-09 A propos

[1911.02168] CoKE: Contextualized Knowledge Graph Embedding

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2020-03-22 A propos

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

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2019-10-09 A propos

Supercharging Elasticsearch for extended Knowledge Graph use cases: Data science + business analytics training: Strata Data Conference

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2019-05-16 A propos

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

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2018-06-08 A propos

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 A propos

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 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 A propos

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

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2018-02-12 A propos

How To Tell Search Engines What "Entities" Are On Your Web Pages

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2014-03-26 A propos

JSON-LD, the Google Knowledge Graph and schema.org SEO

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2014-03-19 A propos

At SemTechBiz, Knowledge Graphs Are Everywhere - semanticweb.com

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2013-06-18 A propos

Bing Gets Its Own Knowledge Graph Via Britannica Partnership

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2013-01-24 A propos