Semanlink - NLP ; Semantic Web AND Knowledge Graphs

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NLP ; Semantic Web AND Knowledge Graphs

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NLP 4 Semanlink

11 Documents

Frank van Harmelen sur X : "...GenAI is rapidly becoming the best motivation for symbolic AI in a long time!"

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2024-03-15 About

KGC23 Keynote: The Future of Knowledge Graphs in a World of LLMs — Denny Vrandečić, Wikimedia - YouTube

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2024-03-07 About

[2311.07509] A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases

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2023-11-21 About

FP Servant sur Twitter : "constructing a personal knowledge graph as a support for learning (and a metaphor of the learning experience)..."

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2020-12-03 About

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

Label unstructured data using Enterprise Knowledge Graphs 2

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2020-05-22 About

Disambiguating KBpedia Knowledge Graph Concepts

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2020-05-03 About

Martynas Jusevicius sur Twitter : "Is there a solution for entity recognition that would use a local #KnowledgeGraph to look for matches? Ideally any SPARQL datasource..."

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2020-03-13 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 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