Semanlink - Machine learning ; Semantic Web AND Tagging

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

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

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2019-06-22 About

Using Machine Learning to Support Continuous Ontology Development (2010)

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2018-08-07 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

SKOS + DC + Linked Data = Semantic Tagging? - benjamin nowack's blog

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2009-08-27 About

Using tags for POWDER content labels

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2008-05-14 About

SCOT:Let’s Share Tags! » About SCOT

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2007-08-24 About