Semanlink - Information retrieval ; Internet AND Knowledge Graphs

> 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

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

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2014-03-26 About

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

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2014-03-19 About

At SemTechBiz, Knowledge Graphs Are Everywhere - semanticweb.com

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2013-06-18 About

Bing Gets Its Own Knowledge Graph Via Britannica Partnership

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2013-01-24 About