SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #61: NLP, Machine Learning and Analysis
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CWPK #61: NLP, Machine Learning and Analysis
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-11-02T17:08:50Z
content: A Wealth of Applications Sets the Stage for Pay Offs from KBpedia With this installment of the Cooking with Python and KBpedia series we move into Part VI of seven parts, a part with the bulk of the analytical and machine learning (that is, "data science") discussion, and the last part where significant code is developed and documented. Because of the complexity of these installments, we will also be reducing the number released per week for the next month or so. We also will not be able to post fully operational electronic notebooks to MyBinder since the supporting libraries strain the limits of that service. At the conclusion of this part, which itself has 11 installments, we have four installments to wrap up the series and provide a consistent roadmap to the entire project. Knowledge graphs are unique information artifacts, and KBpedia is further unique in terms of its consistent and logical construction as well as its incorporation of significant text content via Wikipedia pages. These characteristics provide unique value for KBpedia, but it is also a combination not duplicated anywhere else in the data science ecosystem. One of the objectives, therefore, of this part of our CWPK series is the creation of some baseline knowledge representations useful to data science aims that capture these unique characteristics. KBpedia's (or any knowledge graph constructed in a similar manner) combination of characteristics make it a powerful resource in three areas of data science and machine learning. First, the nearly universal scope and degree of topic coverage with about 56,000 concepts, logically organized into typologies with a high degree of disjointedness, means that accurate 'slices' or training sets may be extracted from KBpedia nearly instantaneously. Labeled training sets are one of the most time consuming and expensive activities in doing supervised machine learning. We can extract these nearly for free from KBpedia. Further, with its links to tens of millions o…
encoded: A Wealth of Applications Sets the Stage for Pay Offs from KBpedia With this installment of the Cooking with Python and KBpedia series we move into Part VI of seven parts, a part with the bulk of the analytical and machine learning (that is, “data science”) discussion, and the last part where significant code is developed and documented. Because of the complexity of these installments, we will also be reducing the number released per week for the next month or so. We also will not be able to post fully operational electronic notebooks to MyBinder since the supporting libraries strain the limits of that service. At the conclusion of this part, which itself has 11 installments, we have four installments to wrap up the series and provide a consistent roadmap to the entire project. Knowledge graphs are unique information artifacts, and KBpedia is further unique in terms of its consistent and logical construction as well as its incorporation of significant text content via Wikipedia pages. These characteristics provide unique value for KBpedia, but it is also a combination not duplicated anywhere else in the data science ecosystem. One of the objectives, therefore, of this part of our CWPK series is the creation of some baseline knowledge representations useful to data science aims that capture these unique characteristics. KBpedia’s (or any knowledge graph constructed in a similar manner) combination of characteristics make it a powerful resource in three areas of data science and machine learning. First, the nearly universal scope and degree of topic coverage with about 56,000 concepts, logically organized into typologies with a high degree of disjointedness, means that accurate ‘slices’ or training sets may be extracted from KBpedia nearly instantaneously. Labeled training sets are one of the most time consuming and expensive activities in doing supervised machine learning. We can extract these nearly for free from KBpedia. Further, with its links to tens of millions o…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: MyBinder
links to: Knowledge graphs
links to: KBpedia
links to: supervised machine learning
links to: entity recognition
links to: deep learning
links to: neural networks
links to: CNN
links to: RNN
links to: PyTorch
links to: Clojure posted online
links to: classify Iris species
links to: linear discriminant analysis
links to: "Machine Learning Algorithms For Beginners with Code Examples in Python"
links to: unsupervised learning
links to: reinforcement learning
links to: word2vec
links to: BERT
links to: geometric deep learning
links to: pandas
links to: CWPK #55
links to: NetworkX
links to: PyViz
links to: NumPy
links to: NLTK
links to: spaCy
links to: flair
links to: gensim
links to: OpenNMT-py
links to: Polyglot
links to: Stanza
links to: TextBlob
links to: language embedding
links to: tf-idf
links to: vector space model
links to: LDA
links to: explicit semantic analysis
links to: gloVe
links to: topics
links to: sentences
links to: categories and paragraphs
links to: documents
links to: node2vec
links to: GPT-3
links to: fastText
links to: TextBrewer
links to: scikit-learn
links to: SciPy
links to: project
links to: documentation
links to: comparatively evaluate
links to: Chainer
links to: CNTK
links to: main release
links to: ONNX
links to: Keras
links to: PlaidML
links to: TensorFlow
links to: Theano
links to: GitHub
links to: Transformers
links to: KRL
links to: KGE
links to: link prediction
links to: KGC
links to: deep graphs

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

CWPK #55
label: CWPK #56

project
label: scikit-learn

KGE
label: Knowledge Graph Embedding: A Survey of Approaches and Applications

geometric deep learning
label: Geometric Deep Learning Library Comparison

deep graphs
label: Deep Graph Library
label: DGL

graph representation (or representation) learning

AmpliGraph

DGL-KE

Graph Nets

KGCN

GRAKN

OpenKE

OWL2Vec*

GitHub

PyKEEN

GitHub

Pykg2vec: A Python Library for Knowledge Graph Embedding

GitHub

PyTorch Geometric

documentation

RDF2Vec

RDF2Vec Light
label: RDF2Vec Light — A Lightweight Approach for Knowledge Graph Embeddings

scikit-kge

StellarGraph

TorchKGE

reportedly

KarateClub

KGTK

standard KG file format

tools

"Machine Learning in Python: Main Developments and technology Trends in Data Science, Machine Learning, and Artificial Intelligence"

Captum

Catalyst

fastai

PyTorch-NLP

skorch

RDFLib

Dask

sense2vec

spacy-stanza

spacy-wordnet

torchtext

40 supervised ones

PCA

recipes for the transfer

DeepSNAP

RDFFrames

GitHub

KarateClub

PyGSP

quaternions

Clifford algebras

Charles Peirce

William Kingdon Clifford

Natural Language Processing Recipes: Best Practices and Examples

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

Python Machine Learning Tutorials

Real Python

PyTorch vs. TensorFlow – A Detailed Comparison

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

GitHub GCNN TensorFlow implementation

Awesome Graph Classification

A Comprehensive Comparison of Unsupervised Network Representation Learning Methods

A Review of Relational Machine Learning for Knowledge Graphs

Awesome Graph Representation Learning

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Heterogeneous Network Representation Learning: Survey and Benchmark

Introduction to Geometric Deep Learning

KBpedia

code listing covering the series

direct download

notify me