Graph-Powered Machine Learning - Meetup Paris - March 5, 2018
Graph -based machine learning is becoming an important trend in artificial intelligence, transcending a lot of other techniques. Using graphs as a basic representation of data for multiple purposes:
- the data is already modeled for further analysis
- graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs;
- improving computation performances and quality. The talk will present these advantages and present applications in the context of recommendation engines and natural language processing.
Speaker: Dr. Vlasta Kus (@VlastaKus) is a Data Scientist at GraphAware, specializing in graph-based Natural Language Processing and related topics, including deep learning techniques. He speaks English, Czech and some French and currently lives in Prague.
2. WHAT IS MACHINE LEARNING?
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[Machine Learning is the] field of study that gives
computers the ability to learn without being explicitly
programmed.
— Arthur Samuel, 1959
12. Store the results of the training phase
Provide multiple access patterns
Mix models
The size depends on the algorithm
STORING THE RESULTS & MODEL
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15. STORING DATA SOURCES: TENSOR
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Simple Recommendation
f: User x Item -> Relevance Score
Context Aware Recommendation
f: User x Item x Context1 x Context2 x Context3 -> Relevance Score
16. The results of machine learning process can be stored in a graph as well.
Some examples are:
‣ Similarity (k-Nearest Neighbours)
‣ Cluster
‣ Spanning Tree
‣ Decision Tree
‣ Random forest
‣ Markov Chain
STORING RESULTS & MODELS
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20. ‣ NLP and graphs: natural fit
GRAPH-BASED NATURAL LANGUAGE
PROCESSING
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‣ Knowledge enrichment
Source: http://nlp.stanford.edu:8080/corenlp/process
21. Unsupervised techniques tend to be underestimated …
‣ No need for time & money to get massive labeled training datasets
‣ Often faster to train & faster to predict
‣ Unsupervised deep learning
UNSUPERVISED ML ALGORITHMS
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22. Some graph-native algorithms that are relevant to machine learning processes:
‣ Random Walk
‣ Page Rank
‣ Graph Matching
‣ Shortest Path
‣ Depth-First Graph Traversal
‣ Breadth-First Graph Traversal
‣ Minimum Spanning Tree
‣ Graph Clustering
‣ Node2vec
GRAPH-BASED ML ALGORITHMS
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27. ‣ Meet us tomorrow at Neo4j GraphTour
‣ Come to our meet-ups
graphaware.com/events
‣ Visit our blog
graphaware.com/blog
‣ Watch us
youtube.com -> GraphAware channel
‣ And most importantly …
Get in touch!
INTERESTED IN MORE?
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