You're reading from Graph Machine Learning

Product typeBook

Published inJun 2021

PublisherPackt

ISBN-139781800204492

Edition1st Edition

Concepts

Machine Learning

Authors (3):

Claudio Stamile

Aldo Marzullo

Enrico Deusebio

View More author details

Creating graphs from a corpus of documents

In this section, we will use the information we extracted in the previous section using the different text engines to build networks that relate the different information. In particular, we will focus on two kinds of graphs:

Knowledge-based graphs, where we will use the semantic meaning of sentences to infer relationships between the different entities.
Bipartite graphs, where we will be connecting the documents to the entities that appear in the text. We will then project the bipartite graph into a homogeneous graph, which will be made up of either document or entity nodes only.

Knowledge graphs

Knowledge graphs are very interesting as they not only relate entities but also provide a direction and a meaning to the relationship. For instance, let's take a look at the following relationship:

I (->) buy (->) a book

This is substantially...

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Graph Machine Learning

Published in: Jun 2021Publisher: PacktISBN-13: 9781800204492

Authors (3)

Claudio Stamile

Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Université Claude Bernard Lyon 1 (Lyon, France). During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.
Read more about Claudio Stamile

Aldo Marzullo

Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Université Claude Bernard Lyon 1 (Lyon, France), with a thesis entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.
Read more about Aldo Marzullo

Enrico Deusebio

Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees in aerospace engineering from Politecnico di Torino.
Read more about Enrico Deusebio

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You're reading from Graph Machine Learning

Creating graphs from a corpus of documents

Knowledge graphs

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