Скачать или смотреть Developing NLP Solutions for Automatic Knowledge Graph Construction, Part 1 | Panos Alexopoulos

Knowledge graphs are increasingly becoming important in the AI world as an enabling technology for data integration and analytics, semantic search and question answering, and other cognitive applications.

However, developing and maintaining large knowledge graphs in a manual way is too expensive and time consuming. To accelerate and scale the process, methods and techniques from the areas of information extraction and natural language processing (NLP) can be very helpful.

In this masterclass we will take a deep dive into the tasks required for mining a knowledge graph from text, and we will implement basic and advanced NLP techniques for each of them, using open-source tools and resources.

Key Topics
Knowledge Graphs
Natural Language Processing
Target Audience
NLP practitioners who want to learn how to apply their craft for constructing knowledge graphs
Data Scientists and Machine Learning Engineers
Goal
By the end of this masterclass, the attendees will be able to: - Identify the main components of a knowledge graph and the stages involved in its development process. - Identify and formulate the NLP tasks involved in a knowledge graph mining project - Develop and evaluate methods for mining entities and relations using open-source software

Format
The masterclass will be delivered in four 1-hour sessions, with one long break in between. Each session will comprise a slide-based presentation of key topics and techniques, a hands-on demonstration of these techniques with relevant software, and a Q&A.

Session Outline

Session 1: Introduction, Knowledge Graph Basics and Schema Definition (60mins)
Introduction (10 mins)
Knowledge Graph Basics (25 mins)
What are knowledge graphs and why we build them
Basic knowledge graph elements: entities, classes, individuals, relations
Knowledge graph representation: RDF Graphs vs Labeled Property Graphs
Knowledge graph development: lifecycle and approaches
Q&A
Defining a Knowledge Graph Schema (25 mins)
What a schema should include
Good and bad practices
Hands-on: Creating a knowledge graph schema with Protege
Q&A

Session 2: Mining Entities (60mins)
Task formulation and approaches
Off-The-Shelf APIs vs Custom solutions
Hands-On: Mining entities with the Spacy Named Entity Extractor
Hands-On: Mining entities with the Spacy EntityRuler
Hands-on: Training a custom entity extractor in Spacy

For Sessions 3&4, check out part 2 of this Masterclass.

Level
Intermediate - Advanced

Prerequisite Knowledge
Basic coding skills in Python and Jupyter notebooks
Familiarity with RDF/OWL or Labeled Property Graphs (e.g. Neo4J).

About The Speakers

Panos Alexopoulos, Head of Ontology, Textkernel

Panos Alexopoulos has been working at the intersection of data, semantics, and software, contributing to building intelligent systems that deliver value to business and society since 2006. Currently Head of Ontology at Textkernel BV, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain

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