Developing NLP Solutions for Automatic Knowledge Graph Construction, Part 2 | Panos Alexopoulos | Connected Data

This masterclass explores the exciting world of automatically populating knowledge graphs using Natural Language Processing (NLP). The podcast is organized into sections that delve into entity discovery and relation extraction, along with important methodological insights. The speaker showcases how to use the Sentence-BERT library for relation extraction, demonstrating the process of fine-tuning a pre-trained model with specific domain data to enhance its ability to identify synonymous occupations accurately. A key takeaway is that while NLP tools are incredibly powerful, it's essential to carefully select data, ensure semantic clarity, and acknowledge potential biases to create reliable knowledge graphs. This underlines the importance of human validation and a solid methodology in the process.

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Developing NLP Solutions for Automatic Knowledge Graph Construction, Part 2 | Panos Alexopoulos

Connected Data

Initial Knowledge Graph Challenges: Stakeholder Identification

Introduction to Relation Extraction in Knowledge Graph Mining

Transfer Learning for Relation Extraction using Sentence-BERT

Implementing Sentence-BERT for Similarity Analysis

Applying Sentence-BERT for Relation Extraction and Paraphrase Mining

Q&A Session 1: Addressing User Queries on Relation Extraction and Synonym Suggestion

Q&A Session 2: Fine-tuning Models and Incorporating Website Structure

Validating and Fine-tuning Sentence-BERT using ESCO Data

Q&A Session 3: Knowledge Graph Construction and Embedding Extracted Information

Methodological Aspects of Knowledge Graph Mining: Pitfalls and Best Practices

Evaluating and Selecting Knowledge Mining Tools: A Critical Approach

Concluding Discussion and Q&A Session 4: Addressing User Questions on Model Training and Ontology

Closing Remarks and Final Q&A

Developing NLP Solutions for Automatic Knowledge Graph Construction, Part 2 | Panos Alexopoulos

Connected Data

00:05Initial Knowledge Graph Challenges: Stakeholder Identification

Initial Knowledge Graph Challenges: Stakeholder Identification

01:23Introduction to Relation Extraction in Knowledge Graph Mining

Introduction to Relation Extraction in Knowledge Graph Mining

04:55Transfer Learning for Relation Extraction using Sentence-BERT

Transfer Learning for Relation Extraction using Sentence-BERT

09:08Implementing Sentence-BERT for Similarity Analysis

Implementing Sentence-BERT for Similarity Analysis

14:42Applying Sentence-BERT for Relation Extraction and Paraphrase Mining

Applying Sentence-BERT for Relation Extraction and Paraphrase Mining

19:30Q&A Session 1: Addressing User Queries on Relation Extraction and Synonym Suggestion

Q&A Session 1: Addressing User Queries on Relation Extraction and Synonym Suggestion

24:26Q&A Session 2: Fine-tuning Models and Incorporating Website Structure

Q&A Session 2: Fine-tuning Models and Incorporating Website Structure

27:31Validating and Fine-tuning Sentence-BERT using ESCO Data

Validating and Fine-tuning Sentence-BERT using ESCO Data

35:16Q&A Session 3: Knowledge Graph Construction and Embedding Extracted Information

Q&A Session 3: Knowledge Graph Construction and Embedding Extracted Information

45:58Methodological Aspects of Knowledge Graph Mining: Pitfalls and Best Practices

Methodological Aspects of Knowledge Graph Mining: Pitfalls and Best Practices

1:00:30Evaluating and Selecting Knowledge Mining Tools: A Critical Approach

Evaluating and Selecting Knowledge Mining Tools: A Critical Approach

1:20:14Concluding Discussion and Q&A Session 4: Addressing User Questions on Model Training and Ontology

Concluding Discussion and Q&A Session 4: Addressing User Questions on Model Training and Ontology

1:57:12Closing Remarks and Final Q&A

Closing Remarks and Final Q&A