Published Mar 13, 2019

83 - Knowledge Base Construction, with Sebastian Riedel

Sebastian Riedel delves into the evolution and impact of the Automatic Knowledge Base Construction (AKBC) conference, examining complexities in knowledge representation and extraction techniques, while discussing the practical challenges and industry applications of integrating knowledge graphs into NLP technologies.

Episode Highlights

Topics covered

Knowledge Base Construction Conference
01
Knowledge Representation
02
Information Extraction
03
Practical Applications
04

83 - Knowledge Base Construction, with Sebastian Riedel

Topics covered

Popular Clips

Named Entities in Knowledge Bases

Knowledge Graphs in NLP

Knowledge Representation Challenges

Challenging Information Extraction

Learning vs. Knowledge

AI Knowledge Representation

Trusting Information

Knowledge-Based Construction Conference

Knowledge Base Construction

Knowledge Base Construction

Knowledge Bases Impact

Relation Extraction Overview

Knowledge Base Challenges

Episode Highlights

Knowledge Base Construction Conference

Knowledge Representation

Information Extraction

Practical Applications

83 - Knowledge Base Construction, with Sebastian Riedel

Topics covered

Popular Clips

Episode Highlights

Knowledge Base Construction Conference

Knowledge RepresentationSebastian Riedel explores the complexities of relational knowledge and the focus on named entities in knowledge base construction. He examines the challenges models face in capturing intricate relationships and the reasons behind emphasizing named entities.

Knowledge Representation

Information ExtractionSebastian Riedel explores the intricacies of knowledge base construction, focusing on schema trade-offs and relation extraction techniques. He discusses the balance between open and closed schemas and the role of distant supervision in automating data labeling.

Information Extraction

Practical ApplicationsThe discussion shifts to the industry's interest in knowledge base construction and the challenges faced in integrating knowledge graphs into NLP applications. Sebastian Riedel and Matt Gardner explore the potential and limitations of these technologies in practical settings.

Practical Applications