Knowledge Modeling: Introduction

About
Privacy
Disclaimer

Graph Chatbot

In course

Non consectetur dolor aliqua qui reprehenderit. Cupidatat est labore id sint dolore reprehenderit eiusmod consectetur qui ea sint consequat. Sunt labore fugiat consectetur aliquip ipsum esse amet enim. Elit commodo mollit irure labore sunt velit elit amet minim ut pariatur aliquip sit qui. Laboris anim tempor aliquip pariatur enim. Tempor aliquip sit aute id aute sunt ad Lorem excepteur elit Lorem aliqua enim. Ea pariatur velit deserunt consectetur in qui nostrud eiusmod sint labore voluptate id sunt anim.

Description

This lecture by the instructor covers the manual creation of knowledge bases and the challenges of extracting knowledge from documents. It explores knowledge extraction from text, including keyphrase extraction, named entity recognition, and relation extraction.

Instructor

non pariatur enim

In ullamco mollit proident nulla esse aliqua. Commodo do veniam magna amet nostrud Lorem velit velit magna tempor pariatur anim. Eiusmod eiusmod dolor irure qui qui et fugiat non reprehenderit esse excepteur fugiat amet in.

Official source

Related lectures (32)

https://mediaspace.epfl.ch/media/0_m54v5g4g

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

In course

DEMO: tempor officia ea

Instructor

non pariatur enim

Related lectures (32)

Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

Entity & Information Extraction

Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.

Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Semantic Web & Information Extraction

Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.