Lecture

Patrimonial Capitalism: The DH Circle

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

DH-405: Foundations of digital humanities

This course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...

Description

This lecture explores the concept of patrimonial capitalism within the context of the DH Circle, which encompasses the digitization, processing, analysis, and visualization of sources to create societal value. It discusses the implications of the DH Circle leading to the control of large cultural datasets by global actors and the potential risks associated with it. The lecture also delves into the challenges and opportunities of digitization, better services, and data extraction in the digital humanities field, emphasizing the importance of optimizing workflows and valorization processes. Additionally, it touches upon the ethical considerations surrounding the digitization of historical records and the impact of making genomic data freely available to researchers.

Instructor

Frédéric Kaplan

Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Short biography Pro

Official source

Related concepts (12)

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains.

Data scraping

Data scraping is a technique where a computer program extracts data from human-readable output coming from another program. Normally, data transfer between programs is accomplished using data structures suited for automated processing by computers, not people. Such interchange and protocols are typically rigidly structured, well-documented, easily parsed, and minimize ambiguity. Very often, these transmissions are not human-readable at all.

Data extraction

Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices.

Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

Related lectures (13)

Digital Humanities Internship Experience

Showcases a student's internship experience in developing a video search engine using machine learning.

Patrimonial Capitalism: Digitisation and DH Circle

Delves into patrimonial capitalism in digitisation projects and the importance of closing the loop for sustainability and societal impact.

Information Extraction: Approaches and Techniques

Covers Information Extraction approaches, including hand-written patterns and supervised learning.

Garzoni Project - KNIME Recipe 1: Person relations

Delves into the lives of young apprentices in Venice and demonstrates data analysis with KNIME.

Digital Urban History: Lausanne Time Machine