Lecture

Document Structure: Describing Content

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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 discusses the complexity of describing document content and the development of new open standards in the last decade to address this issue. It explores the regulated representation approach to create generic descriptions of document information structure, the extraction of document content, and the construction of world models. The lecture also covers the general structure of the document pipeline, challenges in modeling document structure and content, as well as the Open Annotation Model and its data model. It delves into the concepts of modeling content, circulations, dimensions, and homologous pairs in document analysis.

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

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Related concepts (32)

Data model

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The corresponding professional activity is called generally data modeling or, more specifically, database design.

Data modeling

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. It may be applied as part of broader Model-driven engineering (MDD) concept. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

Semantic data model

A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them.

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.

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.

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