Linked Data

Tim Berners-Lee is the man behind the concept of the Semantic Web. He coined the term in 2001 in his seminal article “Semantic Web”, published in the Scientific American. He is also the author of the concept of Linked Data, which he developed based on a design note regarding the construction of the Semantic Web project.

Linked Data is the name of a structured method of publishing data that, in practice, is what makes navigating with hyperdata and Knowledge Graph construction possible.  This publication method enables people to query the data of a graph semantically.

In order to publish according to the principles of the Linked Data Web, standards such as HTTP, RDF and URIs must be used, not so much for the purpose of displaying the pages that people read as for editing the pages so that they can be interpreted automatically by systems, and therefore share information. This is what makes it possible to connect data from various sources in a unified, searchable graph.

At GNOSS, from the very beginning, the Museo del Prado Online project aimed to integrate all the Museum’s resources into a Knowledge Graph. The goal of the project was to build a new presence for the Museum online, to improve users’ experience during their interaction with the Museum’s resources, and to integrate and link the Museum’s complete production into a unified graph. To put it another way: to convert all the data from all its systems into hyperdata. It has been demonstrated that this focus has had a significant impact on the way the Museum operates because it directly connects processes of creation and knowledge generation with publication and knowledge discovery processes. Focus is placed on the use of the Museum’s data for the improvement of its own processes and not solely on their reuse by third parties.

Likewise, when one transcends the perspective of publishing data for presumed reuse and adopts the vision of developing utilities for diverse audiences, including groups of interest to the institution itself, the result can significantly transform the production and consumption models for the materials that comprise institutional heritage and knowledge.

What is a graph?

Graph, in Greek, means “drawing”.  From a technical perspective, “graph” in maths and computation sciences refers to a set of objects called vertices and nodes connected by arcs and edges, which represent the relationships between the elements of a set. The word “graph” is used to describe the means by which these mathematical objects are frequently represented as a set of points (vertices) united by lines (edges). Graph theory concerns the study of this mathematical structure.

When theory is applied into practice, graphs are what allow the relationships between units to be studied. A network of computers is one example, as is the set of implicit relationships between the books in library, works of art in a museum, a set of scientific articles, or a given set of clinical trials.

Graph theory allows practical applications and exploitations to be represented, formalised and developed for an extremely wide set of problems.

Knowledge Graphs

A Knowledge Graph is a system that represents a set of digital resources and content that, based on an ontological model, understand facts related to the knowledge objects or entities within a specific knowledge area, and in particular, understand the method by which this set of entities is connected. When we say that this system “understands”, we mean that it is written in technical language that enables machines or systems to “understand” and correctly handle the group of entities mentioned in order to provide reliable recommendation systems and to collaborate with people when they query, retrieve information, discover knowledge, and learn.

Knowledge Graphs are a fundamental aspect of artificial intelligence projects. They supply a searchable, cognitive means by which to navigate. Based on the requests of users, they enable inference and suggest new relations or narratives linked with the knowledge areas in question.