The INESData Local Environment allows users Get familiar with the INESData connector. By running the local environment, users will be able to learn how the INESData connector works as well as see how various connectors interact with each other.

The environment is packaged and allows using the latest versions of INESData Connector (v0.2.0) and the INESData Interface Connector using docker images, using a simple: docker compose up. Among the main functionalities are:

• A Postman collection has been made available to make examples of interactions between local environment connectors in a simple way
• A repository for S3-type assets has been included using MinIO as part of the automated deployment of the connector
• First version of Vocabulary Management that allows defining the vocabularies of each Data Space at design time and automatically creating the UI for annotation
• Asset transfer is fully functional from the interface

Link to Github

We are proud to announce our participation in Data Week 2024, within the framework of the Data Spaces Symposium 2024, which was held in Darmstadt, Germany. During the session "Generative AI: BDVA's Members Experience", we had the opportunity to share our experiences and advances in generative artificial intelligence with other leaders in the sector.

This event has been a crucible of innovation and collaboration, where we will explore how generative AI is transforming data spaces and empowering new opportunities for the industry.

We can't wait to show you how INESData is at the forefront of this technological revolution! For more information, you can visit: Data Week 2024 - Generative AI Session

The portal of the Polytechnic University of Madrid has published a press release echoing the development, implications and potential of the INESData incubator.

The press release highlights that "Develop a Spanish Data Spaces Incubator to encourage the adoption of this type of technology and accelerate the development of a Data Spaces ecosystem in our country is the objective of INESData (Infrastructure for the Research of Data Spaces), a project led by the Polytechnic University of Madrid (UPM) that has received 5 million euros of financing from the Spanish UNICO R&D Cloud program."

You can find the entry on its portal here

Our colleagues Gabriela Argüelles Terrón, Patricia Martín Chozasy, and Víctor Rodríguez Doncel have published the article "Event Extraction and Semantic Representation from Spanish Workers' Statute Using Large Language Models" as part of the 36th International Conference on Legal Knowledge and Information Systems (JURIX 2023).

This work uses LLMs to process an important piece of Spanish legislation: the Workers' Statute. The proposed method extracts the relevant events in their articles using a GPT-3.5 model and represents the entities involved in the events and the relationships between them as RDF triplets. The experiments carried out to select a strategy include zero- and few-shot learning. Finally, this work proposes a strategy to elevate the extracted legal relationships to a legal knowledge graph.

You can find the article as part of the IOS Press Ebooks here

The portal of the Higher Technical School of Computer Engineers has published a press release echoing the development, implications and potential of the INESData incubator.

The press release highlights that "The INESData project (Infrastructure for the Research of Data Spaces distributed at UPM) is an initiative led by the Polytechnic University of Madrid (UPM) to through its research groups Ontology Engineering Group (OEG) and the Information Processing and Telecommunications Center (IPTC). Its main mission is to create an incubator of data spaces in Spain, with the aim of promoting research, development and. innovation in areas related to Data Space technology and promoting public-private collaboration."

You can find the entry on its portal here

Advances in technology and artificial intelligence (AI) have highlighted the importance of data. Currently, it is very common to use deep learning techniques, Machine Learning in English, for the creation of systems and services. Such techniques need to use a certain amount of data; Furthermore, depending on the quantity and quality of the data used, the system obtained will be of greater or lesser quality.

This practical guide aims to present the data spaces created in the INESData incubator and explain the creation process so that those interested in developing data spaces can have a practical reference. Given that the project is still in its early phases, this version of the guide will focus, above all, on explaining what data spaces are as well as analyzing the different international initiatives that serve as a reference.

You can find the link and download the document in the following link.