What is linked data
What is open data
What is the difference between linked and open data
How to publish linked data (5-star schema)
The economic and social aspects of linked data.
Open Data: Barriers, Risks, and OpportunitiesSlim Turki, Dr.
Despite the development of Open Data platforms, the wider deployment of Open Data still faces significant barriers. It requires identifying the obstacles that have prevented e-government bodies either from implementing an Open Data strategy or from ensuring its sustainability.
This paper presents the results of a study carried out between June and November 2012, in which we analyzed three cases of Open Data development through their platforms, in a medium size city (Rennes, France), a large city (Berlin, Germany), and at national level (UK). It aims to draw a clear typology of challenges, risks, limitations and barriers related to Open Data. Indeed the issues and constraints faced by re-users of public data differ from the ones encountered by the public data providers. Through the analysis of the experiences in opening data, we attempt to identify how barriers were overcome and how risks were managed. Beyond passionate debates in favor or against Open Data, we propose to consider the development of an Open Data initiative in terms of risks, contingency actions, and expected opportunities. We therefore present in this paper the risks to Open Data organized in 7 categories: (1) governance, (2) economic issues, (3) licenses and legal frameworks, (4) data characteristics, (5) metadata, (6) access, and (7) skills.
Sébastien Martin 1, Muriel Foulonneau 2, Slim Turki 2, Madjid Ihadjadene 1
1 Université Paris 8, Vincennes-Saint-Denis, France
2 PRC Henri Tudor, Luxembourg, Luxembourg
Presentation given at the conference "open data for impact"
Erasmus+ project "Public Makers"
https://www.linkedin.com/posts/wide-luxembourg_opendata-publicmakers-activity-6818166878473596928-7ImU/
Open Data Institute Course - Open Data in a Day conducted by Registered ODI Trainer Ian Henshaw on October 14, 2015 in RTP, NC USA - Deck #1 Introduction to Open Data
Briefing on US EPA Open Data Strategy using a Linked Data Approach3 Round Stones
An overview presented by Ms. Bernadette Hyland on 18-Nov 2014 on the US EPA Open Data strategy, focusing on the Resource Conservation & Recovery Act (RCRA) dataset to be published as linked data . This work is in support of Presidential Memorandum M13-13 - Open Data Policy and Managing Information as an Asset.
presented at the 2011 SemTech
Open government data and related services/applications are quickly growing on the Web. Although most agree that the government data has great potential in solving real world problems, there are still many challenges that must be addressed. This talk will describe several representative domain applications and provide concrete examples of evolving technical challenges remaining. We will show solution paths that have proven useful and make recommendations on the corresponding Semantic Web best practices.
• Scalability. How can we handle(e.g. search and cleanse) the 3,000+ raw/tool datasets, and the additional 300,000+ geo datasets from data.gov?
• Interoperability. Multi-scale open government data came from city governments, state governments, and national governments. How can one compare the GDP of the US and China, and later link to state-level financial data? Open government data covers many domains. How can one associate open government data with domain knowledge to build a cancer prevention application?
• Provenance and quality. How should provenance be leveraged to facilitate high-quality data management interactions (e.g. reuse, mash-up and feedback) between the government and the public?
Open Data: Barriers, Risks, and OpportunitiesSlim Turki, Dr.
Despite the development of Open Data platforms, the wider deployment of Open Data still faces significant barriers. It requires identifying the obstacles that have prevented e-government bodies either from implementing an Open Data strategy or from ensuring its sustainability.
This paper presents the results of a study carried out between June and November 2012, in which we analyzed three cases of Open Data development through their platforms, in a medium size city (Rennes, France), a large city (Berlin, Germany), and at national level (UK). It aims to draw a clear typology of challenges, risks, limitations and barriers related to Open Data. Indeed the issues and constraints faced by re-users of public data differ from the ones encountered by the public data providers. Through the analysis of the experiences in opening data, we attempt to identify how barriers were overcome and how risks were managed. Beyond passionate debates in favor or against Open Data, we propose to consider the development of an Open Data initiative in terms of risks, contingency actions, and expected opportunities. We therefore present in this paper the risks to Open Data organized in 7 categories: (1) governance, (2) economic issues, (3) licenses and legal frameworks, (4) data characteristics, (5) metadata, (6) access, and (7) skills.
Sébastien Martin 1, Muriel Foulonneau 2, Slim Turki 2, Madjid Ihadjadene 1
1 Université Paris 8, Vincennes-Saint-Denis, France
2 PRC Henri Tudor, Luxembourg, Luxembourg
Presentation given at the conference "open data for impact"
Erasmus+ project "Public Makers"
https://www.linkedin.com/posts/wide-luxembourg_opendata-publicmakers-activity-6818166878473596928-7ImU/
Open Data Institute Course - Open Data in a Day conducted by Registered ODI Trainer Ian Henshaw on October 14, 2015 in RTP, NC USA - Deck #1 Introduction to Open Data
Briefing on US EPA Open Data Strategy using a Linked Data Approach3 Round Stones
An overview presented by Ms. Bernadette Hyland on 18-Nov 2014 on the US EPA Open Data strategy, focusing on the Resource Conservation & Recovery Act (RCRA) dataset to be published as linked data . This work is in support of Presidential Memorandum M13-13 - Open Data Policy and Managing Information as an Asset.
presented at the 2011 SemTech
Open government data and related services/applications are quickly growing on the Web. Although most agree that the government data has great potential in solving real world problems, there are still many challenges that must be addressed. This talk will describe several representative domain applications and provide concrete examples of evolving technical challenges remaining. We will show solution paths that have proven useful and make recommendations on the corresponding Semantic Web best practices.
• Scalability. How can we handle(e.g. search and cleanse) the 3,000+ raw/tool datasets, and the additional 300,000+ geo datasets from data.gov?
• Interoperability. Multi-scale open government data came from city governments, state governments, and national governments. How can one compare the GDP of the US and China, and later link to state-level financial data? Open government data covers many domains. How can one associate open government data with domain knowledge to build a cancer prevention application?
• Provenance and quality. How should provenance be leveraged to facilitate high-quality data management interactions (e.g. reuse, mash-up and feedback) between the government and the public?
Extending Memory on the Web via Human-Centric Knowledge Exchange Network. Presented at W3C Workshop on Social Standards: The Future of Business, 7-8 August 2013, San Francisco, USA
EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of...European Data Forum
BIG - NESSI Networking Session, Talk by Edward Curry, National University of Ireland Galway at the European Data Forum 2014, 20 March 2014 in Athens, Greece: The Big Data Value Chain.
Open data, decision points and distribution of benefitsTim Davies
Slides from a presentation at the ICA 2014 Pre-conference on Data and Discrimination - http://oti.newamerica.net/events/2014/05/22/data-and-discrimination
In recent years governments and research institutions have emphasized the need for open data as a fundamental component of open science. But we need much more than the data themselves for them to be reusable and useful. We need descriptive and machine-readable metadata, of course, but we also need the software and the algorithms necessary to fully understand the data. We need the standards and protocols that allow us to easily read and analyze the data with the tools of our choice. We need to be able to trust the source and derivation of the data. In short, we need an interoperable data infrastructure, but it must be a flexible infrastructure able to work across myriad cultures, scales, and technologies. This talk will present a concept of infrastructure as a body of human, organisational, and machine relationships built around data. It will illustrate how a new organization, the Research Data Alliance, is working to build those relationships to enable functional data sharing and reuse.
This paper describes the NHS National Innovation Centre's Linked Data initiative. A discussion on the OHIO (Open Health Innovation Ontology) is also provided.
In a world where many of our digital spaces are becoming more closed than ever, open data is a concept that is rapidly on the rise.
In this talk we explore what open data is (and what it isn't), and why we should care about it. We'll look at how you can introduce it into your projects with regards to practical publication and consumption, and discuss some useful tools and reference points.
Open data isn't just dry and technical - it gives us great scope to be creative, and throughout this talk we'll go through some of the amazing things that it has been used for globally in the hope that it will inspire you to create something amazing yourself.
OPEN DATA: ECOSYSTEM, CURRENT AND FUTURE TRENDS, SUCCESS STORIES AND BARRIERSAnastasija Nikiforova
"OPEN DATA: ECOSYSTEM, CURRENT AND FUTURE TRENDS, SUCCESS STORIES AND BARRIERS" set of slides was prepared for the Guest Lecture, which I has delivered to the students of the University of South-Eastern Norway (USN), October 2021
EDF2014: Michele Vescovi, Researcher, Semantic & Knowledge Innovation Lab, It...European Data Forum
Selected Talk by Michele Vescovi, Researcher, Semantic & Knowledge Innovation Lab, Italy at the European Data Forum 2014, 19 March 2014 in Athens, Greece: Toward Personal Big Data passing through user Transparency, Control and Awareness: a Living-Lab Experience
Connecting the dots - e-Infra services for open scienceOpenAIRE
Starting from Open access towards services for open science, we present OpenAIRE, OpenMinTeD and OpenUP, three EU projects that build services to facilitate and accelerate open science.
Semantic Data Architecture and Ontological Infrastructure (ASIO)AdrinSaavedraSerrano
Semantic Data Architecture and Ontological Infrastructure (ASIO) is a pre-commercial public procurement project, whose objective is to develop two innovative solutions in relation to the challenge of Semantic Architecture and Ontological Infrastructure, likely to be used in the future on a regular basis both by the University of Murcia as well as the other Universities that are part of the CRUE, with similar needs and skills The objective of the Semantic Data Architecture project is to develop an efficient platform to store, manage and publish data from the Spanish University System (SUE) Research, based on ontologies, with the ability to synchronize instances installed in different Universities. To achieve this goal, the project has two companies that are researching, in parallel, the development of the "semantic architecture" component. This component will be natively connected to the SGI Hercules and in addition, it will be your Capable of connecting to any other management system through standard protocols. Additionally, and with the aim of allowing maximum flexibility to universities, you will be able to import research data in CVN and CERIF format. The main objective of the SUE Information Ontological Infrastructure project is to develop an ontological infrastructure, known as "Network of Ontologies Hercules" (ROH), which accurately and highly granularly describes the research domain.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
Extending Memory on the Web via Human-Centric Knowledge Exchange Network. Presented at W3C Workshop on Social Standards: The Future of Business, 7-8 August 2013, San Francisco, USA
EDF2014: BIG - NESSI Networking Session: Edward Curry, National University of...European Data Forum
BIG - NESSI Networking Session, Talk by Edward Curry, National University of Ireland Galway at the European Data Forum 2014, 20 March 2014 in Athens, Greece: The Big Data Value Chain.
Open data, decision points and distribution of benefitsTim Davies
Slides from a presentation at the ICA 2014 Pre-conference on Data and Discrimination - http://oti.newamerica.net/events/2014/05/22/data-and-discrimination
In recent years governments and research institutions have emphasized the need for open data as a fundamental component of open science. But we need much more than the data themselves for them to be reusable and useful. We need descriptive and machine-readable metadata, of course, but we also need the software and the algorithms necessary to fully understand the data. We need the standards and protocols that allow us to easily read and analyze the data with the tools of our choice. We need to be able to trust the source and derivation of the data. In short, we need an interoperable data infrastructure, but it must be a flexible infrastructure able to work across myriad cultures, scales, and technologies. This talk will present a concept of infrastructure as a body of human, organisational, and machine relationships built around data. It will illustrate how a new organization, the Research Data Alliance, is working to build those relationships to enable functional data sharing and reuse.
This paper describes the NHS National Innovation Centre's Linked Data initiative. A discussion on the OHIO (Open Health Innovation Ontology) is also provided.
In a world where many of our digital spaces are becoming more closed than ever, open data is a concept that is rapidly on the rise.
In this talk we explore what open data is (and what it isn't), and why we should care about it. We'll look at how you can introduce it into your projects with regards to practical publication and consumption, and discuss some useful tools and reference points.
Open data isn't just dry and technical - it gives us great scope to be creative, and throughout this talk we'll go through some of the amazing things that it has been used for globally in the hope that it will inspire you to create something amazing yourself.
OPEN DATA: ECOSYSTEM, CURRENT AND FUTURE TRENDS, SUCCESS STORIES AND BARRIERSAnastasija Nikiforova
"OPEN DATA: ECOSYSTEM, CURRENT AND FUTURE TRENDS, SUCCESS STORIES AND BARRIERS" set of slides was prepared for the Guest Lecture, which I has delivered to the students of the University of South-Eastern Norway (USN), October 2021
EDF2014: Michele Vescovi, Researcher, Semantic & Knowledge Innovation Lab, It...European Data Forum
Selected Talk by Michele Vescovi, Researcher, Semantic & Knowledge Innovation Lab, Italy at the European Data Forum 2014, 19 March 2014 in Athens, Greece: Toward Personal Big Data passing through user Transparency, Control and Awareness: a Living-Lab Experience
Connecting the dots - e-Infra services for open scienceOpenAIRE
Starting from Open access towards services for open science, we present OpenAIRE, OpenMinTeD and OpenUP, three EU projects that build services to facilitate and accelerate open science.
Semantic Data Architecture and Ontological Infrastructure (ASIO)AdrinSaavedraSerrano
Semantic Data Architecture and Ontological Infrastructure (ASIO) is a pre-commercial public procurement project, whose objective is to develop two innovative solutions in relation to the challenge of Semantic Architecture and Ontological Infrastructure, likely to be used in the future on a regular basis both by the University of Murcia as well as the other Universities that are part of the CRUE, with similar needs and skills The objective of the Semantic Data Architecture project is to develop an efficient platform to store, manage and publish data from the Spanish University System (SUE) Research, based on ontologies, with the ability to synchronize instances installed in different Universities. To achieve this goal, the project has two companies that are researching, in parallel, the development of the "semantic architecture" component. This component will be natively connected to the SGI Hercules and in addition, it will be your Capable of connecting to any other management system through standard protocols. Additionally, and with the aim of allowing maximum flexibility to universities, you will be able to import research data in CVN and CERIF format. The main objective of the SUE Information Ontological Infrastructure project is to develop an ontological infrastructure, known as "Network of Ontologies Hercules" (ROH), which accurately and highly granularly describes the research domain.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
morning session talk at the second Keystone Training School "Keyword search in Big Linked Data" held in Santiago de Compostela.
https://eventos.citius.usc.es/keystone.school/
An introduction to linked data (semantic web) for a Knowledge and Information Network (KIN) webinar. The presentation shows some examples of linked data in action, data visualization, difference between open and linked data and how linkd data is being used in UK gov and local gov.
This module supported the training on Linked Open Data delivered to the EU Institutions on 30 November 2015 in Brussels. https://joinup.ec.europa.eu/community/ods/news/ods-onsite-training-european-commission
Google's recent announcement that it will support the use of microformats in their search opens up new possibilities for librarians and library technologists to support the goals of the semantic web; namely to provide better access, reuse and recombinations of library resources and services on the open web. This lightning talk introduces the semantic web and semantic markup technologies.
Discovering Resume Information using linked data dannyijwest
In spite of having different web applications to create and collect resumes, these web applications suffer
mainly from a common standard data model, data sharing, and data reusing. Though, different web
applications provide same quality of resume information, but internally there are many differences in terms
of data structure and storage which makes computer difficult to process and analyse the information from
different sources. The concept of Linked Data has enabled the web to share data among different data
sources and to discover any kind of information while resolving the issues like heterogeneity,
interoperability, and data reusing between different data sources and allowing machine process-able data
on the web.
Talk given at Open Knowledge Foundation 'Opening Up Metadata: Challenges, Standards and Tools' Workshop, Queen Mary University of London, 13th June 2012.
Info on the event at http://openglam.org/2012/05/31/last-places-left-for-opening-up-metadata-challenges-standards-and-tools/
Nelson Piedra , Janneth Chicaiza
and Jorge López, Universidad Técnica Particular de Loja, Edmundo
Tovar, Universidad Politécnica de Madrid,
and Oscar Martínez, Universitas
Miguel Hernández
Explore the advantages of using linked data with OERs.
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Similar to Introducción a Linked Open Data (espacios enlazados y enlazables) (20)
In the era of digital transformation, the concept of Digital Twins has emerged as a revolutionary approach to managing and optimizing the lifecycle of physical assets, systems, and processes. This talk delves into the transformative potential of Digital Maintenance in the Digital Twin Era, highlighting the seamless integration of digital replicas with real-world operations to foster unprecedented levels of efficiency, predictability, and sustainability in maintenance practices. We will explore how Digital Twins serve as dynamic, real-time reflections of physical assets, allowing for meticulous monitoring, analysis, and simulation. Through vivid examples, we'll demonstrate the benefits of this paradigm, such as predictive maintenance, which leverages data analytics and machine learning to anticipate failures and optimize maintenance schedules, thereby reducing downtime and extending asset lifespan. Further, the talk will showcase the role of Digital Twins in facilitating remote maintenance operations. By providing a comprehensive, virtual view of assets, maintenance professionals can perform diagnostics and identify issues without being physically present, enhancing safety and reducing response times. We'll also explore the environmental benefits of Digital Maintenance within the Digital Twin framework. By optimizing maintenance schedules and operations, organizations can significantly reduce their carbon footprint and resource consumption, contributing to more sustainable industrial practices. Finally, the presentation will highlight case studies from various industries, including manufacturing, energy, and transportation, where the adoption of Digital Twins has led to substantial cost savings, improved operational efficiency, and enhanced decision-making processes. These examples will illustrate the tangible value and competitive advantage that Digital Maintenance in the Digital Twin Era offers to forward-thinking organizations.
Large Techno Social Systems (LTSS) involve leveraging technological advancements and digital platforms to improve access to essential services, enhance quality of life, and ensure social inclusivity. In LTSS, people cannot be mere users of networked technologies and services designed for optimization purposes. Their behaviour should become one of the key levers for designing technologies turning them into real “Smart citizens” that teach their surrounding environment (and embedded devices) but learn reciprocally from it. LTSS can be realized by promoting smart communities which leverage technology, data, and innovation to improve the quality of life for its residents, enhance sustainability, and optimize the use of resources. Human-centric technology can empower citizens to actively engage in societal decision-making processes, participate in deliberative systems, and contribute to societal welfare. On the other hand, technological advancements, including data analytics and artificial intelligence, can inform evidence-based policymaking and planning processes. Indeed, digital technologies have the potential to influence human behaviour change by providing information, personalized feedback, social support, targeted interventions, and opportunities for learning. This work explores two approaches to realize LTSS driven smart communities that leverage digital technologies to achieve a higher collaboration and reciprocal learning between machines and people. On one hand, co-production in smart communities promotes behaviour change by empowering citizens in the co-design and co-delivery process, designing user-centric solutions, leveraging local knowledge, fostering collaboration, and facilitating capacity building. On the other hand, Citizen Science can inspire and enable behaviour change that leads to more sustainable, responsible, and community-oriented actions by promoting awareness, empowering individuals, and facilitating collaboration.
realizing human-centric innovation around public services
From data collector to co-researcher - how to successfully collaborate with society
Delivered to UNIC CityLab 10 November 2022, 10:00-12:00, https://unic.eu/en
Towards more citizen-centric and sustainable public services
INTERLINK co-production methodology
INTERLINK’s key principles and concepts
INTERLINK Collaborative Environment
INTERLINK: co-production of public services
A public service is an aggregation of all activities that realize a public authority's commitment to make available to individuals, businesses, or other public authorities some capabilities intended to answer their needs, giving them some possibilities to control whether, how and when such capabilities are manifested
Co-production is defined as the process in which services are jointly designed and/or delivered by public authorities and other stakeholders
Internet of People is a new computing paradigm designed to enable Smart Sustainable Places which follow Social Good principles
Smart Sustainable Places =
IoT +
Big Data +
Blockchain +
People Participation through CO-PRODUCTION
FAIR Data
Principles
FAIR vs Open Data
Implementing FAIR & FAIRmetrics
FAIRness de ASIO-HERCULES
Research Objects
Definition
Standard RO-CRATE
Usage examples
Introducción a la Web de Datos
Grafos de Conocimiento
Web Semántica
Ontologías
Linked Data: Wikidata & Dbpedia
Ontología ROH: Red de Ontologías Hércules
Proceso de diseño de la ontología
Descripción de la ontología en detalle
Entidades principales explicadas en base a casos de uso
Generación de datos: IoP & Citizen science
Explosión datos + IA = Economía de Datos
Data Marketplaces: EDI & REACH
Explotación de los datos:
Ciudadanos co-idean, co-crean y co-explotan (WeLive)
Colaboración sostenible entre ciudadanos y personas (AUDABLOK)
More from Diego López-de-Ipiña González-de-Artaza (20)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Introducción a Linked Open Data (espacios enlazados y enlazables)
1. FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER)
Una manera de hacer Europa
Introducción a Linked Open Data (espacios enlazados y
enlazables)
Diego López-de-Ipiña
MORElab research group, Universidad de Deusto
dipina@deusto.es
2. FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER)
Una manera de hacer Europa
Temas a tratar
What is linked data
What is open data
What is the difference between linked and open data
How to publish linked data (5-star schema)
The economic and social aspects of linked data.
3. FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER)
Una manera de hacer Europa
Web of Data: Limitaciones de la Web de Documentos
3
Demasiada información con muy poca estructura y hecha además para consumo
humano
o Es una web sintáctica no semántica
o La búsqueda de contenidos es muy simplista
• Se requieren mejores métodos
Los contenidos web son heterogéneos
o En términos de contenido
o En términos de estructura
o En términos de codificación de caracteres
El futuro requiere integración de información inteligente
4. FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER)
Una manera de hacer Europa
What is Linked Data?
4
Evolution from a document-based Web to a Web of interlinked data
The Web is evolving from a “Web of linked documents” into a “Web of linked data”...
Web of documents... Web of linked data...
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The Web is evolving from a “Web of linked documents” into a “Web of
linked data”...
5
• The Web started as a collection of documents
published online – accessible at Web location
identified by a URL.
• These documents often contain data about
real-world resources which is mainly human-
readable and cannot be understood by
machines.
• The Web of Data is about in machine-readable
enabling the access to this data, by making it
available formats and connecting it using Uniform
Resource Identifiers (URIs), thus enabling people
and machines to collect the data, and put it
together to do all kinds of things with it
(permitted by the licence).
• Machine-readable data (or metadata)
is data in a format that can be
interpreted by a computer.
• 2 types of machine-readable data:
• human-readable data that is marked
up so that it can also be understood
by computers, e.g.
microformats, RDFa;
• data formats intended principally
for computers, e.g. RDF, XML and
JSON.
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Democratizando la web semántica: Metadatos empotrados
6
Necesitamos que nuestros datos estén preparados para responder adecuadamente a las
preguntas de los navegadores y agentes software
o “Embedded metadata” son datos sobre datos empotrados en una página web que pueden ser extraídos
por buscadores y agentes de búsqueda
Tres opciones principales:
o RDFa – sistema complejo conectado a XHTML
o Microformats – ampliamente usado y apoyado, usan etiquetas XHTML antiguas
<a href="http://jane-blog.example.org/" rel="sweetheart date met">Jane</a>
o Microdata – más nuevo, soportado por los buscadores, nivel de complejidad intermedio
<div itemscope itemtype="http://schema.org/SoftwareApplication">
<span itemprop="name">Angry Birds</span> -
REQUIRES <span itemprop="operatingSystem">ANDROID</span><br>
<link itemprop="applicationCategory" href="http://schema.org/GameApplication"/>
</div>
¡¡Todas juntas nos ayudarán a alcanzar la visión de una web con más
significado, pero todavía comprensible tanto a humanos como máquinas!!
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Democratizando las ontologías: Schema.org
7
Initiative launched in 2011 by Bing, Google, Yahoo and then Yandex
Objective: “create and support a common set of schemas for structured data mark-up on web pages.”
o Propose to use their schemas to annotate contents in a web page with metadata
Metadata are recognized by search engines and other parsers, thus accessing to the “meaning” of portals
Their vocabularies were inspired by earlier formats like Microformats, FOAF, GoodRelations and OpenCyc
Offer schemas in the following domains (http://schema.org/docs/schemas.html):
o Events, health, organization, person, place, product, offer, revisión and so on.
To map declarations in microdata to RDF the following tools can be used:
o http://tools.seochat.com/category/schema-generators
More info at: http://schema.org/
Examples:
http://schema.org/CreativeWork
http://paginaspersonales.deusto.es/dipina/ (microdata.reveal Chrome plugin)
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Defining Linked Data
8
• “Linked data is a set of design principles for sharing machine-readable data on the
Web for use by public administrations, business and citizens.”
• EC ISA Case Study: How Linked Data is transforming eGovernment
The four design principles of Linked Data (by Tim Berners Lee):
1. Use Uniform Resource Identifiers (URIs) as names for things.
2. Use HTTP URIs so that people can look up those names.
3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL).
4. Include links to other URIs so that they can discover more things.
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LinkedData
9
“A term used to describe a recommended best practice for exposing, sharing, and
connecting pieces of data, information, and knowledge on the Semantic Web
using URIs and RDF.“
Permite descubrir, conectar, describir y reutilizar todo tipo de datos.
o Pasa de una Web de Documentos a una Web de Datos
• En Septiembre 2011 ya contenía 31 billones de tripletas RDF, ligadas por 504millones de enlaces
Pensado para abrir y conectar diversos vocabularios e instancias semánticas, para
que puedan ser utilizados por la comunidad semántica
URL: https://www.w3.org/standards/semanticweb/data
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Ejemplo de Linked Data
10
http://…/isb
n978
Programming the
Semantic Web
978-0-596-15381-6
Toby Segaran
http://…/publi
sher1
O’Reilly
title
name
author
publisher
isbn
http://…/isb
n978
sameAs
http://…/rev
iew1
Awesome
Book
http://…/rev
iewer
Juan
Sequeda
http://juanseque
da.com/id
hasReview
hasReviewer
description
name
sameAs
livesIn
Juan Sequeda
name
http://dbpedia.org/Austin
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Visualizing Linked Data
11
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Linked (open) government data (LOGD) – value proposition
12
Flexible data integration: LOGD facilitates data integration and enables the
interconnection of previously disparate government datasets.
Increase in data quality: The increased (re)use of LOGD triggers a growing demand to
improve data quality. Through crowd-sourcing and self-service mechanisms, errors
are progressively corrected.
New services: The availability of LOGD gives rise to new services offered by the public
and/or private sector.
Cost reduction: The reuse of LOGD in e-Government applications leads to
considerable cost reductions.
Example:
o EU Open Data Portal: https://data.europa.eu/euodp/en/home
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The four principles in practice... (1)
13
1. Use Uniform Resource Identifiers (URIs) as names for things.
2. Use HTTP URIs so that people can look up those names.
o E.g. for an organisation: UNICEF
http://publications.europa.eu/resource/authority/corporate-body/UNICEF
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The four principles in practice... (2)
14
3. When someone looks up a URI, provide useful information, using the standards
(RDF*, SPARQL).
4. Include links to other URIs so that they can discover more things.
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Linked Data vs. Open Data
15
“Open data is data that can be freely used, reused and redistributed by anyone – subject
only, at most, to the requirement to attribute and sharealike.”
- OpenDefinition.org
Open data
Data can be published and be
publicly available under an open
licence without linking to other
data sources.
Linked data
Data can be linked to URIs from
other data sources, using open
standards such as RDF without
being publicly available under an
open licence.
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4 reglas de Linked Data
16
1. Usa URIs para identificar cosas
2. Usa URIs HTTP para que estas cosas puedan ser referenciadas y dereferenciadas por
gente y agentes de usuario
3. Proporciona información útil (descripción estructurada y metadatos) sobre la
cosa/concepto al que referencia la URI
4. Incluye enlaces a otras URIs para mejorar el descubrimiento de información relacionada
en la Web
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Linked Data Foundations: URIs
17
URIs for naming things, RDF for describing data and SPARQL for querying it
o “A Uniform Resource Identifier (URI) is a compact sequence of characters that identifies an abstract
or physical resource.”
– ISA’s 10 Rules for Persistent URIs
Examples:
o A country, e.g. Spain
o http://publications.europa.eu/resource/authority/country/ESP
o An organisation, e.g. the Publications Office
o http://publications.europa.eu/resource/authority/corporate-body/PUBL
o A dataset, e.g. Countries Named Authority List
o http://publications.europa.eu/resource/authority/country
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Linked Data Foundations: RDF & SPARQL
18
The Resource Description Framework (RDF ) is a syntax for representing data and
resources in the Web
SPARQL is a standardised language for querying RDF data.
RDF breaks every piece of information down in triples:
o Subject – a resource, which may be identified with a URI.
o Predicate – a URI-identified reused specification of the relationship.
o Object – a resource or literal to which the subject is related.
http://example.org/place/Madrid is the capital of “Spain”.
OR
http://example.org/place/Madrid is the capital of http://example.org/place/Madrid.
Subject Predicate Object
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Manifestaciones de Linked Data
19
Los datos publicados como LinkedData puede seguir la siguiente
clasificación, según Tim Bernes-Lee:
o 1 estrella: datos disponibles en la web (en cualquier formato), pero con una
licencia abierta
o 2 estrellas: datos disponibles son estructurados y legibles por máquinas. Por
ejemplo, Microsoft Excel en vez de una imagen escaneada de una tabla.
o 3 estrellas: los datos disponibles como en (2) pero no siguen un formato
propietario. Por ejemplo, CSV en vez de Excel.
o 4 estrellas: los datos son dispuestos de manera abierta usando un estándar
abierto de W3C (RDF y SPARQL) para identificar cosas, de modo que la gente
los pueda enlazar.
o 5 estrellas: los datos son dispuestos siguiendo lo anterior, incluyendo enlaces
externos a los datos de otra gente.
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How to publish Linked Data?
20
Paving the way towards 5-star linked data
★ Make your stuff available on the Web (whatever format) under an open license.
★★ Make it available as structured data (e.g., Excel instead of image scan of a table)
★★★ Use non-proprietary formats (e.g., CSV instead of Excel)
★★★★ Use URIs to denote things, so that people can point at your stuff
★★★★★ Link your data to other data to provide context
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★ Make your stuff available on the web under an open license
21
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Pros & Cons of ★ Open Data
22
As a consumer... As a publisher...
You can access the data. It is simple to publish.
You can store it locally. You do not have explain repeatedly to
others that they can use your data.
You can enter the data into any other
system.
You can change the data.
You can share the data with anyone.
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★ ★ Make it available as structured data
23
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Pros & Cons of ★ ★ Open Data
24
All the benefits of ★ open data; plus
As a consumer... As a publisher...
You can directly process it with
proprietary software to aggregate it,
perform calculations, visualise it, etc.
It is still simple to publish.
You can export it into another
(structured and/or non proprietary)
format.
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★ ★ ★ Use non-proprietary formats
25
Proprietary: Excel, Word, PDF...
Non-proprietary: XML, CSV, RDF, JSON, ODF...
Road safety- Accidents 2006:
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Pros & Cons of ★ ★ ★ Open Data
26
All the benefits of ★ ★ open data; plus
As a consumer... As a publisher...
You can manipulate the data in any
way you like, without being confined
by the capabilities of any particular
software.
It is still simple to publish.
- But, you may need converters or
plug-informat.s to export the data from
the proprietary
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★ ★ ★ ★ Use URIs to denote things
27
Por ejemplo, Comercios situados en el Municipio de Santander principalmente
dedicados a la venta al por menor.
https://datos.gob.es/es/catalogo/l01390759-comercios
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Pros & Cons of ★ ★ ★ ★ Open Data
28
All the benefits of ★ ★ ★ ★ open data; plus
As a consumer... As a publisher...
You can link to it from any other place. Other data publishers can now link into your data,
promoting it to 5 star.
You can bookmark it. You will be able to reuse vocabularies, data and
metadata, and URI design patterns instead of creating
them from scratch.
You can access information about a particular resource
directly through its URI, without having to download the
complete dataset.
You may be able to reuse existing tools
and libraries.
- But you typically need to invest some time in identifying
the resources and assigning URIs.
You can combine the data with other data. - You need to invest in a stable policy, management and
infrastructure for persistent URIs.
- But understanding the technology requires effort and can have a steep learning curve.
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★ ★ ★ ★ ★ Link your data to other data to provide context
29
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Pros & Cons of ★ ★ ★ ★ Open Data
30
All the benefits of ★ ★ ★ ★ open data; plus
As a consumer... As a publisher...
You can discover more (related) data while
consuming the data.
You make your data discoverable.
You can directly learn about the data schema. You increase the context, expressivity, quality
and value of your data (and consequently you
give visibility to your organisation).
You can combine data from different source,
be innovative, gain new knowledge, be an
entrepreneur...
- This requires an investment in time, money,
technology and competencies/ skills.
- But, you now have to deal with broken data links. Not all publishers/data sources will be reliable.
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Prepare your data for publishing – LOD lifecycle
31
LOGD and metadata lifecycle focusing on supply and demand
Supply Demand
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Selection of high-value data
32
Several dimensions can be considered in the selection process of Linked Open
Government Data, both from the publisher’s and the re-user’s point of view:
o Transparency: Does the publication of the dataset increase transparency and openness of the
government towards its citizens?
o Legal requirements: Is there a law that makes open publication mandatory or is there no specific
obligation?
o Relation to public task: Is the data the direct result of the primary public task of government or is it
a product of a non-essential activity?
o Current status of open publication: Is the data already openly available or does it still need to be
opened up?
o Type of value: Is the data useful for social engagement or does it have commercial value?
o Audience: Is the data primarily intended for the public or is it primarily aimed at back-office
integration?
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Modelling your data & metadata is about ...
33
Making your data available in a structured, comprehensible and machine-readable
way.
Reusing what already exists in terms of vocabularies and reference data.
Reaching the right quality level by cleansing your data.
Providing licensing information so that data consumers know what the conditions of
reuse are.
Providing a rich description (metadata).
Using semantic technologies (RDF, HTTP URIs...) for describing your data.
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Cleansing your data & metadata
34
To ensure data and metadata can be published with an appropriate level of quality
and minimum errors.
o This means:
• Fixing errors.
• Transforming/homogenising formats.
• Aligning inconsistencies in data and metadata.
• Removing duplicate/redundant information.
• Adding lacking information.
• Making sure the information is up-to-date.
OpenRefine tool
• Portal: https://openrefine.org/
• Video demostrador:
• https://www.youtube.com/watch?v=tzXExfZCA1w
• GitHub: https://github.com/OpenRefine/OpenRefine
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Cleansing your data – example
35
Company_name Registration date Country E-mail # Establishments
Nikè 1991-04-28 Belgium niké 7
BARCO 15 September 1986 BE Barco@email.be 2
Nikè België
Coca-Cola United States coca@cola.com 3
Formatting
issue
Missing
information
Duplicate error
Redundant
information
Inconsistent
information
Company_name Registration date Country E-mail
Nikè 1991-04-28 BE niké@sport.org
BARCO 1986-09-05 BE Barco@email.be
Coca-Cola 1964-03-26 US coca@cola.com
Cleansing
operations
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Publishing linked data is about ...
36
Breaking down the walls of the silos in order to create more value.
o Making your data and metadata publicly and easily accessible on the Web.
o Linking your data and metadata to other data (or metadata) in order to:
• Attach meaning and content to it.
• Give context to it.
• Enrich it.
• Allow people to discover more.
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Ejemplos de fuentes de datos públicas reutilizables
37
Datos Abiertos de Zaragoza https://www.zaragoza.es/sede/portal/datos-abiertos/api
Dades Obertes Manlleu: https://dadesobertes.diba.cat/dades-obertes/documentacio-tecnica/api
Aemet OpenData API: https://opendata.aemet.es/
Catálogo de APIs Abiertas ISTAC: https://www3.gobiernodecanarias.org/aplicaciones/appsistac/api
EMT mobilitylabs: https://mobilitylabs.emtmadrid.es/es/portal/opendata
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WikiData & DBpedia
38
Wikidata is a volunteer-created knowledge base of structured data that anyone can edit
o Focused on structured data: possible for humans and computers alike to use the data
o Many ways to contribute to Wikidata: translate, write apps, add and edit data.
o It works with:
• Items – abstract concepts with theirs own and a unique identifier (Q###) and optionally a label, description and aliases
• Statements are added to items: category of data as a property, while the data that describes an item for a given property is known as
a value.
o Example: entry for Everest mountain https://www.wikidata.org/wiki/Q513
o Documentation: https://www.wikidata.org/wiki/Wikidata:Tours
o Wikidata query service: https://query.wikidata.org/
DBpedia, a project to create a graph from Wikipedia data – allows users to semantically query
relationships and properties associated with Wikipedia resources, including links to other related
datasets
o Wikipedia articles consist mostly of free text, but also include structured information embedded in the articles,
such as "infobox" tables, categorisation information, images, geo-coordinates and links to external Web pages.
• This structured information is extracted and put in a uniform dataset which can be queried: http://live.dbpedia.org/sparql
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WikiData & DBpedia
39
There are 4 main differences:
o Wikidata provides data to Wikipedia, while DBpedia extracts data from Wikipedia.
o Wikidata's ontology is community curated, and part of the data maintained on the site, while DBpedia’s ontology is
statically defined, and much stricter.
o Formally, Wikidata only asserts statements (who claims what), while DBpedia asserts facts, often causing
contradictions.
o Wikidata is licensed CC-0, and is this re-usable without any restrictions, while DBpedia is licensed CC-BY-SA, which
requires author attribution - which is a good thing generally, but impractical for a knowledge base automatically derived
from text.
More info: https://www.quora.com/How-is-Wikidata-related-to-Wikipedia-in-a-way-different-from-
how-DBpedia-is-related-to-Wikipedia
Examples:
o Listar los nombres en castellano de los países en Dbpedia
o Countries sorted by population in WikiData
PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?nombre WHERE {
?pais rdf:type dbo:Country .
?pais rdfs:label ?nombre .
FILTER (lang(?nombre)='es')
}
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Iniciativa Aporta – https://datos.gob.es/es/acerca-de-la-iniciativa-aporta
40
La Iniciativa Aporta es la estrategia nacional de coordinación y de impulso de la apertura de datos
procedentes del sector público, y de promoción del desarrollo de servicios avanzados basados ellos.
Objetivos:
o Impulsar y coordinar la apertura de los datos generados por el sector público.
o Estimular un mercado ligado a la reutilización de la información del sector público.
o Contribuir a favorecer las condiciones para el desarrollo de la Estrategia europea de datos en España.
Sensibilización Análisis y
Estadísticas
Cooperación
Internacional
Regulación
Cooperación
Nacional
Catálogo Nacional
y Soporte Innovación
https://datos.gob.es/ Punto de encuentro entre las
administraciones, las empresas y los ciudadanos que forman
parte del ecosistema de datos abiertos en España
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41
LOS DATOS, MOTOR DEL MUNDO
2018
33
zettabytes
almacenados en tabletas de 512 GB,
formarían una torre que llegaría a la luna.
bastarían para hacer cinco veces el camino
de ida y vuelta a la luna.
2025
175
zettabytes
5 veces más Estos datos son un elemento
fundamental para facilitar la
toma de decisiones y generar
valor económico y social.
En 2018, el valor de la economía de
datos superó los 300 mil millones de
euros en la UE28, con un crecimiento
del 12% con respecto al año anterior.*
Fuente: Unión Europea, 2020
Cada año crece el volumen de datos en el mundo.
Unos datos generados tanto por empresas, como por el conjunto de la sociedad.
CRECERÁ EL VOLUMEN GLOBAL DE DATOS:
*Fuente: DATA ASTHE ENGINE OF EUROPE’S DIGITAL FUTURE, IDC, 2019
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42
• Los datos abiertos son la materia prima de nuevos servicios, productos y aplicaciones.
• Las empresas infomediarias crean productos y servicios de valor añadido con datos públicos y privados.
En España, el sector generó 2.000 millones de euros y empleó a más de 15.000 personas en 2018 *
*Fuente: informe “Del sector infomediario a la economía del dato. Parte I. Caracterización del Sector Infomediario“, ONTSI .
•+ 15,4%
• Volumen de negocio
(2015-2018)
•+ 14,3%
• Empleados
(2016-2018)
EJEMPLOS
Turismo:
análisis de tendencias de
viajeros
Energía:
medidores inteligentes que
tienen en cuenta las condiciones
atmosféricas
Sector Inmobiliario:
análisis de precios del
mercado y condiciones de las
distintas zonas
Agricultura:
datos del suelo o del tiempo
para optimizar el riego
EL USO DE LOS DATOS EN LAS EMPRESAS
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Conclusions
43
Linked data is a set of design principles for sharing machine-readable
data on the Web.
Linked data and open data are not the same.
URIs, RDF and SPARQL form the foundational layer for Linked data.
Linked data offers a number of advantages for:
o Data integration with small impact on legacy systems;
o Enables for semantic interoperability;
o Enables creativity and innovation through context and knowledge-creation.
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Referencias
44
Iniciativa Aporta: https://datos.gob.es/es/acerca-de-la-iniciativa-aporta
o Presentación: http://ondemand2.redes.ondemand.flumotion.com/redes/ondemand2/EX-
24956/PresentacionMultimedia_07.09.20.pptx
European Data Portal – Introduction to Linked Data, training module:
https://www.europeandataportal.eu/sites/default/files/d2.1.2_training_module_1.2_introduction_
to_linked_data_en_edp.pdf
Guía práctica para la publicación de Datos Abiertos usando APIs – Iniciativa Aporta
o https://datos.gob.es/es/documentacion/guia-practica-para-la-publicacion-de-datos-abiertos-usando-apis
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Red Ontologías Hércules – ROH
Diego López-de-Ipiña
MORElab research group, Universidad de Deusto
dipina@deusto.es