The document is an assignment for a Semantic Web course. It includes questions and answers about key concepts of the Semantic Web, such as the meaning of the term "Semantic Web", why data interoperability on the web is difficult, why DBpedia is important for linking data, and the four rules of linked data. It also lists and describes four datasets from linkeddata.org and the ontologies used by each.
The document discusses several options for publishing data on the Semantic Web. It describes Linked Data as the preferred approach, which involves using URIs to identify things and including links between related data to improve discovery. It also outlines publishing metadata in HTML documents using standards like RDFa and Microdata, as well as exposing SPARQL endpoints and data feeds.
The document discusses different options for publishing metadata on the Semantic Web, including standalone RDF documents, embedding metadata in web pages using techniques like RDFa, providing SPARQL endpoints, publishing feeds, and using automated tools. It provides examples and discusses the advantages of each approach. A brief history of metadata publishing efforts is also presented, from early initiatives like HTML meta tags and SHOE to current standards like RDFa and microformats.
Year of the Monkey: Lessons from the first year of SearchMonkeyPeter Mika
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using techniques like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly discusses the history of embedding metadata in HTML and examples of metadata standards.
The document discusses the semantic web and ontology inference. It describes how ontologies are used on the semantic web to represent knowledge through concepts and relationships. It then explains different types of ontology inference including TBox inference, ABox inference, and rule-based inference using languages like SWRL. Examples of inference engines that support ontology reasoning are also provided.
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using formats like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly reviews the history of embedding metadata in HTML and examples of formats used.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It defines Linked Data as a set of best practices for publishing and connecting structured data on the web using URIs, HTTP, RDF, and other standards. It also explains semantic web technologies like RDF, ontologies, SKOS, and SPARQL that enable representing and querying structured data on the web. Finally, it discusses how libraries are applying these concepts through projects like BIBFRAME, FAST, library linked data platforms, and the LD4L project to represent bibliographic data as linked open data.
The document discusses the evolution of the concept of a web resource from early notions of static documents and files to a more abstract definition encompassing any entity that can be identified on the web. It describes how resources were initially implied to be addressable objects like files, but the definition has expanded to include abstract concepts identified by URIs. The document also examines how resources are described using RDF and the semantic web, the use of HTTP URIs to identify abstract resources, and issues of resource ownership and intellectual property.
This document provides an overview of linked data and the linking open data project. It discusses linked data principles, including using URIs to identify things and including links between data. It also describes the web of data 101 including URIs, HTTP, and RDF. The document outlines the linking open data community project and its goal of interlinking open datasets. It provides examples of datasets in the project like DBpedia and Geonames. Finally, it discusses some tools and applications for working with linked data.
The document discusses several options for publishing data on the Semantic Web. It describes Linked Data as the preferred approach, which involves using URIs to identify things and including links between related data to improve discovery. It also outlines publishing metadata in HTML documents using standards like RDFa and Microdata, as well as exposing SPARQL endpoints and data feeds.
The document discusses different options for publishing metadata on the Semantic Web, including standalone RDF documents, embedding metadata in web pages using techniques like RDFa, providing SPARQL endpoints, publishing feeds, and using automated tools. It provides examples and discusses the advantages of each approach. A brief history of metadata publishing efforts is also presented, from early initiatives like HTML meta tags and SHOE to current standards like RDFa and microformats.
Year of the Monkey: Lessons from the first year of SearchMonkeyPeter Mika
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using techniques like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly discusses the history of embedding metadata in HTML and examples of metadata standards.
The document discusses the semantic web and ontology inference. It describes how ontologies are used on the semantic web to represent knowledge through concepts and relationships. It then explains different types of ontology inference including TBox inference, ABox inference, and rule-based inference using languages like SWRL. Examples of inference engines that support ontology reasoning are also provided.
This document discusses publishing content on the Semantic Web. It introduces basic concepts of RDF and the Semantic Web like resources, literals, and triples. It then describes six main ways to publish RDF data on the web: 1) standalone RDF documents, 2) metadata inside webpages using formats like RDFa, 3) SPARQL endpoints, 4) feeds, 5) XSLT transformations, and 6) automatic markup tools. Finally, it briefly reviews the history of embedding metadata in HTML and examples of formats used.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It defines Linked Data as a set of best practices for publishing and connecting structured data on the web using URIs, HTTP, RDF, and other standards. It also explains semantic web technologies like RDF, ontologies, SKOS, and SPARQL that enable representing and querying structured data on the web. Finally, it discusses how libraries are applying these concepts through projects like BIBFRAME, FAST, library linked data platforms, and the LD4L project to represent bibliographic data as linked open data.
The document discusses the evolution of the concept of a web resource from early notions of static documents and files to a more abstract definition encompassing any entity that can be identified on the web. It describes how resources were initially implied to be addressable objects like files, but the definition has expanded to include abstract concepts identified by URIs. The document also examines how resources are described using RDF and the semantic web, the use of HTTP URIs to identify abstract resources, and issues of resource ownership and intellectual property.
This document provides an overview of linked data and the linking open data project. It discusses linked data principles, including using URIs to identify things and including links between data. It also describes the web of data 101 including URIs, HTTP, and RDF. The document outlines the linking open data community project and its goal of interlinking open datasets. It provides examples of datasets in the project like DBpedia and Geonames. Finally, it discusses some tools and applications for working with linked data.
This tutorial explains the Data Web vision, some preliminary standards and technologies as well as some tools and technological building blocks developed by AKSW research group from Universität Leipzig.
This document discusses the Semantic Web and Linked Open Data. It explains how the Semantic Web helps integrate data by using shared vocabularies and URIs to normalize meanings between data sources. As more datasets adopt Semantic Web principles by exposing structured data through URIs and RDF formats, individual datasets become less isolated and are interconnected to form a large knowledge base. The document provides examples of querying and exploring Linked Open Data through SPARQL and the LOD Cloud. It also offers recommendations for publishing and working with Linked Open Data.
The document discusses the semantic web and how it can potentially disrupt or benefit online commerce. It provides definitions and explanations of key concepts related to the semantic web including RDF, ontologies, linked data, and semantic search. It outlines how search engines and websites are increasingly adopting and leveraging semantic web technologies like RDFa to provide richer search results and experiences for users.
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
This document introduces linked data and discusses how publishing data as linked RDF triples on the web allows for a global linked database. It explains that linked data uses HTTP URIs to identify things and links data from different sources to be queried using SPARQL. Publishing linked data provides benefits like being able to integrate and discover related data on the web. Tools are available to convert existing data or publish new data as linked open data.
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
Talk delivered at YOW! Developer Conferences in Melbourne, Brisbane and Sydney Australia on 1-9 December 2016.
Abstract: Governments collect a lot of data. Data on air quality, toxic chemicals, laws and regulations, public health, and the census are intended to be widely distributed. Some data is not for public consumption. This talk focuses on open government data — the information that is meant to be made available for benefit of policy makers, researchers, scientists, industry, community organisers, journalists and members of civil society.
We’ll cover the evolution of Linked Data, which is now being used by Google, Apple, IBM Watson, federal governments worldwide, non-profits including CSIRO and OpenPHACTS, and thousands of others worldwide.
Next we’ll delve into the evolution of the U.S. Environmental Protection Agency’s Open Data service that we implemented using Linked Data and an Open Source Data Platform. Highlights include how we connected to hundreds of billions of open data facts in the world’s largest, open chemical molecules database PubChem and DBpedia.
WHO SHOULD ATTEND
Data scientists, software engineers, data analysts, DBAs, technical leaders and anyone interested in utilising linked data and open government data.
The document discusses the history and components of the internet. It defines the internet as a global network of interconnected computer networks that use standard protocols to serve billions of users worldwide. It describes the world wide web as a global set of documents and resources linked by hyperlinks. Key components that enable access to the internet are internet service providers, web browsers, hypertext transfer protocol, and uniform resource locators. Search engines help users find information on the world wide web through algorithms that crawl websites in real-time.
Learning Resource Metadata Initiative: Vocabulary Development Best PracticesMike Linksvayer
This document discusses best practices for developing learning resource metadata vocabularies based on guidelines from the Dublin Core Metadata Initiative. It recommends defining clear use cases, selecting an appropriate domain model, reviewing existing vocabularies to reuse terms, designing detailed metadata records, providing usage guidelines, and engaging relevant communities to ensure long-term stewardship of the vocabulary. The Learning Resource Metadata Initiative (LRMI) could benefit from following these best practices in its development.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
reegle - a new key portal for open energy datareeep
A new key portal for Open Energy Data
A new portal called reegle provides open energy data. It offers clean energy search, country energy profiles, an actors catalog, and energy glossary. Datasets labeled as open can be accessed freely in machine-readable formats at data.reegle.info using standards like RDF. Reegle aims to accelerate clean energy by enabling more transparent, efficient use and reuse of information through open data and linked open data approaches.
The document provides an overview of semantic technologies for representing semantic data. It discusses why semantics are needed, describes common metadata models like XML, RDF, and RDFa. It explains how RDF uses triples to represent knowledge as graphs and can be serialized in formats like XML, Notation3, and Turtle. It also discusses how RDFa allows embedding RDF within web pages to add semantics.
This document discusses various use cases for linked data and semantic web technology, including linked data for cross-domain knowledge bases like DBpedia and Freebase, linked geographic data like GeoNames and LinkedGeoData, linked government data from data portals like Data.gov and data.gov.uk, linked media data from projects like MusicBrainz, BBC, and LinkedMDB, linked data for user generated content from projects like flickr wrappr and Revyu.com, and linked life science data. It provides an overview of the concepts of linked data, RDF, URIs and describes several popular linked open datasets.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
From the Semantic Web to the Web of Data: ten years of linking upDavide Palmisano
This document discusses the concepts and technologies behind the Semantic Web. It describes how RDF, RDF Schema, and OWL allow structured data and relationships to be represented and shared across the web. It also discusses tools for working with semantic data in Java, such as Jena, Sesame, and Any23 for extracting and working with RDF. The document provides examples of representing data and relationships in RDF and querying semantic data with SPARQL.
This document describes the generation of two Linked Data datasets from sensor data - LinkedSensorData and LinkedObservationData. LinkedSensorData contains descriptions of about 20,000 weather stations in the US with links to sensor observations. LinkedObservationData contains over a billion triples of sensor observations during major storms, linked to the weather stations. The datasets are generated by converting sensor data from MesoWest into the Observations and Measurements (O&M) format, then using an API to convert O&M to RDF and load it into a Virtuoso triplestore. The datasets are made available via SPARQL endpoints and a Pubby interface to allow querying and browsing of the sensor descriptions and observations.
The document provides an overview of how the LOCAH project is applying Linked Data concepts to expose archival and bibliographic data from the Archives Hub and Copac as Linked Open Data. It describes the process of (1) modeling the data as RDF triples, (2) transforming existing XML data to RDF, (3) enhancing the data by linking to external vocabularies and datasets, (4) loading the RDF into a triplestore, and (5) creating Linked Data views to expose the data on the web. The goal is to publish structured data that can be interconnected across domains to enable new uses by both humans and machines.
Information Extraction and Linked Data CloudDhaval Thakker
The document discusses Press Association's semantic technology project which aims to generate a knowledge base using information extraction and the Linked Data Cloud. It outlines Press Association's operations and workflow, and how semantic technologies can be used to develop taxonomies, annotate images, and extract entities from captions into an ontology-based knowledge base. The knowledge base can then be populated and interlinked with external datasets from the Linked Data Cloud like DBpedia to provide a comprehensive, semantically-structured source of information.
Cloud computing has led to a massive proliferation of data centers worldwide that consume vast amounts of electricity. Data centers only utilize 6-12% of energy for actual computation, with most servers running at low utilization rates. This high level of inefficiency means that the energy wasted by data centers keeping idle machines running and for cooling is as much as 30 times the amount needed for their core functions. Due to poor management practices like running servers at max capacity 24/7, most data centers waste 90% of the energy they draw from the power grid.
This document discusses how icons represent ideas and how visual models can convey information. It notes that visual processing makes up 33% of our brain and that seeing developed before speaking. Pictures can represent words and ideas, helping to communicate complex concepts in a clear, visual way. However, images can also potentially deceive or overload us with too much information. The goal is to use visualizations and icons to help people understand content through familiar things.
This tutorial explains the Data Web vision, some preliminary standards and technologies as well as some tools and technological building blocks developed by AKSW research group from Universität Leipzig.
This document discusses the Semantic Web and Linked Open Data. It explains how the Semantic Web helps integrate data by using shared vocabularies and URIs to normalize meanings between data sources. As more datasets adopt Semantic Web principles by exposing structured data through URIs and RDF formats, individual datasets become less isolated and are interconnected to form a large knowledge base. The document provides examples of querying and exploring Linked Open Data through SPARQL and the LOD Cloud. It also offers recommendations for publishing and working with Linked Open Data.
The document discusses the semantic web and how it can potentially disrupt or benefit online commerce. It provides definitions and explanations of key concepts related to the semantic web including RDF, ontologies, linked data, and semantic search. It outlines how search engines and websites are increasingly adopting and leveraging semantic web technologies like RDFa to provide richer search results and experiences for users.
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
This document introduces linked data and discusses how publishing data as linked RDF triples on the web allows for a global linked database. It explains that linked data uses HTTP URIs to identify things and links data from different sources to be queried using SPARQL. Publishing linked data provides benefits like being able to integrate and discover related data on the web. Tools are available to convert existing data or publish new data as linked open data.
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
Talk delivered at YOW! Developer Conferences in Melbourne, Brisbane and Sydney Australia on 1-9 December 2016.
Abstract: Governments collect a lot of data. Data on air quality, toxic chemicals, laws and regulations, public health, and the census are intended to be widely distributed. Some data is not for public consumption. This talk focuses on open government data — the information that is meant to be made available for benefit of policy makers, researchers, scientists, industry, community organisers, journalists and members of civil society.
We’ll cover the evolution of Linked Data, which is now being used by Google, Apple, IBM Watson, federal governments worldwide, non-profits including CSIRO and OpenPHACTS, and thousands of others worldwide.
Next we’ll delve into the evolution of the U.S. Environmental Protection Agency’s Open Data service that we implemented using Linked Data and an Open Source Data Platform. Highlights include how we connected to hundreds of billions of open data facts in the world’s largest, open chemical molecules database PubChem and DBpedia.
WHO SHOULD ATTEND
Data scientists, software engineers, data analysts, DBAs, technical leaders and anyone interested in utilising linked data and open government data.
The document discusses the history and components of the internet. It defines the internet as a global network of interconnected computer networks that use standard protocols to serve billions of users worldwide. It describes the world wide web as a global set of documents and resources linked by hyperlinks. Key components that enable access to the internet are internet service providers, web browsers, hypertext transfer protocol, and uniform resource locators. Search engines help users find information on the world wide web through algorithms that crawl websites in real-time.
Learning Resource Metadata Initiative: Vocabulary Development Best PracticesMike Linksvayer
This document discusses best practices for developing learning resource metadata vocabularies based on guidelines from the Dublin Core Metadata Initiative. It recommends defining clear use cases, selecting an appropriate domain model, reviewing existing vocabularies to reuse terms, designing detailed metadata records, providing usage guidelines, and engaging relevant communities to ensure long-term stewardship of the vocabulary. The Learning Resource Metadata Initiative (LRMI) could benefit from following these best practices in its development.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
reegle - a new key portal for open energy datareeep
A new key portal for Open Energy Data
A new portal called reegle provides open energy data. It offers clean energy search, country energy profiles, an actors catalog, and energy glossary. Datasets labeled as open can be accessed freely in machine-readable formats at data.reegle.info using standards like RDF. Reegle aims to accelerate clean energy by enabling more transparent, efficient use and reuse of information through open data and linked open data approaches.
The document provides an overview of semantic technologies for representing semantic data. It discusses why semantics are needed, describes common metadata models like XML, RDF, and RDFa. It explains how RDF uses triples to represent knowledge as graphs and can be serialized in formats like XML, Notation3, and Turtle. It also discusses how RDFa allows embedding RDF within web pages to add semantics.
This document discusses various use cases for linked data and semantic web technology, including linked data for cross-domain knowledge bases like DBpedia and Freebase, linked geographic data like GeoNames and LinkedGeoData, linked government data from data portals like Data.gov and data.gov.uk, linked media data from projects like MusicBrainz, BBC, and LinkedMDB, linked data for user generated content from projects like flickr wrappr and Revyu.com, and linked life science data. It provides an overview of the concepts of linked data, RDF, URIs and describes several popular linked open datasets.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
From the Semantic Web to the Web of Data: ten years of linking upDavide Palmisano
This document discusses the concepts and technologies behind the Semantic Web. It describes how RDF, RDF Schema, and OWL allow structured data and relationships to be represented and shared across the web. It also discusses tools for working with semantic data in Java, such as Jena, Sesame, and Any23 for extracting and working with RDF. The document provides examples of representing data and relationships in RDF and querying semantic data with SPARQL.
This document describes the generation of two Linked Data datasets from sensor data - LinkedSensorData and LinkedObservationData. LinkedSensorData contains descriptions of about 20,000 weather stations in the US with links to sensor observations. LinkedObservationData contains over a billion triples of sensor observations during major storms, linked to the weather stations. The datasets are generated by converting sensor data from MesoWest into the Observations and Measurements (O&M) format, then using an API to convert O&M to RDF and load it into a Virtuoso triplestore. The datasets are made available via SPARQL endpoints and a Pubby interface to allow querying and browsing of the sensor descriptions and observations.
The document provides an overview of how the LOCAH project is applying Linked Data concepts to expose archival and bibliographic data from the Archives Hub and Copac as Linked Open Data. It describes the process of (1) modeling the data as RDF triples, (2) transforming existing XML data to RDF, (3) enhancing the data by linking to external vocabularies and datasets, (4) loading the RDF into a triplestore, and (5) creating Linked Data views to expose the data on the web. The goal is to publish structured data that can be interconnected across domains to enable new uses by both humans and machines.
Information Extraction and Linked Data CloudDhaval Thakker
The document discusses Press Association's semantic technology project which aims to generate a knowledge base using information extraction and the Linked Data Cloud. It outlines Press Association's operations and workflow, and how semantic technologies can be used to develop taxonomies, annotate images, and extract entities from captions into an ontology-based knowledge base. The knowledge base can then be populated and interlinked with external datasets from the Linked Data Cloud like DBpedia to provide a comprehensive, semantically-structured source of information.
Cloud computing has led to a massive proliferation of data centers worldwide that consume vast amounts of electricity. Data centers only utilize 6-12% of energy for actual computation, with most servers running at low utilization rates. This high level of inefficiency means that the energy wasted by data centers keeping idle machines running and for cooling is as much as 30 times the amount needed for their core functions. Due to poor management practices like running servers at max capacity 24/7, most data centers waste 90% of the energy they draw from the power grid.
This document discusses how icons represent ideas and how visual models can convey information. It notes that visual processing makes up 33% of our brain and that seeing developed before speaking. Pictures can represent words and ideas, helping to communicate complex concepts in a clear, visual way. However, images can also potentially deceive or overload us with too much information. The goal is to use visualizations and icons to help people understand content through familiar things.
Health Datapalooza 2013: Health Data Consortium Affiliates - Sunnie Southern,...Health Data Consortium
The document discusses the Health Data Consortium Affiliate Panel which focuses on igniting the use of health data in local communities. The panelists represent affiliates from Colorado, Louisiana, New York, and Ohio that are working to promote open health data use. The affiliates aim to inspire innovation, catalyze local programs, share best practices, and coordinate efforts to transform health and healthcare through greater health data utilization. The Health Data Consortium seeks to establish an ecosystem and accelerate benefits by years through the affiliates program and advocacy.
The document discusses perceptrons and gradient descent algorithms for training perceptrons on classification tasks. It contains 4 exercises:
1) Explains the role of the learning rate in perceptron training and which Boolean functions can/cannot be modeled with perceptrons.
2) Applies a perceptron to a sample dataset, calculates outputs, and determines the accuracy.
3) Performs one iteration of gradient descent on the same dataset, computing weight updates with a learning rate of 0.2.
4) Performs one iteration of stochastic gradient descent on the dataset, recomputing outputs and updating weights after each instance.
El documento habla sobre los esfuerzos de Euskadi para implementar una política de datos abiertos (Open Data Euskadi) con el objetivo de aumentar la transparencia y fomentar la creación colaborativa. Se liberan datos públicos en formatos reutilizables bajo licencias abiertas para que la iniciativa ciudadana y privada creen servicios a partir de ellos. Se describe el enfoque de Euskadi, que incluye liderazgo político, un proyecto ágil y participativo centrado en liberar gran cantidad de conjuntos de datos en formatos abiert
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.
Linked Data provides a standardized framework for publishing structured data on the web by linking data instead of documents. It uses URIs, HTTP, and RDF to link related data across different sources to create a global data space without silos. EnAKTing is a research project focused on building ontologies from large-scale user participation, querying linked data at web-scale, and visualizing the massive amounts of interconnected data. Some of its applications include services for discovering backlinks, geographical resources, and dataset equivalences in the Web of Data.
Linked Data Driven Data Virtualization for Web-scale Integrationrumito
- Linked data and data virtualization can help address challenges of growing data heterogeneity, complexity, and need for agility by providing a common data model and identifiers.
- Linked data uses RDF to represent information as graphs of triples connected by URIs, allowing different data sources to be integrated and queried together.
- As more data is published using common vocabularies and linking to existing URIs, it increases opportunities for discovery, integration and novel ways to extract value from diverse data sources.
Linked Data allows information to be linked across the web using RDF standards and URIs. It utilizes triples consisting of a subject, predicate, and object to uniformly describe relationships between nodes and metadata. There are over 1,000 Linked Open Data sources that can be queried using SPARQL to retrieve and link external information to locally managed data. This enhances search, knowledge retrieval, and allows leveraging of external expertise without needing to develop it in-house. Linked Data is helping to realize Tim Berners-Lee's original vision of the Semantic Web by making more information on the web machine-readable and interconnected.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It introduces Uniform Resource Identifiers (URIs), Resource Description Framework (RDF), ontologies, SPARQL query language, and library projects applying these technologies like BIBFRAME, the Digital Public Library of America, and Europeana. The goal is to connect structured data on the web through shared vocabularies and relationships between resources from different sources.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/introduction-to-data-integration.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=TEgHq2J1OMo
The lecture covers:
- Web of Data
- Classical Web
- Web APIs and Mashups
- Beyond Web APIs and Mashups: The Data Web and Linked Data
- How to create linked-data?
- Properties of the Web of Linked Data
-
The document introduces the concept of Linked Data and discusses how it can be used to publish structured data on the web by connecting data from different sources. It explains the principles of Linked Data, including using HTTP URIs to identify things, providing useful information when URIs are dereferenced, and including links to other URIs to enable discovery of related data. Examples of existing Linked Data datasets and applications that consume Linked Data are also presented.
The document discusses a webinar presented by NISO and DCMI on Schema.org and Linked Data. The webinar provides an overview of Schema.org and Linked Data, examines the advantages and challenges of using RDF and Linked Data, looks at Schema.org in more detail, and discusses how Schema.org and Linked Data can be combined. The goals of the webinar are to illustrate the different design choices for identifying entities and describing structured data, integrating vocabularies, and incentives for publishing accurate data, as well as to help guide adoption of Schema.org and Linked Data approaches.
This document discusses best practices for publishing linked data on the semantic web. It covers key topics such as using URIs to identify things, following the four principles of linked data, establishing links within and across datasets, choosing appropriate vocabularies, adding metadata about datasets and licenses, testing and debugging the data, and achieving higher levels of the five star model of linked open data. The overall goal is to connect and integrate structured data on the web in a way that is discoverable and reusable.
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.
Linked Data Integration and semantic webDiego Pessoa
This document discusses linked data and the semantic web. It explains that as data volumes on the web grow, linking related data from different sources becomes important. Linked data uses URIs and RDF to connect related data and establish links between resources on the web. The principles of linked data include using URIs to identify things, providing HTTP URIs so people can look up those names, and including links to other related resources. Guidelines are provided for publishing linked data, such as using dereferenceable URIs and creating RDF links. Both browsers and domain-specific applications can be used to consume linked data. Research challenges for linked data include user interfaces, application architectures, and maintaining links between data.
Linked Open Data Libraries Archives Museums. This presentation is a basic overview of what LOD is and what technologies are needed to ensure the metadata around your collections is machine readable.
Linked data presentation for libraries (COMO)robin fay
The document provides an overview of linked data and libraries. It discusses basic principles of linked data such as reusing and linking data to make it reusable, easy to correct, and potentially useful to others. The document also discusses how linked data fits into the semantic web vision by allowing machines to better understand and utilize data. Finally, it discusses getting started with linked data through terminology, advantages, and modeling library data in linked data formats like RDF.
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 document provides a summary of a talk given by Tope Omitola on using linked data for world sense-making. The talk discussed EnAKTing, a project focused on building ontologies from large-scale user participation and querying linked data. It also covered publishing and consuming public sector datasets as linked data, including challenges around data integration, normalization and alignment. The talk concluded with a discussion of linked data services and applications developed by the project to enhance findability, search, and visualization of linked data.
The Web of Linked Open Data, or LOD, is the most relevant achievement of the Semantic Web. Initially proposed by Tim Berners-Lee in a seminal paper published in Scientific American in 2001, the Semantic Web envisions a web where software agents can interact with large volumes of structured, easy to process data. It is now when users have at our disposal the first, mature results of this vision. Among them, and probably the most significant ones, are the different LOD initiatives and projects that publish open data in standard formats like RDF.
This presentation provides an overview and comparison of different LOD initiatives in the area of patent information, and analyses potential opportunities for building new information services based on largely available datasets of patent information. Information is based on different interviews conducted with innovation agents and on the analysis of professional bibliography and current implementations.
LOD opportunities are not only restricted to information aggregators, but also to end-users and innovation agents that need to face with the difficulties of dealing with large amounts of data. In both cases, the opportunities offered by LOD need to be assessed, as LOD has just become a standard, universal method to distribute, share and access data.
The document discusses the concepts of the semantic web and linked data. It explains that the semantic web aims to convert the web into a single database that can be understood by machines through linking data using URIs, RDF, and other standards. It provides examples of projects like DBpedia and the Linking Open Data cloud that publish open government and other data as linked data. The document outlines some of the technologies and best practices for publishing and connecting data as linked data.
The document discusses experiments performed using the Weka machine learning tool to evaluate the performance of a multi-layer perceptron classifier on a soybean dataset. The experiments varied hyperparameters like the number of epochs, learning rate, and number of hidden layers. Increasing the epochs improved accuracy up to 100 epochs. Increasing the learning rate from 0.1 to 0.3 also improved accuracy, but higher rates did not. Increasing the hidden layers from 1 to 20 significantly improved accuracy, but more layers did not help as much. Using multiple hidden layers together worked best, achieving over 94% accuracy with 10 hidden layers.
The document discusses Bayes' rule and entropy in data mining. It provides step-by-step derivations of Bayes' rule from definitions of conditional probability and the chain rule. It then gives examples of calculating entropy for variables with different probability distributions, noting that maximum entropy occurs with a uniform distribution where all outcomes are equally likely, while minimum entropy occurs when the probability of one outcome is 1.
The document discusses various concepts in data mining and decision trees including:
1) Pruning trees to address overfitting and improve generalization,
2) Separating data into training, development and test sets to evaluate model performance,
3) Information gain favoring attributes with many values by having less entropy,
4) Strategies for dealing with missing attribute values such as predicting values or focusing on other attributes/classes,
5) Changing stopping conditions for regression trees to use standard deviation thresholds rather than discrete classes.
The document discusses three exercises related to data mining and machine learning algorithms:
1. Drawing a decision tree corresponding to a logical formula in disjunctive normal form about loan eligibility.
2. Calculating the information gain of attributes to determine the best attribute to use for the first branch in a decision tree.
3. Providing an example of when a decision tree would overfit the training data by perfectly modeling unique observations but being unable to generalize to new data.
Lazy learning differs from other machine learning approaches in that it stores all training data and uses it directly to make predictions on new data, rather than developing a predictive model or function from the training data. k-nearest neighbor classification can overfit if too small a value for k is used, but this can be addressed by increasing k to consider more neighboring points. Given sample data, instances 7 and 8 are classified as positive using k=1,3 and the prototype classifier, which determines class prototypes as the average values for each class.
This document discusses applying data mining techniques to predict whether a football match will be cancelled due to weather. Attributes that could be used in the prediction include amount of rain, temperature, humidity, and weather conditions. The data would come from weather stations and the football club. A second example discusses using attributes like player numbers, injuries, past goals, and team performance to predict the outcome of a match between Ajax and Real Madrid. The document also explains the difference between a training set, which is used to weight attributes, and a test set, which is used to evaluate the training set's predictions. Key data mining concepts like features, instances, and classes are briefly defined with examples.
The J48 decision tree classifier was used to classify instances in the zoo.test.arff dataset into animal types. It correctly classified 17 of 20 instances (85%). The decision tree examines attributes like feathers, milk, legs, fins to determine if an animal is a mammal, bird, reptile, fish, etc. New instances are classified by traversing the tree and seeing which leaf node they reach based on their attribute values.
Semantic web final assignment, We've used Sqvizler to build our own semantic web application. The application prototype was used to show the possibilites of finding all popular spots in the region of a university. The data which is used for this application comes from several datasources; respectively dbpedia.org, linkedgeodata.org and a local database with university information.
This document is a student's assignment submission for a semantic web course. It includes the student's name and details, followed by their responses to several questions about widely used ontologies on the Web of Data, formats for embedding structured data into HTML, the implication of using owl:sameAs, approaches for connecting semantic web resources, whether a resource can have multiple representations, and developing an RDFa web page with external ontologies.
This is a regular query asking for the abstract of the Semantic Web resource from DBPedia. The query returns the English abstract which describes the Semantic Web as a collaborative movement led by the W3C that promotes common data formats on the World Wide Web in order to convert it from unstructured documents to a web of shared and reusable data across applications through the use of semantic annotations.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Film vocab for eal 3 students: Australia the movie
Semantic web assignment1
1. Semantic
Web
–
Assignment
1
Assigment
name:
WebKR
Assignment
1
Full
name:
Barry
Kollee
Student
number:
10349863
Student
username:
UvA
student
(barry.kollee@student.uva.nl)
Web
of
Data
1.
What
does
the
word
Semantic
web
means?
Semantic
web
can
be
described
as
how
computers
are
linked
to
each
other
in
a
conceptual
way.
They
manage
to
talk
to
one
and
another
by
using
a
common
language
which
results
in
an
appropriate
way
of
sending
and
retrieving
data.
All
the
data
from
the
web
(text,
images,
video,
sound
etc.)
is
organized
by
using
keywords
and
paths
(URI’s).
The
ideal
goal
for
a
‘Semantic
web’
is
to
be
able
to
share
information
easily
with
different
computers
so
that
the
paths
and
indexes
would
become
‘Machine
readable’.
By
using
this
methodology
we
should
be
able
to
link
all
data,
which
is
available
on
the
web,
to
one
and
another
which
enables
data
sharing
to
all
kinds
of
services.
So
the
goal
of
Semantic
web
is
to
“make
the
web
more
accessible
to
computers”.
2.
Why
is
automatic
reuse
and
data
interoperability
on
the
web
difficult?
The
web
it
not
just
a
Semantic
web.
Applications
on
the
web
need
information
to
work
with.
Because
our
information
systems
are
keeping
their
data
to
themselves
we’re
unable
to
link
them.
Applications
use
different
formats,
structures,
vocabularies
and
have
a
different
way
of
giving
meaning
to
certain
values.
We
already
try
to
let
the
web
share
their
information
easier.
We
do
that
by
using
different
API’s
and/or
give
structure
to
our
work
by
using
common
languages
which
are
defined
as
standards.
But
still
there
remains
a
translation
or
index-‐bridge
throughout
these
information
systems.
3.
Why
is
DBpedia
a
hub
in
the
Web
of
Data?
DBpedia
gives
us
the
opportunity
to
create
new
links
to
all
this
information
on
the
web.
DBpedia
is
able
to
link
data,
which
gives
us
a
way
to
communicate
and
share
data
with
other
datasets
and
ontologies.
With
this
in
mind
we
could
make
a
reference
from
a
‘squirrel’
to
a
‘swimming
pool’.
1
4.
What
are
the
four
rules
of
linked
data
?
There
aren’t
actual
rules
for
linking
data
but
it’s
more
that
they
can
be
described
as
behaviors.
However
we
can
state
that
not
keeping
us
to
these
‘rules’
would
disable
us
to
make
data
interconnected.
1. Use
URL’s
as
names
for
things.
All
data
on
the
web
is
being
placed
on
a
unique
addressee.
The
naming
conventions
of
these
data
files/paths
is
really
important
so
that
you
can
easily
refer
to
it.
2. Use
HTTP
URL’s
so
that
people
can
look
up
those
names.
The
main
goal
for
this
rule
is
that
we
apply
standards
to
our
URL’s
(addresses
of
data)
so
that
they
are
accessible
more
easily.
1
Berners-‐Lee.,
(2006),
http://www.w3.org/DesignIssues/LinkedData.html
2. 3. When
someone
looks
up
a
URL,
provide
useful
information,
using
the
standards
4. Include
links
to
other
URL’s,
so
that
they
can
discover
more
things.
This
rule
is
all
about
linking
data
to
the
web.
5.
Pick
and
investigate
four
other
datasets
from
http://linkeddata.org.
Briefly,
describe
what
kind
of
data
the
dataset
describes.
LinkedMDB
This
dataset
it’s
goal
is
to
build
a
Semantic
web
for
video’s.
It
includes
a
large
number
of
interlinks
to
several
datasets
on
the
open
data
could
and
references
to
related
web
pages.
GovTrack
GovTrack
is
a
helper
for
public
research
about
the
United
States
Congress
and
the
state
legislatures.
Their
goal
is
to
give
government
transparency
and
to
innovate
their
government
with
this
transparency.
Berkeley
BOP
(BBOB)
Our
group
is
focused
on
the
development,
use,
and
integration
of
ontologies
into
biological
data
analysis.
We
invite
you
to
learn
more
about
our
projects
and
people.
Jamendo
Jamendo
is
a
dataset
of
Creative
Commons
licensed
music,
based
in
France.
It
publishes
a
set
of
URL’s
with
an
RDF
representation
holding
links
to
external
datasets.
6.
For
each
of
the
four
datasets
you
selected,
list
a
scheme
or
ontology
used
by
that
dataset.
Are
there
ontologies
that
are
commonly
used?
LinkedMDB
• Actor
• Performance
• Writer
GovTrack
(searching
for
politicians)
• State
• Addresse
• Zip
code
Berkeley
BOB
(BBOB)
• malaria_ontology
• plant_environment:
Jamendo
• nameOfArtist
• nameOfSong
3. There
could
probably
be
lots
of
commonly
used
ontologies.
However
these
datasets
are
not
that
alike
and/or
the
same
naming
convention
could
mean
something
else
(Homonyms).
We
could
state
that
(for
example)
‘nameOfArtist’
could
also
be
available
inside
the
LinkedMDB
and
Jamendo
database.
However
the
meaning
of
Artist
could
differ
between
the
movie
dataset
(LinkedMDB)
and
the
music
dataset
(Jamendo).
However
in
some
cases
they
could
refer
to
the
same
class.
For
example
if
you
would
search
for
‘nameOfArtist’
in
both
Jamendo
and
LinkedMDB
we
could
get
an
actor
who
is
also
a
musician
(i.e.
Will
Smith).
7.
What
is
the
relation
between
RDF,
RDFS
and
OWL?
RDF
RDF
is
a
standard
model
for
data
sharing
throughout
the
web
and
describes
a
data
model.
‘RDF
extends
the
linking
structure
of
the
Web
to
use
URIs
to
name
the
relationship
between
things
as
well
as
the
two
ends
of
the
link’
Using
this
simple
model,
it
allows
structured
and
semi-‐structured
data
to
be
mixed,
exposed,
and
shared
across
different
2
applications.’
RDFS
RDFS
are
vocabularies
for
describing
ontologies
in
RDF.
A
developer
can
use
RDFS
to
give
meaning
to
vocabularies.
By
using
RDFS
we
can
in
stead
refer
to
just
to
individual
object
to
a
certain
class.
OWL
Owl
is
an
ontology
language
where
you
can
describe
how
data
is
linked
together
and
you
can
set
certain
constraints
and
restrictions
on
this
data.
I.e.
that
a
parent
could
only
have
one
child.
This
enables
us
to
give
more
specified
information
about
a
certain
object.
The
relation
between
these
above
three
is
that
they
describe
a
data
model.
They
are
distinguished
by
each
other
because
one
model
is
more
specific
then
the
other
or
in
a
is
describing
data
in
a
different
way.
34
8.
What
is
RDFa
(Resource
Sescription
Framework
in
attributes)
?
RDFa
is
a
specification
for
attributes
to
be
used
with
languages
such
as
HTML
and
XHTML
to
express
structured
data
and
it’s
a
tool
for
HTML
authors
to
link
data
together
in
a
structural
manner.
These
authors
are
able
to
add
a
set
of
attribute-‐level
extensions
to
HTML,
XHTML
and
XML.
An
example
of
a
goal
of
this
usage
is
when
you
order
a
concert
ticket
and
you’ll
have
it
scheduled
in
your
agenda
right
away.
If
you
would
zoom
in
to
all
our
data
and
would
give
taqs
and
hints
for
our
computer
programs
then
this
would
become
very
helpful
because
they
start
to
understand
the
data
it’s
structure.
9.
What
is
the
relationship
between
the
Facebook
Open
Graph
Protocol
5
and
RDFa?
They
both
are
defining
the
action
or
path
that
the
data
should
be
linked
to.
So
links
are
being
created
to
the
properties
of
a
certain
user.
Also
mobile
applications
can
create
new
links
to
the
exisiting
facebook
web
by
creating
links
to
the
facebook
Open
Graph.
We
also
create
links
with
RDFa
to
certain
objects
by
giving
taqs
and
hints
in
the
2
http://www.w3.org/RDF/
3
http://www.w3.org/TR/xhtml-‐rdfa-‐primer/
4
http://www.w3.org/TR/2008/CR-‐rdfa-‐syntax-‐20080620/
5
http://developers.facebook.com/docs/opengraph/
4. created
HTML.
10.
Can
you
consider
a
data
dictionary
an
ontology?
No,
because
a
dictionary
has
got
objects
and
the
meaning
of
these
objects,
but
their
not
linked
to
each
other.
Every
objects
defines
itself
and
is
not
referring
or
saying
something
about
other
data
parts.
However
a
CMS
(or
program
alike)
should
be
able
to
give
meaning
(properties)
to
all
our
objects
and
could
possible
be
able
to
link
objects
to
one
and
another.
RDF(S)
6
1.
Name
four
different
syntaxes
for
RDF.
• Turtle
• RDFa
• RDF-‐XML
• Notation
3
(n3)
2.
What
is
the
difference
between
the
data
models
of
RDF
and
XML?
Within
XML
there
is
no
definition
of
the
data
that
is
listed.
And
within
RDF
there
is.
That’s
because
RDF
is
a
data
model
and
not
a
data
format.
3.
What
is
the
relation
between
RDF
and
RDFS?
7
‘RDF
is
a
universal
language
that
lets
users
describe
resources
in
their
own
vocabularies’.
So
they
both
describe
a
resources.
4.
What
information
of
a
class
can
RDFS
describe?
And
what
information
8
of
a
property?
Class
• Rdfs:Resource,
the
class
of
all
resources
• Rdfs:Class,
the
class
of
all
classes
• Rdfs:Literal,
the
call
of
all
literals
(strings)
• Rdf:Property,
the
class
of
all
properties
• Rdf:Statement,
the
class
of
all
reified
statements
Properties
• Rdf:type,
relates
a
resource
to
it’s
class
• Rdfs:subClassOf,
which
relates
a
class
to
one
of
its
superclasses
• Rdfs:subPropertyOf,
relates
a
property
to
one
of
its
superproperties
• Rdfs:domain,
which
specifies
the
domain
of
a
property
• Rdfs:range
6
http://www.w3.org/TeamSubmission/n3/
7
http://ids.snu.ac.kr/w/images/8/85/WEC_2009_RDF_RDFS.pdf
8
http://ids.snu.ac.kr/w/images/8/85/WEC_2009_RDF_RDFS.pdf
5. 5.
Give
two
example
inferences
that
you
can
draw
in
RDFS,
using
IF-‐
THEN
rules
(for
each
rule,
give
the
antecedents
and
conclusion).
1. IF
PvdA
owl:sameAs
VVD
Barry
voted
VVD
THEN
Barry
voted
PvdA
2. IF
Human
owl:sameAs
Person
Barry
isA
Person
THEN
Barry
isA
Human
Ontology
This
assignment
has
been
made
together
with
Eric
de
Rijcke
(Vu
studentID:
2523479).
The
domain
we
have
chosen
for
is
‘common
food’.
RDFS
scheme
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns# > .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema# > .
@prefix ex: <http://www.example.org/> .
@prefix food: <http://www.example.org/food/> .
ex:Vegetables rdfs:subClassOf ex:Holland .
ex:Candy rdfs:subClassOf ex:Holland .
ex:Kale rdf:type ex:Vegetables .
ex:Endive rdf:type ex:Vegetables .
ex:Stroopwaffle rdf:type ex:Candy .
ex:Drop rdf:type ex:Candy .
food:typical rdfs:range ex:Holland .
ex:FoodOfCountry food:typical ex:Japan .
Validation
Confirmation