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.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
LinkML Intro July 2022.pptx PLEASE VIEW THIS ON ZENODOChris Mungall
NOTE THAT I HAVE MOVED AWAY FROM SLIDESHARE TO ZENODO
The identical presentation is now here:
https://doi.org/10.5281/zenodo.7778641
General introduction to LinkML, The Linked Data Modeling Language.
Adapter from presentation given to NIH May 2022
https://linkml.io/linkml
Part of a joint presentation with Midori Harris comparing OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) as ontology languages, This presentation concentrates on OWL, Midori Harris presented OBO.
Towards an Open Research Knowledge GraphSören Auer
The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
LinkML Intro July 2022.pptx PLEASE VIEW THIS ON ZENODOChris Mungall
NOTE THAT I HAVE MOVED AWAY FROM SLIDESHARE TO ZENODO
The identical presentation is now here:
https://doi.org/10.5281/zenodo.7778641
General introduction to LinkML, The Linked Data Modeling Language.
Adapter from presentation given to NIH May 2022
https://linkml.io/linkml
Part of a joint presentation with Midori Harris comparing OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) as ontology languages, This presentation concentrates on OWL, Midori Harris presented OBO.
Towards an Open Research Knowledge GraphSören Auer
The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables scenarios of semantic data access and data integration.
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
For efficient and innovative use of big data, it is important to integrate multiple data bases across domains. For example, various public data bases are developed in life science, and how to find a novel scientific result using them is an essential technique. In social and business areas, open data strategies in many countries promote diversity of public data, how to combine big data and open data is a big challenge. That is, diversity of dataset is a problem to be solved for big data.
Ontology gives a systematized knowledge to integrate multiple datasets across domains with semantics of them. Linked Data also provides techniques to interlink datasets based on semantic web technologies. We consider that combinations of ontology and Linked Data based on ontological engineering can contribute to solution of diversity problem in big data.
In this talk, I discuss how ontological engineering could be applied to big data with some trial examples.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
A tutorial on how to create mappings using ontop, how inference (OWL 2 QL and RDFS) plays a role answering SPARQL queries in ontop, and how ontop's support for on-the-fly SQL query translation enables scenarios of semantic data access and data integration.
The slideset used to conduct an introduction/tutorial
on DBpedia use cases, concepts and implementation
aspects held during the DBpedia community meeting
in Dublin on the 9th of February 2015.
(slide creators: M. Ackermann, M. Freudenberg
additional presenter: Ali Ismayilov)
This presentation looks in detail at SPARQL (SPARQL Protocol and RDF Query Language) and introduces approaches for querying and updating semantic data. It covers the SPARQL algebra, the SPARQL protocol, and provides examples for reasoning over Linked Data. We use examples from the music domain, which can be directly tried out and ran over the MusicBrainz dataset. This includes gaining some familiarity with the RDFS and OWL languages, which allow developers to formulate generic and conceptual knowledge that can be exploited by automatic reasoning services in order to enhance the power of querying.
This presentation covers the whole spectrum of Linked Data production and exposure. After a grounding in the Linked Data principles and best practices, with special emphasis on the VoID vocabulary, we cover R2RML, operating on relational databases, Open Refine, operating on spreadsheets, and GATECloud, operating on natural language. Finally we describe the means to increase interlinkage between datasets, especially the use of tools like Silk.
This presentation focuses on providing means for exploring Linked Data. In particular, it gives an overview of current visualization tools and techniques, looking at semantic browsers and applications for presenting the data to the end used. We also describe existing search options, including faceted search, concept-based search and hybrid search, based on a mix of using semantic information and text processing. Finally, we conclude with approaches for Linked Data analysis, describing how available data can be synthesized and processed in order to draw conclusions.
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
This presentation gives details on technologies and approaches towards exploiting Linked Data by building LD applications. In particular, it gives an overview of popular existing applications and introduces the main technologies that support implementation and development. Furthermore, it illustrates how data exposed through common Web APIs can be integrated with Linked Data in order to create mashups.
Linked Data knowledge sources such as DBpedia, Freebase, and Wikidata currently offer large amounts of factual data. As the amount of information that can be grasped by users is limited, data summaries are needed. If a summary relates to a specific entity we refer to it as entity summarization. Unfortunately, in many settings, the summaries of entities are tightly bound to user interfaces. This practice poses problems for efficient and objective comparison and evaluation.
In this paper we focus on the question of how to make summaries exchangeable between multiple interfaces and multiple summarization services in order to facilitate evaluation and testing. We introduce SUMMA, an API definition that enables to decouple generation and presentation of summaries. It enables multiple consumers to retrieve summaries from multiple providers in a unified and lightweight way.
Introduction to Apache Any23. Any23 is a library, a Web Service and a Command Line Tool written in Java, that extracts structured RDF data from a variety of Web documents and markup formats.
Any23 is an Apache Software Foundation top level project.
Overview of how data on the Web of Data can be consumed (first and foremost Linked Data) and implications for the development of usage mining approaches.
References:
Elbedweihy, K., Mazumdar, S., Cano, A. E., Wrigley, S. N., & Ciravegna, F. (2011). Identifying Information Needs by Modelling Collective Query Patterns. COLD, 782.
Elbedweihy, K., Wrigley, S. N., & Ciravegna, F. (2012). Improving Semantic Search Using Query Log Analysis. Interacting with Linked Data (ILD 2012), 61.
Raghuveer, A. (2012). Characterizing machine agent behavior through SPARQL query mining. In Proceedings of the International Workshop on Usage Analysis and the Web of Data, Lyon, France.
Arias, M., Fernández, J. D., Martínez-Prieto, M. A., & de la Fuente, P. (2011). An empirical study of real-world SPARQL queries. arXiv preprint arXiv:1103.5043.
Hartig, O., Bizer, C., & Freytag, J. C. (2009). Executing SPARQL queries over the web of linked data (pp. 293-309). Springer Berlin Heidelberg.
Verborgh, R., Hartig, O., De Meester, B., Haesendonck, G., De Vocht, L., Vander Sande, M., ... & Van de Walle, R. (2014). Querying datasets on the web with high availability. In The Semantic Web–ISWC 2014 (pp. 180-196). Springer International Publishing.
Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., & Van de Walle, R. (2014, April). Web-Scale Querying through Linked Data Fragments. In LDOW.
Luczak-Rösch, M., & Bischoff, M. (2011). Statistical analysis of web of data usage. In Joint Workshop on Knowledge Evolution and Ontology Dynamics (EvoDyn2011), CEUR WS.
Luczak-Rösch, M. (2014). Usage-dependent maintenance of structured Web data sets (Doctoral dissertation, Freie Universität Berlin, Germany), http://edocs.fu-berlin.de/diss/receive/FUDISS_thesis_000000096138.
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
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.
RDF Linked Data - Automatic Exchange of BIM ContainersSafe Software
This presentation tells the story, and FME solutions of a Dutch Utility company for the automatic exchange of data containers containing RDF Linked data, BIM, and documents.
The presentation will focus on the non-traditional representation of RDF Linked Data and how this integrates with FME through SPARQL, Apache Jena, and a few customer-built transformers in FME.
This FME solution also uses my Excel switch-based method of directing the data flow (my presentation during the FME World Fair).
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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.
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
4. Music!
4
• Provision of a music-based portal.
• Bring together a number of disparate components of
data-oriented content:
1. Musical content (streaming data & downloads)
2. Music and artist metadata
3. Review content
4. Visual content (pictures of artists & albums)
6. Music!
6
Expected Results
• The developer will contribute back the aggregated and
interlinked content to the Linked Open Data Cloud.
• Linking of artists will be improved.
• Metadata, visual content and reviews will be improved.
• Links to emerging Web technologies that inherit from
semantics: Google RichSnippets, Facebook OpenGraph
and schema.org annotation.
8. • Extension of the technology of computer
networks.
• The technology supporting the Internet
includes the Internet Protocol (IP) .
• Each computer on the Internet is assigned an
IP number.
• Messages can be routed from one computer
to another.
Internet
8
10. • There is a wealth of information on the Web.
• It is aimed mostly towards consumption by
humans as end-users:
• Recognize the meaning behind content and draw conclusions,
• Infer new knowledge using context and
• Understand background information.
TheWeb
10
11. TheWeb
11
• Billions of diverse documents online, but it is not easily
possible to automatically:
• Retrieve relevant documents.
• Extract information.
• Combine information in a meaningful way.
• Idea:
• Also publish machine processable data on the web.
• Formulate questions in terms understandable for a machine.
• Do this in a standardized way so machines can interoperate.
• The Web becomes a Web of Data
• This provides a common framework to share knowledge on the Web
across application boundaries.
14. WebTechnology Basics
14
HTML – HyperText Markup Language
• Language for displaying web pages and other
information in a web browser.
• HTML elements consist of tags (enclosed in angle
brackets), attributes and content.
HTTP – HypertextTransfer Protocol
• Foundation of data communication for the WWW.
• Client-server protocol.
• Every interaction is based on: request and response.
15. WebTechnology Basics
15
Uniform Resource Identifier (URI)
• Compact sequence of characters that identifies an
abstract or physical resource.
• Examples:
ldap://[2001:db8::7]/c=GB?objectClass?one
mailto:John.Doe@example.com
news:comp.infosystems.www.servers.unix
tel:+1-816-555-1212
telnet://192.0.2.16:80/
urn:oasis:names:specification:docbook:dtd:xml:4.1.2
http://dbpedia.org/resource/Karlsruhe
16. Describing Data
16
Vocabularies
• Collections of defined relationships and classes of
resources.
• Classes group together similar resources.
• Terms from well-known vocabularies should be
reused wherever possible.
• New terms should be define only if you can not find
required terms in existing vocabularies.
17. Describing Data
17
Vocabularies
A set of well-known vocabularies has evolved in the
Semantic Web community. Some of them are:
Vocabulary Description Classes and Relationships
Friend-of-a-Friend (FOAF) Vocabulary for describing
people.
foaf:Person, foaf:Agent, foaf:name,
foaf:knows, foaf:member
Dublin Core (DC) Defines general metadata
attributes.
dc:FileFormat, dc:MediaType,
dc:creator, dc:description
Semantically-Interlinked
Online Communities (SIOC)
Vocabulary for representing
online communities.
sioc:Community, sioc:Forum,
sioc:Post, sioc:follows, sioc:topic
Music Ontology (MO) Provides terms for describing
artists, albums and tracks.
mo:MusicArtist, mo:MusicGroup,
mo:Signal, mo:member, mo:record
Simple Knowledge
Organization System (SKOS)
Vocabulary for representing
taxonomies and loosely
structured knowledge.
skos:Concept, skos:inScheme,
skos:definition, skos:example
18. Describing Data
18
Vocabularies
More extensive lists of well-known vocabularies are
maintained by:
• W3C SWEO Linking Open Data community project
http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/CommonVocabularies
• Mondeca: Linked Open Vocabularies
http://labs.mondeca.com/dataset/lov
• Library Linked Data Incubator Group: Vocabularies in
the library domain
http://www.w3.org/2005/Incubator/lld/XGR-lld-vocabdataset-20111025
20. Semantics on theWeb
20
Semantic Web Stack
Berners-Lee (2006)
Syntatic basis
Basic data model
Simple vocabulary
(schema) language
Expressive vocabulary
(ontology) language
Query language
Application specific
declarative-knowledge
Digital signatures,
recommendations
Proof generation,
exchange, validation
22. Semantics on theWeb
22
RDF – Resource Description Framework
• RDF is the basis layer of the Semantic Web stack
‘layer cake’.
• Basic building block: RDF triple.
• Subject – a resource, which may be identified with a URI.
• Predicate – a URI-identified reused specification of the
relationship.
• Object – a resource or literal to which the subject is related.
23. Semantics on theWeb
23
RDF – Resource Description Framework
(Example)
<http://musicbrainz.org/artist/b10bbbfc-
cf9e-42e0-be17-e2c3e1d2600d#_>
<http://www.w3.org/2002/07/owl#sameAs>
<http://dbpedia.org/resource/The_Beatles>.
<http://musicbrainz.org/artist/b10bbbfc-
cf9e-42e0-be17-e2c3e1d2600d#_>
<http://xmlns.com/foaf/0.1/name>
"The Beatles" .
URIs are given in
angle brackets in
N-Triples.
Literals are given in quotes in N-Triples.
In N-Triples every
statement is
terminated with a
full stop.
24. Semantics on theWeb
24
RDF Graphs
• Every set of RDF assertions can then be drawn and
manipulated as a (labelled directed) graph:
• Resources – the subjects and objects are nodes of the
graph.
• Predicates – each predicate use becomes a label for an arc,
connecting the subject to the object.
Subject Object
Predicate
26. Semantics on theWeb
26
RDF Blank Nodes
• RDF graphs can also contain unidentified resources, called
blank nodes:
• Blank nodes can group related information, but their use in
Linked Data is discouraged.
[] <http://www.w3.org/2003/01
/geo/wgs84_pos#Point>
<http://www.w3.org/1999/02/
22-rdf-syntax-ns#type>
<http://www.w3.org/2003/01
/geo/wgs84_pos#lat>
<http://www.w3.org/2003/01
/geo/wgs84_pos#long>
49.005 8.386
27. Semantics on theWeb
27
RDFTurtle
• Turtle is a syntax for RDF more readable.
• Since many URIs share same basis we use prefixes:
@prefix rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefix rdfs:<http://www.w3.org/2000/01/rdf-schema#>.
@prefix owl:<http://www.w3.org/2002/07/owl#>.
@prefix mo:<http://purl.org/ontology/mo/>.
@prefix dbpedia:<http://dbpedia.org/resouce/>.
And (sometimes) a unique base:
@base <http://musicbrainz.org/>.
28. Semantics on theWeb
28
RDFTurtle
• Also has a simple shorthand for class membership:
@base <http://musicbrainz.org/>.
@prefix mo:<http://purl.org/ontology/mo/>.
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> a mo:MusicGroup.
Is equivalent to:
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://purl.org/ontology/mo/MusicGroup>.
29. RDFTurtle
• When multiple statements apply to same subject they
can be abbreviated as follows:
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdfs:label "The Beatles";
owl:sameAs dbpedia:The_Beatles ,
<http://www.bbc.co.uk/music/artists/
b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#artist> .
Same subject &
predicate
Semantics on theWeb
29
Same subject
30. RDFTurtle
• Turtle also provides a simple syntax for datatypes and
language tags for literals, respectively:
<recording/5098d0a8-d3c3-424e-9367-1f2610724410#_> a mo:Signal;
rdfs:label "All You Need Is Love" ;
mo:duration "PT3M48S"^^xsd:duration .
dbpedia:The_Beatles dbpedia-owl:abstract
"The Beatles were an English rock band formed (…) "@en,
"The Beatles waren eine britische Rockband in den (…) "@de .
Semantics on theWeb
30
31. Semantics on theWeb
31
RDF/XML
• This is most useful for inter-machine communication.
• The primary (recurring) element in encoding
assertions (thereby triples) is rdf:Description, e.g.:
<rdf:Description
rdf:about="http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_">
<foaf:name>The Beatles</foaf:name>
<owl:sameAs rdf:resource="http://dbpedia.org/resource/The_Beatles">
</rdf:Description>
<rdf:Description
rdf:about="http://musicbrainz.org/artist/4d5447d7-c61c-4120-ba1b-d7f471d385b9#_">
<foaf:name>John Lennon</foaf:name>
</rdf:Description>
33. Semantics on theWeb
33
RDF-S – RDF Schema
Language for two tasks w.r.t. the RDF data model:
• Expectation – nominate:
• the ‘types’, i.e., classes, of things we might make
assertions about, and
• the properties we might apply, as predicates in these
assertions, to capture their relationships.
• Inference – given a set of assertions, using these classes
and properties, specify what should be inferred about
assertions that are implicitly made.
34. Semantics on theWeb
34
RDF-S – RDF Schema
• rdf:Property - Class of RDF properties. Example:
mo:member - Indicates a member of a musical group.
• rdfs:domain - States that any resource that has a given
property is an instance of one or more classes.
mo:member rdfs:domain mo:MusicGroup .
• rdfs:range - States that the values of a property are
instances of one or more classes.
mo:member rdfs:range foaf:Agent .
35. Semantics on theWeb
35
RDF-S – RDF Schema
mo:MusicGroup
rdfs:subClassOf
foaf:Group .
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdf:type
mo:MusicGroup .
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdf:type
foaf:Group .
Schema
Existing
fact
Inferred
fact
We expect to use this
vocabulary to make assertions
about music groups.
Having made such an
assertion...
Inferences can be drawn that
we did not explicitly make
36. Semantics on theWeb
36
RDF-S – RDF Schema
Resources and predicates
with (limited) inferences:
rdfs:Resource
rdfs:Literal, rdfs:Datatype
rdfs:Class, rdfs:subClassOf
rdfs:subPropertyOf
rdfs:range, rdfs:domain
Some predicates with
NO inferences:
rdfs:comment
rdfs:label
rdfs:seeAlso
rdfs:isDefinedBy
rdf:Property (an instance of rdfs:Class)
38. Semantics on theWeb
38
OWL – Web Ontology Language
• RDFS provides a simplified ontological language for
defining vocabularies about specific domains.
• Sometimes it is necessary to have access to a wider
range of ontological constructs.
• Web Ontology Language (OWL) provides more
ontological constructs and avoids some of the
potential confusion in RDF-S.
39. Semantics on theWeb
39
OWL 2.0 – Web Ontology Language 2.0
Extends the DL further, but has three more computable fragments
(profiles). OWL 2 Full
• Used informally to refer to RDF graphs considered
as OWL 2 ontologies and interpreted using the
RDF-Based Semantics.
OWL 2 DL
• Used informally to refer to OWL 2 DL ontologies
interpreted using the Direct Semantics.
OWL 2 EL
• Limited to basic classification, but with
polynomial-time reasoning.
OWL 2 QL
• Designed to be translatable to relational database
querying.
OWL 2 RL
• Designed to be efficiently implementable in rule-
based systems.
OWL 2 Full
OWL 2 DL
OWL 2 EL
OWL 2 QL OWL 2 RL
40. Semantics on theWeb
40
OWL – Web Ontology Language
OWL is made up of terms which provide for:
• Class construction: forming new classes from membership of existing
ones (e.g., unionOf, intersectionOf, etc.).
• Property construction: distinction between OWL ObjectProperties
(resources as values) and OWL DatatypeProperties (literals as values).
• Class axioms: sub-class, equivalence and disjointness relationships.
• Property axioms: sub-property relationship, equivalence and
disjointness, and relationships between properties.
• Individual axioms: statements about individuals (sameIndividual,
differentIndividuals).
42. Semantics on theWeb
42
SPARQL – * Protocol and RDF Query Language
• Query language designed to use a syntax similar to
SQL for retrieving data from relational databases.
• Different query forms:
• SELECT returns variables and their bindings directly.
• CONSTRUCT returns a single RDF graph specified by a
graph template.
• ASK test whether or not a query pattern has a solution.
Returns yes/no.
• DESCRIBE returns a single RDF graph containing RDF
data about resources.
43. Semantics on theWeb
43
SPARQL – * Protocol and RDF Query Language
• The syntax of a SELECT query is as follows:
– SELECT nominates which components of the matches
made against the data should be returned.
– FROM (optional) indicates the sources for the data against
which to find matches.
– WHERE defines patterns to match against the data.
– ORDER BY defines a means to order the selected
matches.
44. Semantics on theWeb
44
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX music-ont: <http://purl.org/ontology/mo/>
SELECT ?album_name ?track_title
WHERE {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
foaf:made ?album .
?album dc:title ?album_name ;
music-ont:track ?track .
?track dc:title ?track_title . }
Retrieve the names of the albums and tracks recorded byThe Beatles.
45. Semantics on theWeb
45
SPARQL – * Protocol and RDF Query Language
SQL SPARQL
Based on relations (tables). Based on labelled directed graphs.
The relations (tables) to be
matched over should be
indicated.
Assumes a default graph.
(The FROM clause populates this
with specific identified
subgraphs).
(Retrieval) queries produce a
relation from a relation.
SPARQL SELECT queries produce a
relation from a graph.
CONSTRUCT queries (considered
later) produce a graph from a
graph.
46. Semantics on theWeb
46
SPARQL – * Protocol and RDF Query Language
• SPARQL 1.1 provides graph update operations:
• INSERT DATA: adds explicit triples, given inline.
• DELETE DATA: removes explicit triples, given inline.
• DELETE/INSERT WHERE: updates based on triples calculated
from WHERE clause (as in SELECT and CONSTRUCT).
• LOAD: reads the content of a document into a graph.
• COPY/MOVE/APPEND: manipulates at named graph level.
• CLEAR/DROP: removes all triples in one or more graph.
47. Semantics on theWeb
47
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
INSERT DATA { GRAPH <http://myFavGroups/The_Beatles> {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
foaf:made <http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_> ,
< http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_> .
<http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_>
dc:title "Please Please Me".
< http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_ >
dc:title "Something New". } }
Insert the following albums recorded byThe Beatles into the graph
http://myFavGroups/The_Beatles
48. Semantics on theWeb
48
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
DELETE { ?album ?predicate ?object . }
WHERE {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d>
foaf:made ?album .
?album dc:title "Casualities";
?predicate ?object .}
Delete all the information about the album Casualities of The Beatles.
50. Linked Data
50
• Set of best practices for publishing data
on the Web.
• Data from different knowledge
domains, self-described,
linked and accessible.
• Follows 4 simple principles…
51. Linked Data Principles
1. Use URIs as names for things.
2. Use HTTP URIs so that users 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 users
can discover more things.
51
52. Linked Data Principles
• A foundational issue in Linked Data was the
distinction of URIs for object documents that might
describe them.
52
1. Use URIs as names for things.
53. Linked Data Principles
• HTTP allows a second way to distinguish real-world
objects from documents.
• Best practice says HTTP 303 and Location header
should be used.
53
2. Use HTTP URIs so user can look up those names.
54. • While RDF/XML should be the default for look-up.
• RDFa annotations in HTML are now also standard.
• SPARQL endpoint for queries are encouraged, or a
dump of the whole dataset.
Linked Data Principles
54
3. When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL,
Turtle1).
1 To become a standard.
55. What to return for a URI?
• Immediate description: triples where the URI is the subject.
• Backlinks: triples where the URI is the object.
• Related descriptions: information of interest in typical usage scenarios.
• Metadata: information as author and licensing information.
• Syntax: RDF descriptions as RDF/XML and human-readable formats.
Linked Data Principles
55
3. When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL,
Turtle1).
Source: How to Publish Linked Data on The Web - Chris Bizer, Richard Cyganiak, Tom Heath.
56. There are several ways to reuse URIs:
• direct reuse
• (OWL) sameAs
• (RDFS) seeAlso
• direct reuse of class/property
• (RDFS) sub-class/-property
• (OWL) equivalent class/property
• SKOS broad match
Linked Data Principles
56
4. Include links to other URIs, so that users can
discover more things.
Instance Level
Schema Level
57. Linked Data 5 Star
57
Data is available on the Web.
Data is available as machine-readable structured data.
Non-propietary formats are used.
Individual data identified with open standards.
Data is linked to other data provider.
58. Linked Data 5 Star
58
Example:
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
59. Linked Data 5 Star
59
Data is available on the Web
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
It can be retrieved using HTTP.
60. Linked Data 5 Star
60
Data is available as machine-readable structured data
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
"The Beatles"
http://upload.wikimedia.org/wi
kipedia/commons/thumb/d/df/
The_Fabs.JPG/600px-
The_Fabs.JPG
Please Please Me – 1963
A Hard Day‘s Night – 1964
Help! – 1965
Revolver – 1966
…
Images
Scanned Information
Plain text or …
(to continue on the next slide)
Machine-readable data:
61. Non-propietary formats are used
<schema "http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>The Beatles</name>
<member>John Lennon</member>
<member>Paul McCartney</member>
<member>George Harrison</member>
<member>Ringo Starr</member>
<picture>http://upload.wikimedia.org/wikipedia/common
s/thumb/d/df/The_Fabs.JPG/600px-
The_Fabs.JPG</picture>
<album year=1963>Please Please Me</album>
<album year=1964>A Hard Day‘s Night</album>
<album year=1965>Help!</album>
<album year=1966>Revolver</album>
…
</band>
Linked Data 5 Star
61
My Data
62. Linked Data 5 Star
62
Individual data identified with open standards
<schema
"http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>http://musicbrainz.org/artist/b10bbbfc-cf9e-
42e0-be17-e2c3e1d2600d</name>
<member>http://musicbrainz.org/artist/4d5447d7-
c61c-4120-ba1b-d7f471d385b9</member>
...
<album
year=1963>http://musicbrainz.org/release/5f3ba07b-
4a24-4cd5-b8ad-95ba0fcebec1</album>
…
</band>
My Data
In this context, "Revolver" is an
album! Not a gun.
URI: Uniform Resource Identifier
• Data is uniquely identified
The Beatles
John Lennon
Revolver
• Dissambiguation
63. Linked Data 5 Star
63
Data is linked to other data provider
<schema
"http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>http://musicbrainz.org/artist/b10bbbfc-cf9e-
42e0-be17-e2c3e1d2600d</name>
<member>http://musicbrainz.org/artist/4d5447d7-
c61c-4120-ba1b-d7f471d385b9</member>
...
<album
year=1963>http://musicbrainz.org/release/5f3ba07b-
4a24-4cd5-b8ad-95ba0fcebec1</album>
…
<seeAlso>http://dbpedia.org/resource/The_Beatles
</seeAlso>
</band>
http://dbpedia.org/resource/
The_Beatles
70. State of the LOD Cloud1
70
• Total Datasets:
295
1 Version 0.3, 09/19/2011
http://www4.wiwiss.fu-berlin.de/lodcloud/state
• Total Triples:
31,634,213,770
Distribution of triples by domain
71. State of the LOD Cloud1
71
• Total (Out-)Links:
503,998,829
1 Version 0.3, 09/19/2011
http://www4.wiwiss.fu-berlin.de/lodcloud/state
Distribution of links by domain
72. Exploring theWeb of Data
72
• Linked Data browsers
• Linked Data mashups
• Search engines
82. Summary
82
In this chapter we studied:
• The Web and its evolution.
• Web technology basics: HTTP, HTML, URI.
• Vocabularies to describe data.
• The Semantic Web stack: RDF, RDF-S, OWL, SPARQL.
• Linked Data concept and principles.
• Evolution of the LOD cloud.
• Browsers, mashups and search engines to explore the Web
of Data.
• Some application scenarios.
83. For exercises, quiz and further material visit our website:
EUCLID - Providing Linked Data 83
@euclid_project euclidproject euclidproject
http://www.euclid-project.eu
Other channels:
eBook Course
84. Acknowledgements
• Alexander Mikroyannidis
• Alice Carpentier
• Andreas Harth
• Andreas Wagner
• Andriy Nikolov
• Barry Norton
• Daniel M. Herzig
• Elena Simperl
• Günter Ladwig
• Inga Shamkhalov
• Jacek Kopecky
• John Domingue
• Juan Sequeda
• Kalina Bontcheva
• Maria Maleshkova
• Maria-Esther Vidal
• Maribel Acosta
• Michael Meier
• Ning Li
• Paul Mulholland
• Peter Haase
• Richard Power
• Steffen Stadtmüller
84