The document introduces the Semantic Web and the key technologies that enable it, including RDF, RDF Schema, OWL, and SPARQL. RDF allows for describing resources and relationships between them using triples. RDF Schema extends RDF with a vocabulary for describing properties and classes of resources. OWL builds on RDF and RDF Schema to provide additional expressive power for defining complex ontologies. SPARQL is a query language for retrieving and manipulating data stored in RDF format. These technologies work together to transform the existing web of documents into a web of linked data that can be processed automatically by machines.
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 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.
https://doi.org/10.6084/m9.figshare.11854626.v1
Presented at Dutch National Librarian/Information Professianal Association annual conference 2011 - NVB2011
November 17, 2011
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
Connections that work: Linked Open Data demystifiedJakob .
Keynote given 2014-10-22 at the National Library of Finland at Kirjastoverkkopäivät 2014 (https://www.kiwi.fi/pages/viewpage.action?pageId=16767828) #kivepa2014
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/sparql-rdf-query-language.html
and http://www.jarrar.info
The lecture covers:
- SPARQL Basics
- SPARQL Practical Session
https://doi.org/10.6084/m9.figshare.11854626.v1
Presented at Dutch National Librarian/Information Professianal Association annual conference 2011 - NVB2011
November 17, 2011
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
RDF is a general method to decompose knowledge into small pieces, with some rules about the semantics or meaning of those pieces. The point is to have a method so simple that it can express any fact, and yet so structured that computer applications can do useful things with knowledge expressed in RDF.
Connections that work: Linked Open Data demystifiedJakob .
Keynote given 2014-10-22 at the National Library of Finland at Kirjastoverkkopäivät 2014 (https://www.kiwi.fi/pages/viewpage.action?pageId=16767828) #kivepa2014
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/sparql-rdf-query-language.html
and http://www.jarrar.info
The lecture covers:
- SPARQL Basics
- SPARQL Practical Session
Information Extraction and Linked Data CloudDhaval Thakker
In the media industry there is a great emphasis on providing descriptive metadata as part of the media assets to the consumers. Information extraction (IE) is considered an important tool for metadata generation process and its performance largely depend on the knowledge base it utilizes. The advances in the “Linked Data Cloud” research provide a great opportunity for generating such knowledge base that benefit from the participation of wider community. In this talk, I will discuss our experiences of utilizing Linked Data Cloud in conjunction with a GATE-based IE system.
Presentation at ELAG 2011, European Library Automation Group Conference, Prague, Czech Republic. 25th May 2011
http://elag2011.techlib.cz/en/815-lifting-the-lid-on-linked-data/
Culture Geeks Feb talk: Adventures in Linked Data Landval.cartei
Culture Geeks talk: "Adventures in Linked Data Land", by Richard Light.
Feb, 25th 2009 - Regency Town House
Culture Geeks is a Brighton-based community open to everyone who is
interested in using digital technologies in the cultural sector.
Google's recent announcement that it will support the use of microformats in their search opens up new possibilities for librarians and library technologists to support the goals of the semantic web; namely to provide better access, reuse and recombinations of library resources and services on the open web. This lightning talk introduces the semantic web and semantic markup technologies.
Although RDF can be considered the corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who want to safely create and access well-structured data. There is a lack of common tools and methodologies that are available in more conventional settings to improve data quality by defining schemas that can later be validated. Two technologies have recently been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In the talk, we will briefly introduce both technologies using some examples and compare them. We will also present some challenges and applications related with RDF data shapes.
Talk given at: KTH Royal Institute of Technology, School of Industrial Engineering and Management, Mechatronics Division, 7th February, 2020
Although RDF is a corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who need to safely create and access well-structured data. There is a lack of common tools and methodologies that are available in more conventional settings to improve data quality by defining schemas that can later be validated. Two technologies have recently been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In the talk, we will review the history and motivation of both technologies. We will also and enumerate some challenges and future work with regards to RDF validation.
Como publicar los datos: datos abiertos y enlazados
Charla impartida en Jornadas Open Data y Transparencia: Ayuntamiento de Oviedo
11 de septiembre de 2017
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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.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
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.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
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Antifertility, Toxicity studies as per OECD guidelines
1. Introduction toSemantic Web Jose Emilio Labra Gayo University of Oviedo, Spain http://www.di.uniovi.es/~labra
2. The current Web Current Web = the biggest repository of information ever compiled by Humanity Designed for direct human consumption Lots of information available in: Natural Language in HTML English, Spanish, Chinese, Italian, etc. More and More multimedia Images, audio, video, etc. Too much data, not enough knowledge
7. Difficult queries Different languages Multimedia Combine different travel data Example: Travel from Oviedo to Rome Complex questions Example: Asturian people who play soccer
10. …href http://chemistry.uniovi.es Faculty of Chemistry Location Staff … href Location… … … href Unit of information = HTML Page Links are syntactic = href Staff … … href
11. The Semantic Web Data and semantic links School of Computer Engineering http://euitio.uniovi.es r:name 23 r:contains r:author r:age http://uniovi.es Mary Jordan r:name r:name r:contains University of Oviedo http://chemistry.uniovi.es Faculty of Chemistry r:name Unit of information = Data Links are semantic = Properties identified by URI
12. The semantic web The Semantic Web = Web of Data A vision of the web where data is published and can be linked with other data Goals: Reuse data Automate tasks Tim Berners Lee, inventor of the WWW
13. Features of the Web Non-centralized The information may not be consistent Dynamic information Data may change (or servers can fail) Lots of information The system cannot try to capture all the data Open system More information can appear in the future
14. Towards the Semantic Web Trust Digital Signature Proof Unifying Logic Query: SPARQL Ontologies OWL Rules RIF RDF Schema RDF XML URI Unicode Semantic web layer cake, by Tim Berners Lee
15. RDF Resource Description Framework (1998) Description of resources Resources = entities identified by URI Binary Relationships between resources Property = global name of the relationship (URI) Subject Predicate Object
16. RDF Triples Subject A resource identified by URI Can also be a blank node (bNode) Predicate Global Property identified by URI Object Value of property Can be URI, Literal or bNode
17. RDF Graph Model Can be represented in N-Triples @prefix r: <http://example.org#> . <http://euitio.uniovi.es> r:hasPicture <http://pictures.org/p1.jpg>. <http://euitio.uniovi.es> r:name "School of Computer Engineering". <http://pictures.org/p1.jpg> r:subject r:Building . r:Building r:hasPicture http://pictures.org/p1.jpg r:subject http://euitio.uniovi.es School of Computer Engineering r:name URIs are usually abbreviated using namespace declarations
18. RDF is Compositional graph3.rdf graph2.rdf r:subject r:Building http://euitio.uniovi.es r:contains http://pictures.org/p2.jpg University of Oviedo r:hasPicture http://uniovi.es r:name http://chemistry.uniovi.es r:contains r:name http://chemistry.uniovi.es Faculty of Chemistry RDF graphs can be composed graph1.rdf r:Building r:hasPicture http://pictures.org/p1.jpg r:subject http://euitio.uniovi.es School of Computer Engineering r:name
19. RDF is Compositional RDF graphs can be composed graph1.rdf + graph2.rdf+ graph3.rdf r:hasPicture http://pictures.org/p1.jpg r:subject School of Computer Engineering r:Building http://euitio.uniovi.es r:name r:subject r:contains http://pictures.org/p2.jpg University of Oviedo r:hasPicture http://uniovi.es r:name http://chemistry.uniovi.es r:contains r:name Faculty of Chemistry
20. Blank Nodes in RDF Blank nodes are represented as _:number in N-Triples <http://euitio.uniovi.es> r:author _:1 . <http://euitio.uniovi.es> r:author _:2 . _:1 r:name “John Smith” . _:2 r:age “23”^^xsd:positiveInteger . _:2 r:name “Mary Jordan” . Blank nodes are used to identify things that have no URI Example: The author of a web page is a person, not a URI r:name John Smith r:author r:age http://euitio.uniovi.es 23 r:author Mary Jordan r:name
21. Tables in RDF RDF can model relational databases r:firstName John r:lastName _:1 Smith r:nodes r:age _:0 25 r:next r:firstName Mary _:2 r:lastName Jordan r:age 23
22. RDF serialization formats There are several formats to represent RDF data: N3 RDF/XML N-Triples Turtle etc.
23. RDF Data Lots of information is currently published in RDF Some examples: DBPedia offers RDF triples of more than 80,000 persons, 293,000 places, 62,000 music albums, 36,000 films, etc.
26. Benefits of RDF RDF enables better integration of data Transform the Web from fileserver to database Linking open data Expose open datasets in RDF Set links among the data of those datasets Set up query endpoints Altogether billions of triples, millions of links
29. RDF Schema Example r:Person r:Teacher rdfs:subClassOf rdfs:range r:Teacher r:hasTeacher Meaning: if x rdf:type r:Teacher then x rdf:type r:Person Meaning: if X r:hasTeacher Y then Y rdf:type r:Teacher
31. SPARQL Simple Protocol and RDF Query Language Query languagefor the semantic web Graph matching language Extracts information from RDF models A protocol Defines a way of invoking a service WSDL description file HTTP and SOAP bindings It also defines an XML vocabulary for results
32. SPARQL Example prefix r: <http://example.org#> select ?n where { ?p r:subject r:Building. ?x r:hasPicture ?p . ?x r:name ?n . } “Find names of resources who have a picture whose subject is Building”
33. SPARQL example r:subject r:Building r:hasPicture ?p ?x r:name ?n ?p ?n ?x ?p Results School of Computer Engineering ?x ?n r:subject http://pictures.org/p1 r:Building r:hasPicture School of Computer Engineering r:subject r:name http://euitio.uniovi.es http://pictures.org/p2. r:contains University of Oviedo http://uniovi.es r:name r:hasPicture http://chemistry.uniovi.es r:contains r:name Faculty of Chemistry Faculty of Chemistry select ?n where { ?p r:subject r:Building. ?x r:hasPicture ?p . ?x r:name ?n . }
34. SPARQL & Inference The RDF Graph may be obtained by inference rdf:type Results Mary Jordan select ?n where { ?x rdf:type r:Person. ?x r:name ?n . } r:Teacher rdf:type rdfs:subClassOf r:name r:Person Mary Jordan
35. SPARQL More features Limitthe number of returned results; remove duplicates, sort them, … Optional subpatterns (match if possible…) Specify several data sources within the query Construct a graph combining a separate pattern and the query results, or simply askwhether a pattern matches Use datatypes and/or language tags when matching a pattern
36. Obtaining RDF SPARQL Endpoints offer an integration mechanism Big RDF datasets accesible to applications Example: DBPedia Nowadays Data is mostly in Databases It is not feasible to convert all databases to RDF More practical to convert on the fly Several systems: Oracle 11g, Sesame, …
37. RDF and HTML Problems to embed RDF/XML in (x)HTML It can be linked from an HTML page There are some “scrappers” to extract the structure of web pages and dynamically generate RDF Can be a solution for legacy web content Not very elegant 2 proposals for a more systematic way: GRDDL RDFa
39. RDFa RDFa defines attributes to add meta-data to HTML elements Similar to microformats cal:Event rdf:type cal:location meetingBcn Barcelona cal:dtstart 20070508T1000+0200 <html xmlns:cal="http://www.w3.org/2002/12/cal/ical#"> … <body> <p class="cal:Event" about="#meetingBcn"> I will attend a meeting in <span property="cal:location">Barcelona</span>, on <span property="cal:dtstart" content="20070508T1000+0200"> May 8th at 10am </span> </p> ... </body> </html>
40. Ontologies RDFS does not solve all requirements Applications need more expressivity and reasoning In RDFS, it is not possible to create new subclasses OWL (Web Ontology Language) offers a common language to define ontologies
41. What is an Ontology? A model of (some aspect of) the world
42. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy
43. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology
44. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace
45. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace Dogs
46. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain, e.g.: Anatomy Cellular biology Aerospace Dogs Hotdogs …
47. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain Specifies meaning of terms Heartis amuscular organ thatis part of the circulatory system
48. What is an Ontology? A model of (some aspect of) the world Introduces vocabularyrelevant to domain Specifies meaning of terms Heartis amuscular organ thatis part of the circulatory system Formalised using suitable logic
49. OWL OWL enables the description of new classes By enumeration Through intersection, union, complement Through property restrictions It is based on Description Logics Well defined semantics A subset of Predicate Logic Limited use of variables Binary predicates = Properties Unary predicates = Classes
50. OWL: Classes by enumeration Example: “The European Union is formed by Italy, France, Germany, etc.” EUCountry = { Italy, France, Germany, … } #Italy #France #EUCountry oneOf #Germany …
51. OWL: Set theoretic definitions Example: A person is a man or a woman Person = Man Woman #Man #Person unionOf #Woman It also has IntersectionOf and complementOf
52. OWL: Property restrictions It is possible to define new classes by restricting the property values of some class Reasoner acts as a classifier
53. OWL: Property restrictions Value constraints some () value must be from a class all () values must be from a class Example: “An european is a person who has nationality from a European Union country” European Person hasNationality EUCountry hasNationality(#Sergio, #Italy) Person(#Sergio) inference European(#Sergio) x (European(x) Person(x) y (hasNationality(x,y) EUCountry(y))
54. OWL: Property restrictions Cardinality constraints (ie, how many times the property is used on an instance?) exact cardinality minimum cardinality maximum cardinality Person hasFather = 1 x (Person(x) y (hasFather(x,y) z (hasFather(x,z) z = y)))
55. OWL: Property characterizations It is possible to characterize the behaviour of properties: Symmetric, transitive, functional, inverse functional, … transitive(isPartOf) inference isPartOf(#Rome,#Europe) isPartOf(#Rome, #Italy) isPartOf(#Italy, #Europe)
56. OWL: Datatype properties Properties whose range are typed literals Example: age datatypeProperty(#age) range(#age, xsd:positiveInteger) CanVote Person age > “18” inference CanVote(#Sergio) Person(#Sergio) age(#Sergio, 20)
57. OWL and Unique Name Assumption Web = Open System 2 different URIs could identify the same object OWL does not support Unique Name Assumption There is no error in the model It infers that #williamand#Billare the same Person hasFather = 1 hasFather(#Peter, #William) hasFather(#Peter, #Bill) Person(#Peter)
58. OWL: Open World Assumption Traditional systems used Closed World Assumption OWL uses the Open World Assumption Singleton isMarriedWith Person Married isMarriedWith Person Person(#Peter) Person(#Mary) Person(#James) isMarriedWith(#Mary,#Peter) Married(#James) It infers: Married(#Mary) It does not infer: Married(#Peter) Singleton(#Peter) It also infers that James …is married with someone… but it does not know with whom
59. Conclusions Semantic Web technologies = ready for deployment It is easy to publish something in RDF There are already huge amounts of data in RDF Linking to existing ontologies is already possible Social barriers have to be overcome “Open door” policy Use standards Connect to others so others can connect to you A little semantics can have a lot of impact
63. About the University of Oviedo Established in 1608 (400 years) Approx. 25000 students 31 faculties and schools 79 bachelorprogrammes 35 doctoral programmes 26 masterprogrammes
64. About the School of Computing Established in 1982 1000 students 1200 graduates Programmes: Bachelor in Software Engineering Master in Web Engineering Doctoral programme in Web Engineering http://www.informatica.uniovi.es
65. Acknowledgements Parts of this presentation have been taken from similar presentations by: Ivan Hermann Jacco van Ossenbruggen Nova Spivak Ian Horrocks