This document discusses RDF and RDF Schema, which are standards for representing and sharing information on the Semantic Web. It begins by explaining the limitations of XML for representing semantic relationships between objects. It then provides an overview of RDF, describing its core components like statements, graphs, and serialization formats. Examples are given to illustrate RDF graphs and XML serialization. The document also covers RDF Schema and how it can be used to define and share vocabularies for describing resources.
A hands on overview of the semantic webMarakana Inc.
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This document provides an overview of the Semantic Web. It defines the Semantic Web as linking data to data using technologies like RDF, RDFS, OWL and SPARQL. It explains that RDF represents information as subject-predicate-object statements that can be queried using SPARQL. RDFS allows defining schemas and classes for RDF data, while OWL adds more expressiveness for defining complex ontologies. The document outlines popular Semantic Web tools, public ontologies, and companies working in this domain. It positions the Semantic Web as a way to represent and share data universally on the web.
Presentation of SPARQL Anything at the MEI Linked Data IG Meeting in July 2021. We try SPARQL Anything with MEI XML files and experiment with simple and difficult tasks.
Knowledge graph construction with a façade - The SPARQL Anything ProjectEnrico Daga
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The document discusses a project called "SPARQL Anything" which aims to simplify knowledge graph construction by using SPARQL as the single language for representing and transforming diverse data formats into RDF. It presents an approach called "Facade-X" which defines a common RDF structure that can be applied over different formats like CSV, JSON, HTML, etc. This facade focuses on the RDF meta-model and aims to apply minimal ontological commitments. The document outlines how Facade-X can be used to represent different formats and provides examples of using SPARQL to transform sample data into RDF without committing to a specific domain ontology.
The SPARQL Anything project presents a novel approach called "Facade-X" for lifting data from any format (e.g. CSV, JSON, HTML, images) to RDF using a single consistent abstraction and SPARQL 1.1 queries. This minimizes complexity for end users by avoiding specialized mapping languages and hiding complexity. Initial feedback indicates the approach makes mappings easy to understand and the system easy to learn. Ongoing work aims to optimize performance for large datasets and support additional data sources.
The document describes how to use SPARQL to query Linked Open Data from the LODAC Museum dataset to retrieve information about art spots in Yokohama. It provides a SPARQL query that selects the URI, title, latitude, longitude, postal code, address and access information for organizations that are within the specified bounding box coordinates. The query utilizes prefixes to define namespaces and joins data from multiple sources using properties like dc:references.
This document discusses different graph query languages such as SQL, SPARQL, and Gremlin and provides examples of querying graph data models that were created from relational databases. It begins by introducing the authors and providing an overview of querying entity relations with different languages. Several examples are then given that demonstrate how to express common graph queries like finding connections between nodes in each language using sample data from GitHub and Northwind databases modeled as graphs.
This document discusses mapping data from relational databases to RDF. It provides an overview of the direct mapping approach and the R2RML standard for customizable mapping. Direct mapping generates URIs and RDF triples automatically based on the relational schema. R2RML allows customizing the mapping through a mapping language. The document also covers ETL systems for extracting relational data and loading it into triplestores as RDF, as well as use cases involving mapping biological and music databases to Linked Data.
Mapping Relational Databases to Linked DataEUCLID project
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The document discusses mapping relational databases to linked data using RDB2RDF standards. It describes a direct mapping that automatically maps relational data to RDF and R2RML, a customizable mapping language. R2RML allows specifying subject maps, predicate-object maps, and logical tables to control how relational data is represented in RDF. The document provides examples of mapping tables, columns, joins, and custom SQL queries to linked data using R2RML.
A hands on overview of the semantic webMarakana Inc.
Â
This document provides an overview of the Semantic Web. It defines the Semantic Web as linking data to data using technologies like RDF, RDFS, OWL and SPARQL. It explains that RDF represents information as subject-predicate-object statements that can be queried using SPARQL. RDFS allows defining schemas and classes for RDF data, while OWL adds more expressiveness for defining complex ontologies. The document outlines popular Semantic Web tools, public ontologies, and companies working in this domain. It positions the Semantic Web as a way to represent and share data universally on the web.
Presentation of SPARQL Anything at the MEI Linked Data IG Meeting in July 2021. We try SPARQL Anything with MEI XML files and experiment with simple and difficult tasks.
Knowledge graph construction with a façade - The SPARQL Anything ProjectEnrico Daga
Â
The document discusses a project called "SPARQL Anything" which aims to simplify knowledge graph construction by using SPARQL as the single language for representing and transforming diverse data formats into RDF. It presents an approach called "Facade-X" which defines a common RDF structure that can be applied over different formats like CSV, JSON, HTML, etc. This facade focuses on the RDF meta-model and aims to apply minimal ontological commitments. The document outlines how Facade-X can be used to represent different formats and provides examples of using SPARQL to transform sample data into RDF without committing to a specific domain ontology.
The SPARQL Anything project presents a novel approach called "Facade-X" for lifting data from any format (e.g. CSV, JSON, HTML, images) to RDF using a single consistent abstraction and SPARQL 1.1 queries. This minimizes complexity for end users by avoiding specialized mapping languages and hiding complexity. Initial feedback indicates the approach makes mappings easy to understand and the system easy to learn. Ongoing work aims to optimize performance for large datasets and support additional data sources.
The document describes how to use SPARQL to query Linked Open Data from the LODAC Museum dataset to retrieve information about art spots in Yokohama. It provides a SPARQL query that selects the URI, title, latitude, longitude, postal code, address and access information for organizations that are within the specified bounding box coordinates. The query utilizes prefixes to define namespaces and joins data from multiple sources using properties like dc:references.
This document discusses different graph query languages such as SQL, SPARQL, and Gremlin and provides examples of querying graph data models that were created from relational databases. It begins by introducing the authors and providing an overview of querying entity relations with different languages. Several examples are then given that demonstrate how to express common graph queries like finding connections between nodes in each language using sample data from GitHub and Northwind databases modeled as graphs.
This document discusses mapping data from relational databases to RDF. It provides an overview of the direct mapping approach and the R2RML standard for customizable mapping. Direct mapping generates URIs and RDF triples automatically based on the relational schema. R2RML allows customizing the mapping through a mapping language. The document also covers ETL systems for extracting relational data and loading it into triplestores as RDF, as well as use cases involving mapping biological and music databases to Linked Data.
Mapping Relational Databases to Linked DataEUCLID project
Â
The document discusses mapping relational databases to linked data using RDB2RDF standards. It describes a direct mapping that automatically maps relational data to RDF and R2RML, a customizable mapping language. R2RML allows specifying subject maps, predicate-object maps, and logical tables to control how relational data is represented in RDF. The document provides examples of mapping tables, columns, joins, and custom SQL queries to linked data using R2RML.
A Hands On Overview Of The Semantic WebShamod Lacoul
Â
The document provides an overview of the Semantic Web and introduces key concepts such as RDF, RDFS, SPARQL, OWL, and Linked Open Data. It begins with defining what the Semantic Web is, why it is useful, and how it differs from the traditional web by linking data rather than documents. It then covers RDF for representing data, RDFS for defining schemas, and SPARQL for querying RDF data. The document also discusses OWL for building ontologies and Linked Open Data initiatives that have published billions of RDF triples on the web.
This chapter introduces the semantic modeling procedure, detailing its technical characteristics, possibilities and limitations. First, we present the languages that are used for semantic description. We present RDF, RDFS and OWL, describe their expressiveness in terms of describing Web Resources, and the abilities they provide in order to describe, query, administer and manage resources at a semantic layer. Next, we present the vocabularies that are used in order to provide common grounds in understanding and communicating ideas and concepts. The technologies, together with the vocabularies used, altogether comprise the modern landscape of Semantic Web/Linked Data applications and serve as the basis for maintaining, analyzing datasets and building applications on top of them.
semantic web resource description frameworkKomalFatima37
Â
This chapter discusses describing web resources in RDF. It covers the basic ideas of RDF including resources, properties, statements and representing statements as triples, graphs, and XML. It also discusses RDF Schema and querying RDF documents with SPARQL.
The document provides examples of representing data in RDF formats including RDF/XML, Notation 3, Turtle and triples. It shows how to represent basic statements and relationships between resources as well as more complex data structures like bags, sequences and collections. Examples are given for converting between the different RDF syntaxes and representing graphs in RDF/XML.
The document discusses the RDF data model. The key points are:
1. RDF represents data as a graph of triples consisting of a subject, predicate, and object. Triples can be combined to form an RDF graph.
2. The RDF data model has three types of nodes - URIs to identify resources, blank nodes to represent anonymous resources, and literals for values like text strings.
3. RDF graphs can be merged to integrate data from multiple sources in an automatic way due to RDF's compositional nature.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
Â
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.
A presentation on Application Architecture for Semantic Web Applications based on chapter 4 of the book Semantic Web for the Working Ontologist by Dean Allemang and Jim Hendler. It focusses on RDF parsing and serialising and RDF stores.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
RDF is a general-purpose language for representing information on the web. It allows for describing resources and the relationships between them using subject-predicate-object expressions called triples. RDF is used as a foundation for the semantic web and allows machines to mechanically process and interpret the logical pieces of meaning in data. While RDF does not define specific properties or vocabularies, it provides mechanisms for describing properties and classes of resources in a human- or machine-readable format.
This document discusses the Web Ontology Language (OWL), which allows defining classes, individuals, and properties to represent domain knowledge and interconnections in a machine-readable format. OWL builds upon RDF and RDF Schema by adding more expressive features to define complex class descriptions and restrictions over properties. These features include logical class constructors, quantifiers to specify property restrictions, and cardinality constraints. OWL syntax can represent ontologies as XML documents that define classes, properties, and individuals with semantic relationships.
This chapter discusses metadata and ontologies for digitally documenting cultural heritage. It introduces XML, RDF, Dublin Core, and the Semantic Web as standards for representing metadata. It also discusses OWL for defining ontologies and CIDOC-CRM as an ontology for cultural heritage documentation. The chapter aims to explain how these standards help achieve interoperability when sharing digital cultural heritage information on the internet.
Publishing Linked Data 3/5 Semtech2011Juan Sequeda
Â
This document summarizes techniques for publishing linked data on the web. It discusses publishing static RDF files, embedding RDF in HTML using RDFa, linking to other URIs, generating linked data from relational databases using RDB2RDF tools, publishing linked data from triplestores and APIs, hosting linked data in the cloud, and testing linked data quality.
Comparative study on the processing of RDF in PHPMSGUNC
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RAP, ARC, and Raptor are PHP libraries for processing RDF. RAP provides classes for manipulating RDF models and triples. ARC uses associative arrays to represent triples and resources, providing faster performance. Raptor is a C library with PHP bindings that parses RDF files into models. It had the highest performance on tests, passing all tests, while RAP and ARC passed most but not all tests.
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.
The document provides an introduction to RDF (Resource Description Framework). It discusses that RDF is a framework for describing resources using statements with a subject, predicate, and object. RDF identifies resources with URIs and describes resources and their properties and property values. An example RDF document is provided that describes CDs with properties like artist, country, and price.
The document discusses use cases for applying the LOM metadata standard with RDF bindings, including an educational broadcasting company in Sweden using it to describe their media content and an online peer-to-peer network for exchanging metadata. It also covers issues with synchronizing the LOM-XML and LOM-RDF versions and the status of the RDF binding for LOM being ballot-ready with some limitations noted.
Media IT - XML and XML Transformation (XSLT)Serge Linckels
Â
This document discusses XML transformation using XSLT. It provides an overview of XSLT, explaining that XSLT is an XML-based language used to specify rules for transforming one XML document into another document in a different format like HTML or text. It describes the XSLT transformation principle, including how an XSL stylesheet is used to transform the abstract tree representation of an XML document based on a set of transformation rules. Examples are given of transforming an XML document into HTML using an XSL stylesheet and a Java program for performing XSL transformations.
This document discusses XML and its sub-languages. It begins by explaining the limitations of HTML and introduces XML as a markup language that allows the creation of new tags. XML is described as a metalanguage that can be adapted to different domains by creating specialized sub-languages. The document then covers XML specifications, including elements, attributes, and proper nesting of tags.
A Hands On Overview Of The Semantic WebShamod Lacoul
Â
The document provides an overview of the Semantic Web and introduces key concepts such as RDF, RDFS, SPARQL, OWL, and Linked Open Data. It begins with defining what the Semantic Web is, why it is useful, and how it differs from the traditional web by linking data rather than documents. It then covers RDF for representing data, RDFS for defining schemas, and SPARQL for querying RDF data. The document also discusses OWL for building ontologies and Linked Open Data initiatives that have published billions of RDF triples on the web.
This chapter introduces the semantic modeling procedure, detailing its technical characteristics, possibilities and limitations. First, we present the languages that are used for semantic description. We present RDF, RDFS and OWL, describe their expressiveness in terms of describing Web Resources, and the abilities they provide in order to describe, query, administer and manage resources at a semantic layer. Next, we present the vocabularies that are used in order to provide common grounds in understanding and communicating ideas and concepts. The technologies, together with the vocabularies used, altogether comprise the modern landscape of Semantic Web/Linked Data applications and serve as the basis for maintaining, analyzing datasets and building applications on top of them.
semantic web resource description frameworkKomalFatima37
Â
This chapter discusses describing web resources in RDF. It covers the basic ideas of RDF including resources, properties, statements and representing statements as triples, graphs, and XML. It also discusses RDF Schema and querying RDF documents with SPARQL.
The document provides examples of representing data in RDF formats including RDF/XML, Notation 3, Turtle and triples. It shows how to represent basic statements and relationships between resources as well as more complex data structures like bags, sequences and collections. Examples are given for converting between the different RDF syntaxes and representing graphs in RDF/XML.
The document discusses the RDF data model. The key points are:
1. RDF represents data as a graph of triples consisting of a subject, predicate, and object. Triples can be combined to form an RDF graph.
2. The RDF data model has three types of nodes - URIs to identify resources, blank nodes to represent anonymous resources, and literals for values like text strings.
3. RDF graphs can be merged to integrate data from multiple sources in an automatic way due to RDF's compositional nature.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
Â
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.
A presentation on Application Architecture for Semantic Web Applications based on chapter 4 of the book Semantic Web for the Working Ontologist by Dean Allemang and Jim Hendler. It focusses on RDF parsing and serialising and RDF stores.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
RDF is a general-purpose language for representing information on the web. It allows for describing resources and the relationships between them using subject-predicate-object expressions called triples. RDF is used as a foundation for the semantic web and allows machines to mechanically process and interpret the logical pieces of meaning in data. While RDF does not define specific properties or vocabularies, it provides mechanisms for describing properties and classes of resources in a human- or machine-readable format.
This document discusses the Web Ontology Language (OWL), which allows defining classes, individuals, and properties to represent domain knowledge and interconnections in a machine-readable format. OWL builds upon RDF and RDF Schema by adding more expressive features to define complex class descriptions and restrictions over properties. These features include logical class constructors, quantifiers to specify property restrictions, and cardinality constraints. OWL syntax can represent ontologies as XML documents that define classes, properties, and individuals with semantic relationships.
This chapter discusses metadata and ontologies for digitally documenting cultural heritage. It introduces XML, RDF, Dublin Core, and the Semantic Web as standards for representing metadata. It also discusses OWL for defining ontologies and CIDOC-CRM as an ontology for cultural heritage documentation. The chapter aims to explain how these standards help achieve interoperability when sharing digital cultural heritage information on the internet.
Publishing Linked Data 3/5 Semtech2011Juan Sequeda
Â
This document summarizes techniques for publishing linked data on the web. It discusses publishing static RDF files, embedding RDF in HTML using RDFa, linking to other URIs, generating linked data from relational databases using RDB2RDF tools, publishing linked data from triplestores and APIs, hosting linked data in the cloud, and testing linked data quality.
Comparative study on the processing of RDF in PHPMSGUNC
Â
RAP, ARC, and Raptor are PHP libraries for processing RDF. RAP provides classes for manipulating RDF models and triples. ARC uses associative arrays to represent triples and resources, providing faster performance. Raptor is a C library with PHP bindings that parses RDF files into models. It had the highest performance on tests, passing all tests, while RAP and ARC passed most but not all tests.
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.
The document provides an introduction to RDF (Resource Description Framework). It discusses that RDF is a framework for describing resources using statements with a subject, predicate, and object. RDF identifies resources with URIs and describes resources and their properties and property values. An example RDF document is provided that describes CDs with properties like artist, country, and price.
The document discusses use cases for applying the LOM metadata standard with RDF bindings, including an educational broadcasting company in Sweden using it to describe their media content and an online peer-to-peer network for exchanging metadata. It also covers issues with synchronizing the LOM-XML and LOM-RDF versions and the status of the RDF binding for LOM being ballot-ready with some limitations noted.
Media IT - XML and XML Transformation (XSLT)Serge Linckels
Â
This document discusses XML transformation using XSLT. It provides an overview of XSLT, explaining that XSLT is an XML-based language used to specify rules for transforming one XML document into another document in a different format like HTML or text. It describes the XSLT transformation principle, including how an XSL stylesheet is used to transform the abstract tree representation of an XML document based on a set of transformation rules. Examples are given of transforming an XML document into HTML using an XSL stylesheet and a Java program for performing XSL transformations.
This document discusses XML and its sub-languages. It begins by explaining the limitations of HTML and introduces XML as a markup language that allows the creation of new tags. XML is described as a metalanguage that can be adapted to different domains by creating specialized sub-languages. The document then covers XML specifications, including elements, attributes, and proper nesting of tags.
The document discusses author rights and intellectual property. It covers the history and legal basics of author rights, defines intellectual property, outlines author rights such as the right to disclose or modify works, and discusses exceptions and limitations to author rights like fair use and Creative Commons licensing. The document also provides information on organizations in Luxembourg that represent author interests and manage author rights, as well as how to obtain legal information on author rights issues.
This document discusses images and image processing. It begins with an introduction to perspectives, lighting techniques like high-key and low-key lighting, and other techniques used in images like colors vs black and white. It then covers the golden ratio and how it has been applied in art, architecture, design, music and more. Finally, it discusses the physical and optical basics of light, colors, reflection, absorption and transmission of light.
This document contains lecture slides on coding and compression from a media IT course. It discusses key concepts from information theory like entropy, which is a measure of the information produced by a data source, and redundancy, which refers to extra bits used in data transmission. Examples are given to illustrate entropy calculation and how optimal coding aims to minimize average code length and redundancy. Students are assigned a presentation on a selected data format to explain its encoding, compression and other features.
This document contains lecture slides on the topic of natural language processing. It discusses concepts like semantics, syntax, pragmatics, tagging and parsing natural language, wordnet as a lexical database, and computational linguistics tools like the TreeTagger. It provides examples of semantic annotation of lecture videos and solving word problems using natural language. The final section describes a practical exercise for students to install and test the TreeTagger tool.
This document discusses digital signal processing and digitalization. It covers topics like analog versus digital signals, sampling and quantization in digitalization, signal coding, Fourier transforms, and examples of digitizing analog signals with different sampling rates and bit resolutions. The key points are that digitalization converts analog signals to digital by sampling at a rate and quantizing to discrete levels, and the quality depends on the sampling rate and bit resolution used.
The document discusses semantic search and describes several approaches to implementing semantic search, including inserting semantics into HTML, multimedia information retrieval, and developing semantic search engines. It provides examples of semantic search engines like E-Librarian Service, Ask.com, Hakia, and WolframAlpha. Key sections cover Google search, semantic interpretation of queries, calculating semantic distance between queries and documents, and various information retrieval models used in semantic search.
The document introduces ontologies and discusses their role in the Semantic Web. It defines an ontology as an explicit specification of a conceptualization that is shared between people or software agents. Ontologies allow concepts and relationships between concepts to be formally defined so that software applications can interpret data in the same way. The document outlines different types of ontologies including upper ontologies that define common concepts across domains, and domain ontologies that define the terms and relationships within a specific knowledge domain. Formal ontology languages are also discussed as a way to represent ontologies in a machine-readable format.
The document is a lecture on XML and its sub-languages. It begins with an introduction to XML, describing it as an extensible markup language that allows the creation of new tags to structure documents in various domains. It then discusses XML specifications like elements, attributes, and namespaces. Later sections cover document type definitions (DTDs) for validating XML documents and ensuring they follow specified syntax rules. The document provides examples and explanations of various XML concepts.
The document provides an overview of the Semantic Web, including a comparison to the classical web. It describes the vision and architecture of the Semantic Web, highlighting how it aims to add meaning/semantics to data on the web through languages like RDF, OWL, and rules/ontologies to enable reasoning. The Semantic Web is intended to address limitations of HTML/the classical web like a lack of semantics, standardization, and ability to perform queries/reasoning over web content.
The document discusses the evolution of the internet and world wide web, including early networks like ARPANET, the development of the WWW by Tim Berners-Lee, and the rise of social media and user-generated content. It provides statistics on internet usage growth globally and the increasing percentage of populations that are internet users. Examples of popular social networks and platforms are given like Facebook, YouTube, and Skype. The introduction section concludes with a discussion of artificial intelligence, machine learning, and how "Web 2.0" enabled more user participation and collaboration online.
1. The document discusses natural language search in multimedia knowledge bases using semantic web technologies.
2. It explores approaches like keyword matching, syntax trees, natural language processing, and ontological matching to interpret user queries and retrieve relevant multimedia objects.
3. The goal is to go beyond simple keyword matching by leveraging semantics to handle issues like word order, size, and interpret user intent.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
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Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
What is an RPA CoE? Session 2 â CoE RolesDianaGray10
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In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
âą What roles are essential?
âą What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
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This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
ScyllaDB is making a major architecture shift. Weâre moving from vNode replication to tablets â fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
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đ Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
đ Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
đ» Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
đ Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM âisâ and âisnâtâ
- Understand the value of KM and the benefits of engaging
- Define and reflect on your âwhatâs in it for me?â
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
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Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
ââTwitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
â
âFacebook(Meta): https://www.facebook.com/mydbops/
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
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HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.Â
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Hereâs a summary of the key insights and topics discussed:Â
Key Takeaways:Â
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration:Â Insights were shared on how inQubaâs advanced technology can streamline customer interactions and drive operational efficiency.
From Natural Language to Structured Solr Queries using LLMsSease
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This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or âcognitiveâ gap) remains between the data user needs and the data producer constraints.
That is where AI â and most importantly, Natural Language Processing and Large Language Model techniques â could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr indexâs metadata.
This approach leverages the LLMâs ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
What is an RPA CoE? Session 1 â CoE VisionDianaGray10
Â
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
âą The role of a steering committee
âą How do the organizationâs priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
Â
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
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What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
đ Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
đ» Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Â
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
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Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
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Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
Introducing BoxLang : A new JVM language for productivity and modularity!
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Semantic Web - RDF
1. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 1
Semantic Web
Unit 4: RDF and RDF Schema
Faculty of Science, Technology and Communication (FSTC)
Bachelor en informatique (professionnel)
2. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 2
4. RDF and RDF Schema
2
Semantic Web Roadmap:
Controlled growth bottom
up according to this
architecture.
Architecture was (slightly)
modified in the last years.
2
3. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 3
4. RDF and RDF Schema
4.1. Why is XML not Sufficient?
4.2. RDF Specifications
4.3. RDF Schema (RDFS)
4.4. SPARQL â RDF Query Language
4.5. Sharing Vocabulary in RDF
4.6. Jena â RDF in Java
4.7. References
33
4. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 4
4.1. Why is XML not Sufficient?
4. RDF and RDF Schema
Level of knowledge representation and semantics
XML / XML Schema
objects, structure
RDF / RDF Schema
knowledge about
objects, relations
between objects
OWL
domain knowledge,
interconnections
44
5. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 5
4.1. Why is XML not Sufficient?
4. RDF and RDF Schema
Problem 1: missing relational expressivity
What is the relation between a
"Professor", a "Secretary" and a
"PhDStudent"?
Christoph Meinel
Viola Brehmer
Long Wang
Feng Cheng
Dirk Cordel
Serge Linckels
Harald Sack
<ChairMeinel>
<Professor>
<FirstName>Christoph</FirstName>
<LastName>Meinel</LastName>
</Professor>
<Secretary>Viola Brehmer</Secretary>
<PhDStudent>Long Wang</PhDStudent>
<PhDStudent>Feng Cheng</PhDStudent>
<PhDStudent>Dirk Cordel</PhDStudent>
<PhDStudent>Serge Linckels</PhDStudent>
<FormerPhDStudent>Harald Sack</FormerPhDStudent>
</ChairMeinel>
XML document
We need a more powerful formalism above XML to
describe relations between objects
55
6. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 6
4.1. Why is XML not Sufficient?
4. RDF and RDF Schema
Problem 2: missing relational expressivity
Are all three documents
equivalent?
Do they represent the
same information?
<phonenumber>
<owner>Serge Linckels</owner>
<number>++352-691-123456</number>
</phonenumber>
XML document 1
We need a more powerful formalism above XML to
describe objects with a shared vocabulary
<person>
<name>Serge Linckels</name>
<phone>++352-691-123456</phone>
</person>
XML document 2
<person name="Serge Linckels" phone="++352-691-123456" />
XML document 3
Solving this kind of problem requires a
matching of all 3 documents and their
according DTD / XML Schema, e.g., with
XSLT. This reengineering is difficult,
complex and can be avoided.
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4.2. RDF Specifications
4. RDF and RDF Schema
RDF overview
Resource Description Framework
RDF allows to describe resources in
a more expressive way than XML
RDF can be serialized as XML (or in
other formats)
The syntax of a RDF document can
be described in a RDF Schema
RDF documents can be queried
using optimized query languages,
e.g., SPARQL
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8. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 8
RDF
English
4.2. RDF Specifications
4. RDF and RDF Schema
RDF standard
W3C recommendation
Make statements (assertions) about resources, e.g.,
"Serge Linckels is a teacher. He teaches RDF."
The object "Serge Linckels" has a property "hasJob" that has
the value "teacher"
The object "Serge Linckels" has a property "toTeach" that has
the value "RDF"
Statement 1
Statement 2
RDF statement
A RDF statement is a "triple": resource â property â value
teacherSerge Linckels
hasJob
Subject Predicate Object
Resource Property Value
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4.2. RDF Specifications
4. RDF and RDF Schema
RDF graph
RDF statements are represented as directed and labeled graphs, where each resource is
represented as a node
Each resource is identified by a URI
teacher
hasJob
RDF
toTeach
RDF graphs can be serialized in different formats, e.g., as XML
RDF serialization
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:myNS="http://www.linckels.lu/myNS/">
<rdf:Description rdf:about="http://www.linckels.lu/">
<myNS:hasJob>teacher</myNS:hasJob>
<myNS:toTeach>RDF</myNS:toTeach>
</rdf:Description>
</rdf:RDF>
http://www.linckels.lu/
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4.2. RDF Specifications
4. RDF and RDF Schema
RDF validation
RDF graphs can be validated with "a
validator", e.g.,
http://www.w3.org/RDF/Validator/
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19. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 19
4.2. RDF Specifications
4. RDF and RDF Schema
RDF containers - overview
rdf:Bag â containing unordered lists of resources or literals, with duplicate data allowed
rdf:Seq â containing ordered lists of resources or literals, with duplicate data allowed
rdf:Alt â containing resources or literals that represent possible alternatives for a specific
value
RDF collections - overview
Unlike a container, a collection is a finite grouping of resources or literals
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:myNS="http://www.linckels.lu/myNS/">
<rdf:Description rdf:about="http://www.linckels.lu/">
<myNS:hasFriends rdf:parseType="Collection">
<rdf:Description rdf:about="http://www.toto.lu" />
<rdf:Description rdf:about="http://www.titi.lu" />
<rdf:Description rdf:about="http://www.tata.lu" />
</myNS:hasFriends>
<myNS:hasName>Serge Linckels</myNS:hasName>
</rdf:Description>
</rdf:RDF>
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rdf:list
4.2. RDF Specifications
4. RDF and RDF Schema
RDF lists
Serge Linckels
http://www.linckels.lu/
hasName
http://www.toto.lu/
rdf:first
http://www.titi.lu/
rdf:first
rdf:rest
http://www.tata.lu/
rdf:first
rdf:rest
rdf:nil
rdf:rest
A list is a terminated sequence of
items
Traversing a list becomes a
matter of finding the start node
and then accessing the next
predicates for that node
2020
22. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 22
rdf:Statement
4.2. RDF Specifications
4. RDF and RDF Schema
Reification
Reification is a method of formally
modeling a statement about another
statement
sweetThis lemon
are
Serge Linckels
says
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:myNS="http://www.linckels.lu/myNS/">
<rdf:Description rdf:about="http://www.linckels.lu/">
<myNS:says rdf:resource="#myStatement" />
</rdf:Description>
<rdf:Statement rdf:about="#myStatement">
<rdf:subject rdf:resource="http://www.lemon123.lu" />
<rdf:predicate rdf:resource="myNS:are" />
<rdf:object>sweet</rdf:object>
</rdf:Statement>
</rdf:RDF>
identification of
the statement
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23. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 23
4.2. RDF Specifications
4. RDF and RDF Schema
rdf:Statement
Reification â problem
Reification is always an subjective view and can cause ambiguities
sweetThis lemon
are
Serge Linckels
says
rdf:Statement
bitter
are
DenisZampunieris
says
Who do you believe?
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24. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 24
4.2. RDF Specifications
4. RDF and RDF Schema
Reification is useful
Useful to formally express trust
1.61803
Ï hasValue
Serge Linckels
says
3.14159
hasValue
Who do you believe?
A. Einstein
D. Zampunieris
M. Planck
O. Hahn
says
says
Create metadata over assertions (assertion â fact)
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27. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 27
4.3. RDF Schema (RDFS)
4. RDF and RDF Schema
RDFS overview
RDFS specifies:
- how RDF constructs relate to each other
- how RDF constructs can be diagrammed in XML
RDFS is a rules-based dictionary that:
- defines the elements of importance to a domain
- describes how these elements relate to one another
RDF is a way of describing data
RDFS is a domain-neutral way of describing the metadata that can be used to
describe the data for domain-specific vocabulary
RDFS allows to define:
- abstract datatypes (classes)
- hierarchically structure the datamodel
- properties and relations (e.g., inheritance)
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28. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 28
4.3. RDF Schema (RDFS)
4. RDF and RDF Schema
RDFS â defining classes
rdfs:Resource is the RDFS top-class
rdfs:Resource
myNS:Person
fullname
Example:
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#">
<rdfs:class rdf:about="http://www.linckels.lu/myNS/Person">
<rdfs:subClassOf
rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#Resource" />
</rdfs:class>
<rdf:Property rdf:about="http://www.linckels.lu/myNS/fullname">
<rdfs:domain rdf:resource="http://www.linckels.lu/myNS/Person" />
</rdf:Property>
</rdf:RDF>
rdfs:class â definition of a new class that inherits from the super class
specified in rdfs:subClassOf
rdf:Property â definition of a new property of a class specified in
rdfs:domain
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30. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 30
4.3. RDF Schema (RDFS)
4. RDF and RDF Schema
RDFS â defining relations and restrictions rdfs:Resource
myNS:Person
worksFor
age
<rdf:Property rdf:about="http://www.linckels.lu/myNS/worksFor">
<rdfs:domain rdf:resource="http://www.linckels.lu/myNS/Person" />
<rdfs:range rdf:resource="http://www.linckels.lu/myNS/Firm" />
</rdf:Property>
myNS:Firm
rdfs:range â specifies the classes the property can
reference as values
<rdf:Property rdf:about="http://www.linckels.lu/myNS/age">
<rdfs:domain rdf:resource="http://www.linckels.lu/myNS/Person" />
<rdfs:range rdf:resource="http://www.w3.org/2001/XMLSchema#integer" />
</rdf:Property>
This mechanism allows to define basic relations between
classes and to define datatypes for property values
A validator checks only the syntax, not if
the values for properties are correct. This
task must be assumed by the application.
!
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31. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 31
4.3. RDF Schema (RDFS)
4. RDF and RDF Schema
RDFS â other elements
rdfs:label â provides a (human) readable version the resource's name
rdfs:Literal - defines a node as being a literal value, not a resource
rdfs:Comment â defines a comment
rdfs:Container â superclass of all RDF container elements (rdf:Bag, rdf:Seq and rdf:Alt)
rdfs:member â superproperty for each numbered container element (e.g., _1, _2, etc.)
rdfs:subPropertyOf â specifies a property as a refinement of another property
rdfs:seeAlso â identifies another resource that contains additional information
rdfs:isDefinedBy â identifies the namespace for the resource, preventing any ambiguity
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4.4. SPARQL â RDF Query Language
4. RDF and RDF Schema
RDF query languages â overview
There exists multiple languages to query RDF data: ARQ, RDQL, N3QL, RQL, SPARQL, SeRQL,
Versa, XUL, Adenine, RDQ, N3QLâŠ
SPARQL (SPARQL Protocol and RDF Query Language) was standardized in January 2008 by the
W3C
"SQL like" syntax to query RDF data as triples
@prefix myNS: <http://www.linckels.lu/myNS#>
SELECT DISTINCT ?X
FROM <http://www.linckels.lu/myRDF-file.rdf>
WHERE
{
myNS:Person myNS:worksFor ?X .
}
Example: "show me all firms that have employees"
Complex filtering is possible, e.g.,
- string operations
- conjunctions and disjunctions of logical tests
- datatype checking
32
triple
32
33. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 33
4.4. SPARQL â RDF Query Language
4. RDF and RDF Schema
RDF query languages â overview
@prefix myNS: <http://www.linckels.lu/myNS#>
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?person ?firm
FROM <http://www.linckels.lu/myRDF-file.rdf>
WHERE
{
?person rdf:type myNS:Person .
?person myNS:worksFor ?firm .
?firm rdf:type myNS:Firm .
?person myNS:age ?age .
FILTER (?age > 30) .
FILTER (?age <= 40) .
}
Example: "show me all persons and the firms they work for, who are between 31 and 40 in
age"
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36. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 36
4.5. Sharing Vocabulary in RDF
4. RDF and RDF Schema
Dublin Core (DC)
Simple Dublin Core Metadata Element Set (DCMES) consists of 15 metadata elements
Qualified Dublin Core Metadata Element Set (QDCME) includes three additional elements
(audience, provenance and rightsholder), as well as a group of element refinements
title : a name given to a resource
creator : an entity responsible for making the content of the resource
subject : the topic of the content of the resource
description : an account of the content of the resource
publisher : an entity responsible for making the content available
contributor : an entity responsible for making contributions to the content of the resource
date : a date associated with an event in the life cycle of the resource
type : the nature or genre of the content of the resource
format : the physical or digital manifestation of the resource
identifier : an unambiguous reference to the resource within a given context
source : a reference to the resource from which the present resource is derived
language : a language of the intellectual content of the resource
relation : a reference to a related resource
coverage : the extent or scope of the content of the resource
rights : information about rights held in and over the resource
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37. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 37
FOAF Basics
Personal Info
Online Accounts
Projects & Groups
Documents & Images
4.5. Sharing Vocabulary in RDF
4. RDF and RDF Schema
Friend of a Friend (FOAF)
FOAF is a way to describe people and relationships to computers; it is a vocabulary
http://www.foaf-project.org/
Agent
Person
name
nick
title
homepage
mbox
mbox_sha1sum
img
depiction
surname
family_name
givenname
firstName
weblog
knows
interest
currentProject
pastProject
plan
based_near
workplaceHomepage
workInfoHomepage
schoolHomepage
topic_interest
publications
geekcode
myersBriggs
dnaChecksum
OnlineAccount
OnlineChatAccount
OnlineEcommerceAccount
OnlineGamingAccount
holdsAccount
accountServiceHomepage
accountName
icqChatID
msnChatID
aimChatID
jabberID
yahooChatID
Project
Organization
Group
member
membershipClass
fundedBy
theme
Document
Image
PersonalProfileDocument
topic (page)
primaryTopic
tipjar
sha1
made (maker)
thumbnail
logo
3737
39. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 39
4.6. Jena â RDF in Java
4. RDF and RDF Schema
Jena â overview
Jena is a Java framework for building Semantic Web applications
Jena provides a programmatic environment for RDF, RDFS, OWL and SPARQL
Jena is open source and grown out of work with the HP Labs Semantic Web Programme
http://jena.sourceforge.net/
3939
40. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 40
4.6. Jena â RDF in Java
4. RDF and RDF Schema
Jena â example: creating a resource
import com.hp.hpl.jena.mem.ModelMem;
import com.hp.hpl.jena.rdf.model.*;
static String myDemo= "http://www.linckels.lu/";
static String myNS = " http://www.linckels.lu/myNS";
// create an empty graph
Model myModel = new ModelMem();
// create the resource
Resource myResource = myModel.createResource(myDemo);
// create the predicate (property)
Property fullname = myModel.createProperty(myNS,"fullname");
// add the property with associated value (object)
myResource.addProperty(fullname, "Serge Linckels");
// print RDF/XML of model to system out
myModel.write(new PrintWriter(System.out));
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41. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 41
4.6. Jena â RDF in Java
4. RDF and RDF Schema
Jena â example: browsing through a graph
// using a statement iterator
StmtIterator iter = myModel.listStatements();
while (iter.hasNext()) {
// read a statement (resource, predicate, object)
Statement stmt = iter.next();
// identify resource
Resource subject = stmt.getSubject();
// identify predicate
Property predicate = stmt.getPredicate();
// identify object
RDFNode object = stmt.getObject();
// output
System.out.print("("+predicate.toString()+",");
System.out.print(subject.toString()+",");
System.out.println(object.toString()+")");
}
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42. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 42
4.6. Jena â RDF in Java
4. RDF and RDF Schema
Jena â example: read and write a file
String filename = "myFile.rdf";
// creating a model
Model myModel = new ModelMem();
// read a file
Model.read(new FileReader(filename));
// write a file
Model.write(new PrintWriter(System.out));
Jena â example: creating a container
Bag bag = myModel.createBag();
bag.add("Romeo and Juliet")
bag.add("Hamlet")
bag.add("Othello");
NodeIterator iter = bag.iterator();
while (iter.hasNext()) {
System.out.println(" " + iter.next().toString());
}
4242
43. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 43
Creating the Semantic Web with RDF: Professional Developer's Guide
Johan Hjelm
4.7. References
4. RDF and RDF Schema
Practical RDF
Shelley Powers
4343
E-Librarian Service
User-Friendly Semantic Search in Digital Libraries
Serge Linckels, Christoph Meinel
44. Semantic Web ::: Serge Linckels ::: www.linckels.lu ::: serge@linckels.lu ::: 44
A Semantic Web Primer (Cooperative Information Systems)
Grigoris Antoniou , Frank van Harmelen
Semantic Web: Concepts, Technologies and Applications
K.K. Breitman, M.A. Casanova, W. Truszkowski
4.7. References
4. RDF and RDF Schema
44
Foundations of Semantic Web Technologies
Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph
44