“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications”
The document discusses Resource Description Framework (RDF), a W3C standard for describing web resources. RDF uses a graph-based data model consisting of subjects, predicates, and objects, known as triples. It provides a common framework for describing resources, along with their properties and relationships. RDF Schema builds upon RDF by defining additional vocabulary terms like class, subClassOf, and domain to organize RDF vocabularies and semantically relate terms. While useful, RDF Schema has limitations, leading to the development of OWL as a more expressive ontology language.
Semantic - Based Querying Using Ontology in Relational Database of Library Ma...dannyijwest
The traditional Web stores huge amount of data in the form of Relational Databases (RDB) as it is good at
storing objects and relationships between them. Relational Databases are dynamic in nature which allows
bringing tables together helping user to search for related material across multiple tables. RDB are
scalable to expand as the data grows. The RDB uses a Structured Query Language called SQL to access
the databases for several data retrieval purposes. As the world is moving today from the Syntactic form to
Semantic form and the Web is also taking its new form of Semantic Web. The Structured Query of the RDB
on web can be a Semantic Query on Semantic Web.
The document provides an overview of the semantic web including:
1. It describes the key technologies that power the semantic web such as RDF, RDFS, OWL, and SPARQL which allow data to be shared and reused across applications.
2. It discusses semantic web themes like linked data, vocabularies, and inference which enable data from multiple sources to be integrated and new insights to be discovered.
3. It outlines current and future applications of the semantic web such as in e-commerce, online advertising, and government where semantic technologies can enhance search, personalization and data sharing.
The document discusses the agenda for a presentation on the Semantic Web. The agenda includes an overview of the World Wide Web, an introduction to the Semantic Web, tools and applications for the Semantic Web, Linking Open Data, the Social Semantic Web, and Open Government. Each section provides details on the topic covered.
The document describes the development of a semantic web application called Music Event Explorer (meex) that will integrate data from multiple existing music-related data sources using semantic web technologies. It will allow users to explore music events related to artists and styles. The application will merge data about artists, music styles, and events from sources like MusicBrainz, MusicMoz, and EVDB into a unified RDF model using tools like RDF, OWL, and SPARQL. The development will follow good software engineering practices for a semantic web application.
The document discusses the Hypermedia Application Protocol (HAP) which defines a standard for REST APIs to be hypertext-driven. HAP uses representations that are maps containing data, links, queries, forms and other keys. It supports common operations like fetching resources, creating resources by filling out embedded forms, and updating and deleting resources. The hap-client library provides a Clojure client for easily performing these operations based on a service document returned from the API.
The document discusses Resource Description Framework (RDF), a W3C standard for describing web resources. RDF uses a graph-based data model consisting of subjects, predicates, and objects, known as triples. It provides a common framework for describing resources, along with their properties and relationships. RDF Schema builds upon RDF by defining additional vocabulary terms like class, subClassOf, and domain to organize RDF vocabularies and semantically relate terms. While useful, RDF Schema has limitations, leading to the development of OWL as a more expressive ontology language.
Semantic - Based Querying Using Ontology in Relational Database of Library Ma...dannyijwest
The traditional Web stores huge amount of data in the form of Relational Databases (RDB) as it is good at
storing objects and relationships between them. Relational Databases are dynamic in nature which allows
bringing tables together helping user to search for related material across multiple tables. RDB are
scalable to expand as the data grows. The RDB uses a Structured Query Language called SQL to access
the databases for several data retrieval purposes. As the world is moving today from the Syntactic form to
Semantic form and the Web is also taking its new form of Semantic Web. The Structured Query of the RDB
on web can be a Semantic Query on Semantic Web.
The document provides an overview of the semantic web including:
1. It describes the key technologies that power the semantic web such as RDF, RDFS, OWL, and SPARQL which allow data to be shared and reused across applications.
2. It discusses semantic web themes like linked data, vocabularies, and inference which enable data from multiple sources to be integrated and new insights to be discovered.
3. It outlines current and future applications of the semantic web such as in e-commerce, online advertising, and government where semantic technologies can enhance search, personalization and data sharing.
The document discusses the agenda for a presentation on the Semantic Web. The agenda includes an overview of the World Wide Web, an introduction to the Semantic Web, tools and applications for the Semantic Web, Linking Open Data, the Social Semantic Web, and Open Government. Each section provides details on the topic covered.
The document describes the development of a semantic web application called Music Event Explorer (meex) that will integrate data from multiple existing music-related data sources using semantic web technologies. It will allow users to explore music events related to artists and styles. The application will merge data about artists, music styles, and events from sources like MusicBrainz, MusicMoz, and EVDB into a unified RDF model using tools like RDF, OWL, and SPARQL. The development will follow good software engineering practices for a semantic web application.
The document discusses the Hypermedia Application Protocol (HAP) which defines a standard for REST APIs to be hypertext-driven. HAP uses representations that are maps containing data, links, queries, forms and other keys. It supports common operations like fetching resources, creating resources by filling out embedded forms, and updating and deleting resources. The hap-client library provides a Clojure client for easily performing these operations based on a service document returned from the API.
Introduction to linked data and the semantic webDave Reynolds
Linked data provides a method for publishing structured data on the web in a way that allows for integration and reuse across different data silos. It works by applying the principles of the web to data publishing - assigning URIs to identify things and using these URIs along with HTTP to make statements about those things in a structured format called RDF. This allows the data to be linked together into a global web of data. Key benefits include facilitating data integration, enabling extensibility and incremental updates, and supporting querying and reasoning over the integrated data. Challenges include the complexity of the technical stack and lack of familiarity with concepts like ontologies and logical reasoning.
This document provides an agenda and overview of semantic web and linked open data. It discusses the limitations of the current internet and the goals of the semantic web, which aims to make web content machine-readable through annotation and ontologies. It introduces key semantic web technologies like RDF, RDF schema, and OWL, and explains how they allow data to be interlinked and queried. Open linked data seeks to further evolve the web by linking data on the web through common vocabularies and enabling new types of browsers and search engines to utilize this semantic information.
The document discusses the concepts of semantic technology and the semantic web. It defines key concepts like tabula rasa, the network effect, and intelligence embedded in data through relationships. It also outlines technologies used in the semantic web like RDF, OWL, SPARQL, FOAF, and DBpedia and how search engines and companies are using these technologies for applications like sentiment analysis, natural language processing, and information extraction.
An Introduction to Semantic Web TechnologyAnkur Biswas
The document provides an overview of the semantic web and some of its key challenges. It discusses:
1) The evolution of the world wide web from a web of documents to a web of linked data through technologies like RDF, OWL, and SPARQL that add semantic meaning.
2) The vision for the semantic web is to publish machine-readable data using common formats so that information can be automatically processed by agents and integrated across sources.
3) Some challenges in realizing this vision include dealing with implicit knowledge, heterogeneous data distributions, and maintaining links and correctness over time as data changes.
1. Governments are releasing open data to increase transparency and stimulate applications, but data dumps have limitations like siloed data and static formats.
2. Linked data addresses this by using URIs and standards to integrate data on the public sector web. However, the data model and query methods are still barriers for most developers.
3. Middleware like a Linked Data API can provide a web-friendly interface to access linked data through RESTful APIs and standard formats like JSON, lowering the barrier to entry for developers while retaining the benefits of linked data.
s developing mash-ups with Web 2.0 really much easier than using Semantic Web technologies? For instance, given a music style as an input, what it takes to retrieve data from online music archives (MusicBrainz, MusicBrainz D2R Server, MusicMoz) and event databases (EVDB)? What to merge them and to let the users explore the results? Are Semantic Web technologies up to this Web 2.0 challenge? This half-day tutorial shows how to realize a Semantic Web Application we named Music Event Explorer or shortly meex (try it!).
Presentation on the Data Cube vocabulary to support Linked Data publication of statistics and measurement data sets. Given at SemTech 2011, San Francisco.
The document discusses how museums can better connect and share their data online by exposing their structured collection data through technologies like XML, RDF, and semantic standards. This will allow for aggregation of data across museums, new ways for users to access and reuse museum data, and more opportunities for machine-to-machine integration and connections between cultural heritage institutions. While the full vision of the "Semantic Web" may not yet be realized, making museum data available in open, structured, and standardized ways online can provide immediate benefits.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It introduces Uniform Resource Identifiers (URIs), Resource Description Framework (RDF), ontologies, SPARQL query language, and library projects applying these technologies like BIBFRAME, the Digital Public Library of America, and Europeana. The goal is to connect structured data on the web through shared vocabularies and relationships between resources from different sources.
Subject information gateway in information technology (sigit) an introductionkmusthu
1. Subject Information Gateway in Information Technology (SIGIT) is a subject gateway created by Aligarh Muslim University to provide easy access to quality information resources on the Internet related to information technology.
2. SIGIT aims to meet the information needs of the scientific and academic community working in the field of IT. It provides links to e-journals, databases, bibliographies and other resources.
3. The project was funded by the Department of Science and Technology, Government of India and is maintained by the Department of Library and Information Science at Aligarh Muslim University.
The document discusses designing a library portal website. It outlines the objectives of a library website such as promoting library use and providing access to information and resources. It also describes the basic terminology used in website design, development stages, and provides examples of case studies and a live demonstration of designing a model library website.
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
The document discusses several APIs that can be used to access semantic information from the Semantic Web. It describes APIs that produce semantic information from text or URLs, such as the TextWise Semantic Hacker API and Open Calais Semantic Proxy. It also describes APIs that deliver existing semantic information, such as Sindice for entity lookup and semantic search, and Watson as a gateway for finding, exploring and exploiting semantic web content. Examples of applications that use these APIs are provided.
An informational database or webpage contains factual information intended to educate readers. It is created by authorities on the topic who strive for accuracy, objectivity, and currency. Libraries and information centers play an important role by creating informational web pages and databases to disseminate relevant information to their audiences in a way that saves time and money compared to print or paid online resources. The goal is to provide easy access to factual information on various topics.
The document discusses the evolution of the web from a web of linked documents to a web of linked data. It explains that the data web uses the Resource Description Framework (RDF) to create custom link types between data in triples. By linking open government data, agencies can automatically infer relationships and integrate disparate data sources. When combined with social aspects, the social data web allows collaboration to enhance data quality.
Linked Open Data Libraries Archives Museums. This presentation is a basic overview of what LOD is and what technologies are needed to ensure the metadata around your collections is machine readable.
The document discusses the Semantic Web and Linked Data. It provides an overview of key concepts like URIs, RDF, and standardized formats for representing semantic data like Turtle and JSON-LD. It also provides examples of representing personal profile information about individuals using these technologies and linking the data together.
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/
Introduction to linked data and the semantic webDave Reynolds
Linked data provides a method for publishing structured data on the web in a way that allows for integration and reuse across different data silos. It works by applying the principles of the web to data publishing - assigning URIs to identify things and using these URIs along with HTTP to make statements about those things in a structured format called RDF. This allows the data to be linked together into a global web of data. Key benefits include facilitating data integration, enabling extensibility and incremental updates, and supporting querying and reasoning over the integrated data. Challenges include the complexity of the technical stack and lack of familiarity with concepts like ontologies and logical reasoning.
This document provides an agenda and overview of semantic web and linked open data. It discusses the limitations of the current internet and the goals of the semantic web, which aims to make web content machine-readable through annotation and ontologies. It introduces key semantic web technologies like RDF, RDF schema, and OWL, and explains how they allow data to be interlinked and queried. Open linked data seeks to further evolve the web by linking data on the web through common vocabularies and enabling new types of browsers and search engines to utilize this semantic information.
The document discusses the concepts of semantic technology and the semantic web. It defines key concepts like tabula rasa, the network effect, and intelligence embedded in data through relationships. It also outlines technologies used in the semantic web like RDF, OWL, SPARQL, FOAF, and DBpedia and how search engines and companies are using these technologies for applications like sentiment analysis, natural language processing, and information extraction.
An Introduction to Semantic Web TechnologyAnkur Biswas
The document provides an overview of the semantic web and some of its key challenges. It discusses:
1) The evolution of the world wide web from a web of documents to a web of linked data through technologies like RDF, OWL, and SPARQL that add semantic meaning.
2) The vision for the semantic web is to publish machine-readable data using common formats so that information can be automatically processed by agents and integrated across sources.
3) Some challenges in realizing this vision include dealing with implicit knowledge, heterogeneous data distributions, and maintaining links and correctness over time as data changes.
1. Governments are releasing open data to increase transparency and stimulate applications, but data dumps have limitations like siloed data and static formats.
2. Linked data addresses this by using URIs and standards to integrate data on the public sector web. However, the data model and query methods are still barriers for most developers.
3. Middleware like a Linked Data API can provide a web-friendly interface to access linked data through RESTful APIs and standard formats like JSON, lowering the barrier to entry for developers while retaining the benefits of linked data.
s developing mash-ups with Web 2.0 really much easier than using Semantic Web technologies? For instance, given a music style as an input, what it takes to retrieve data from online music archives (MusicBrainz, MusicBrainz D2R Server, MusicMoz) and event databases (EVDB)? What to merge them and to let the users explore the results? Are Semantic Web technologies up to this Web 2.0 challenge? This half-day tutorial shows how to realize a Semantic Web Application we named Music Event Explorer or shortly meex (try it!).
Presentation on the Data Cube vocabulary to support Linked Data publication of statistics and measurement data sets. Given at SemTech 2011, San Francisco.
The document discusses how museums can better connect and share their data online by exposing their structured collection data through technologies like XML, RDF, and semantic standards. This will allow for aggregation of data across museums, new ways for users to access and reuse museum data, and more opportunities for machine-to-machine integration and connections between cultural heritage institutions. While the full vision of the "Semantic Web" may not yet be realized, making museum data available in open, structured, and standardized ways online can provide immediate benefits.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It introduces Uniform Resource Identifiers (URIs), Resource Description Framework (RDF), ontologies, SPARQL query language, and library projects applying these technologies like BIBFRAME, the Digital Public Library of America, and Europeana. The goal is to connect structured data on the web through shared vocabularies and relationships between resources from different sources.
Subject information gateway in information technology (sigit) an introductionkmusthu
1. Subject Information Gateway in Information Technology (SIGIT) is a subject gateway created by Aligarh Muslim University to provide easy access to quality information resources on the Internet related to information technology.
2. SIGIT aims to meet the information needs of the scientific and academic community working in the field of IT. It provides links to e-journals, databases, bibliographies and other resources.
3. The project was funded by the Department of Science and Technology, Government of India and is maintained by the Department of Library and Information Science at Aligarh Muslim University.
The document discusses designing a library portal website. It outlines the objectives of a library website such as promoting library use and providing access to information and resources. It also describes the basic terminology used in website design, development stages, and provides examples of case studies and a live demonstration of designing a model library website.
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
The document discusses several APIs that can be used to access semantic information from the Semantic Web. It describes APIs that produce semantic information from text or URLs, such as the TextWise Semantic Hacker API and Open Calais Semantic Proxy. It also describes APIs that deliver existing semantic information, such as Sindice for entity lookup and semantic search, and Watson as a gateway for finding, exploring and exploiting semantic web content. Examples of applications that use these APIs are provided.
An informational database or webpage contains factual information intended to educate readers. It is created by authorities on the topic who strive for accuracy, objectivity, and currency. Libraries and information centers play an important role by creating informational web pages and databases to disseminate relevant information to their audiences in a way that saves time and money compared to print or paid online resources. The goal is to provide easy access to factual information on various topics.
The document discusses the evolution of the web from a web of linked documents to a web of linked data. It explains that the data web uses the Resource Description Framework (RDF) to create custom link types between data in triples. By linking open government data, agencies can automatically infer relationships and integrate disparate data sources. When combined with social aspects, the social data web allows collaboration to enhance data quality.
Linked Open Data Libraries Archives Museums. This presentation is a basic overview of what LOD is and what technologies are needed to ensure the metadata around your collections is machine readable.
The document discusses the Semantic Web and Linked Data. It provides an overview of key concepts like URIs, RDF, and standardized formats for representing semantic data like Turtle and JSON-LD. It also provides examples of representing personal profile information about individuals using these technologies and linking the data together.
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/
The document provides an overview of the Resource Description Framework (RDF). It describes RDF as a standard for describing web resources using metadata. RDF uses a simple data model based on making statements about resources in the form of subject-predicate-object expressions. This allows data to be shared across different applications. The document discusses key RDF concepts including resources, properties, and statements. It provides examples of RDF statements and illustrates the RDF triple format. The goal of RDF is to enable the encoding, exchange, and reuse of structured metadata about Web resources between applications.
Text Analysis and Semantic Search with GATEDiana Maynard
The document provides an outline for a tutorial on text analysis using GATE (General Architecture for Text Engineering). It discusses natural language processing (NLP) and information extraction, and provides an introduction to GATE, including its components, the ANNIE information extraction system, and applications of NLP techniques like entity recognition, relation extraction, and event recognition.
A First Course in RDF and RDFS (Resource Description Framework and Resource D...Mark Birbeck
This tutorial was given at SemTech 2008, on May 19th.
From the program: "RDF is a key W3C specification and a foundational component of the Semantic Web. This tutorial will explain the basics of RDF and how it functions as a key building block of semantic systems.
Mark Birbeck is the managing director of webBackplane. He has been creating software for many years, and his particular interests are the semantic web, and components that help to create dynamic, flexible, user interfaces. He has consulted, given training, spoken at conferences and contributed to books and articles on these and other topics. He is also heavily involved in the creation of new standards on these themes. Mark is an Invited Expert on both the XForms Working Group and the XHTML 2 Working Group, at the W3C. Over the years his work there has included devising and proposing RDFa."
Text analysis and Semantic Search with GATEDiana Maynard
This document provides an outline for a tutorial on text analysis with GATE (General Architecture for Text Engineering). The tutorial covers topics such as natural language processing, information extraction, social media analysis, semantic search, semantic annotation, and example applications that use GATE like news analysis and patent analysis. It also discusses NLP components for text mining like entity recognition, relation extraction, event recognition, and summarization. Finally, it introduces GATE as an NLP toolkit, its main components, and its built-in information extraction system called ANNIE.
Do you want to upgrade your GWT application or write a sizable web application? Dart is the efficient choice.
As a brief example, check out http://lightningdart.com
This presentation is updated October 2015 for Silicon Valley Code Camp
Tutoriel de présentation de RDF (Resource Description Framework ou modèle informatique de description de ressources) et du web sémantique, avec un exemple concret.
Réalisé dans le cadre d'une introduction à SOA (Service-oriented architecture ou architecture orientée services), prochaine génération.
The document discusses semantic web technology, which aims to make information on the web better understood by machines by giving data well-defined meaning. It outlines the evolution of web technologies from the initial web to the semantic web. Key aspects of semantic web technology include ontologies to define common vocabularies, semantic annotations to associate meaning with data, and reasoning capabilities to enable complex queries and analyses. Languages, tools, and applications are needed to implement these semantic web standards and make the web of linked data usable.
This document introduces the Semantic Web. It defines the Semantic Web as using standards to allow machines to understand web data and extending the capabilities of the existing web. It describes some of the key components of the Semantic Web, including URIs to identify resources, RDF to represent relationships between resources as subject-predicate-object triples, and OWL to define complex rules and constraints. The document provides an overview of the basics of representing semantic data using RDF and RDF Schema.
Introduction to semantic web. Includes its goal, features, why we need, semantic web related framework, RDF's, Advantages, Uniform resource locator, web ontology language, micro-formats.
IRJET- Semantic Web Mining and Semantic Search Engine: A ReviewIRJET Journal
This document provides an overview of the semantic web, semantic web mining, and semantic search engines. It discusses how the semantic web aims to make web data machine-readable through technologies like RDF and ontology. Semantic web mining involves extracting useful knowledge from the semantic web. Semantic search engines then allow users to retrieve more precise and meaningful data from the semantic web through the use of semantic technologies. The document outlines challenges for semantic search engines and opportunities for further research.
The document discusses using Web Oriented Architecture (WOA) principles and technologies to improve transparency, collaboration, and information sharing through publishing and linking government data on the Web. It describes exposing raw data and semantically enriched structured data as public records. Technologies that enable interoperability across disparate data sources for large-scale data federation are also described. Finally, the applicability of the proposed solution architecture to existing frameworks is discussed.
This presentation is the culmination of my detail to the E-Government Office in the US Office of Management and Budget and the work I did to evolve and mature initiatives like recovery.gov and data.gov.
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.
Linked Data, the Semantic Web, and You discusses key concepts related to Linked Data and the Semantic Web. It defines Linked Data as a set of best practices for publishing and connecting structured data on the web using URIs, HTTP, RDF, and other standards. It also explains semantic web technologies like RDF, ontologies, SKOS, and SPARQL that enable representing and querying structured data on the web. Finally, it discusses how libraries are applying these concepts through projects like BIBFRAME, FAST, library linked data platforms, and the LD4L project to represent bibliographic data as linked open data.
The document provides an agenda for a presentation that includes discussions of CRUD, REST, Spring, HATEOAS, microservices and a hands-on portion. It defines CRUD as the basic functions of persistent storage - create, read, update and delete. REST is described as an architectural style for distributed hypermedia systems utilizing resources, uniform interfaces and hypertext. Spring is summarized as an open source framework and inversion of control container. HATEOAS constrains REST applications to include resource state in hypermedia links. Microservices are defined as independently deployable services that compose applications. The document concludes by stating there will be a hands-on portion.
This document provides an overview of tools for developing applications using Resource Description Framework (RDF) and Topic Maps technologies. It classifies these tools into three categories: storage, editing, and visualization. The document aims to compare these tools on various parameters to help researchers and users select the most appropriate one for their needs. It argues that while these technologies can enrich web content with semantic information, RDF and Topic Maps differ in their approaches and architectures, which can hinder interoperability. The comparison of tools presented in this paper seeks to address the interoperability problem between the two technologies and provide insight into how their tools can be used together.
The document discusses RESTful web services and Resource Oriented Architecture (ROA). It defines REST and its constraints including client-server architecture, statelessness, cacheability, uniform interface and layered system. It then explains ROA and how resources are addressed and represented in a RESTful system. Key concepts like addressability, statelessness, representations, links and connectedness, uniform interface and safety are covered in relation to ROA. The document provides examples and discusses designing a RESTful API for a content management system.
Nelson Piedra , Janneth Chicaiza
and Jorge López, Universidad Técnica Particular de Loja, Edmundo
Tovar, Universidad Politécnica de Madrid,
and Oscar Martínez, Universitas
Miguel Hernández
Explore the advantages of using linked data with OERs.
ith the spread of online banking, increasing competition has elevated the need for providing excellent customer service in the Banking and Insurance sector. Digital also offers insurers new ways to cut costs and an opportunity to bring real additional value to the customer experience.
Semantic web technologies and applications provide the
emantic web technologies and applications for InsTemesgenHabtamu
ith the spread of online banking, increasing competition has elevated the need for providing excellent customer service in the Banking and Insurance sector. Digital also offers insurers new ways to cut costs and an opportunity to bring real additional value to the customer experience.
Semantic web technologies and applications provide the emantic web technologies and applications for Insemantic web technologies and applications for Ins
Semantic Web: Technolgies and Applications for Real-WorldAmit Sheth
Amit Sheth and Susie Stephens, "Semantic Web: Technolgies and Applications for Real-World," Tutorial at 2007 World Wide Web Conference, Banff, Canada.
Tutorial discusses technologies and deployed real-world applications through 2007.
Tutorial description at: http://www2007.org/tutorial-T11.php
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
Presentation about Semantic MediaWiki and Semantic Forms given by Sergey Chernyshev and Yaron Koren at "Semantic Wikis" (March 2008 NY SemWeb Meetup) on March 13, 2008
This document provides an overview of semantic web technologies and their role in advancing digital library functions. It begins with definitions of semantic web, its main components like RDF, OWL and ontology. It then discusses functions of digital libraries like access to information, support for multimedia and advanced search. The key role of semantic web for digital libraries is described as representing various types of objects and relations between them to provide meaningful data that can be processed by computers. Technologies like XML, RDF and ontology help achieve this by annotating and describing data in a structured format. Finally, semantic web allows for improved resource sharing and networking across digital libraries by making data interoperable and extending its accessibility.
This document provides an overview of the Semantic Web and related technologies. It defines key concepts like the Semantic Web, ontology, RDF, URIs and describes how they are used to represent data on the web in a structured format. It also discusses technologies and standards used for linking open data on the web, such as the Linking Open Data project, RDFa and Schema.org. The goal of these technologies is to publish structured data on the web that can be interconnected and processed by machines to build a global data space.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Malibou Pitch Deck For Its €3M Seed Roundsjcobrien
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
Project Management: The Role of Project Dashboards.pdfKarya Keeper
Project management is a crucial aspect of any organization, ensuring that projects are completed efficiently and effectively. One of the key tools used in project management is the project dashboard, which provides a comprehensive view of project progress and performance. In this article, we will explore the role of project dashboards in project management, highlighting their key features and benefits.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...kalichargn70th171
In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
Liberarsi dai framework con i Web Component.pptxMassimo Artizzu
In Italian
Presentazione sulle feature e l'utilizzo dei Web Component nell sviluppo di pagine e applicazioni web. Racconto delle ragioni storiche dell'avvento dei Web Component. Evidenziazione dei vantaggi e delle sfide poste, indicazione delle best practices, con particolare accento sulla possibilità di usare web component per facilitare la migrazione delle proprie applicazioni verso nuovi stack tecnologici.
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Microservice Teams - How the cloud changes the way we work
Semantic web
1. SSeemmaannttiicc WWeebb::
SSttaattee ooff tthhee AArrtt aanndd OOppppoorrttuunniittiieess
Aatif Hussain Warraich
Compiled, partly based on various online tutorials
and presentations, with respect to their authors
University of Engineering and Technology, Lahore
2. Where we are Today:
the Syntactic Web
[Hendler & Miller 02]
5. AApppprrooaacchh:: SSeemmaannttiicc WWeebb
“The Semantic Web is a vision: the idea of having data on the
Web defined and linked in a way that it can be used by
machines not just for display purposes,
but for automation, integration and reuse
of data across various applications”
http://www.w3.org/sw/
The Semantic Web is an initiative with the goal of extending the
current Web and facilitating Web automation, universally accessible
web resources, and the 'Web of Trust', providing a universally
accessible platform that allows data to be shared and processed by
automated tools as well as by people.
8. SSeemmaannttiicc WWeebb:: NNeeww ““UUsseerrss””
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
9. Semantic Web: Annotations
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
Semantic annotations are
specific sort of metadata,
which provides information
about particular domain
objects, values of their
properties and relationships,
in a machine-processable,
formal and standardized way.
10. Semantic Web: Ontologies
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
Ontologies make metadata
interoperable and ready for
efficient sharing and reuse. It
provides shared and common
understanding of a domain, that
can be used both by people and
machines. Ontologies are used as
a form of agreement-based
knowledge representation about
the world or some part of it and
generally describe: domain
individuals, classes, attributes,
relations and events.
11. Semantic Web: Rules
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
Logical support in form of rules is needed to
infer implicit content, metadata and ontologies
from the explicit ones. Rules are considered to
be a major issue in the further development of
the semantic web. On one hand, they can be
used in ontology languages, in conjunction with
or as an alternative to description logics. And on
the other hand, they will act as a means to draw
inferences, to configure systems, to express
constraints, to specify policies, to react to
events/changes, to transform data, to specify
behavior of agents, etc.
12. Semantic Web: Languages
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
Languages are needed for machine-processable
formal descriptions of: metadata (annotations) like
e.g. RDF; ontologies like e.g. OWL.; rules like e.g.
RuleML. The challenge is to provide a framework for
specifying the syntax (e.g. XML) and semantics of
all of these languages in a uniform and coherent
way. The strategy is to translate the various
languages into a common 'base' language (e.g. CL
or Lbase) providing them with a single coherent
model theory.
13. Semantic Web: Tools
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
User-friendly tools are needed for
metadata manual creation (annotating
content) or automated generation, for
ontology engineering and validation,
for knowledge acquisition (rules), for
languages parsing and processing,
etc.
14. Semantic Web: Applications and Services
Semantic Users
Web and
Beyond
Semantic
Annotations Ontologies Logical Support
Languages Tools Applications /
Services
WWW Creators Users
and
Beyond
Web content
Semantic
Web
Semantic Web
content
Creators
applications
agents
Utilization of Semantic Web
metadata, ontologies, rules,
languages and tools enables to
provide scalable Web applications
and Web services for consumers
and enterprises" making the web
'smarter' for people and machines.
15. The Semantic Web
The Ontology Articulation
Toolkit helps agents to
understand unknown ontologies
16. Semantic Web basics…
RDF:
• is a W3C standard, which provides tool to describe Web
resources
• provides interoperability between applications that
exchange machine-understandable information
RDF Schema:
– is a W3C standard which defines vocabulary for RDF
– organizes this vocabulary in a typed hierarchy
– capable to explicitly declare semantic relations between
vocabulary terms
17. RDF – Semantic Web over Web Resources
Mary
Director
Secretary
has_job
to_be_in_
love_with
has_job
John
has_homepage
has_homepage
OOnnttoollooggyy
18. Resources
• All things being described by RDF
expressions are called resources:
– entire Web page;
– a specific XML element;
– whole collection of pages;
– an object that is not directly accessible via the
Web.
20. Semantic Relation as RDF statement
(so called “object property”)
Personal web page of Terziyan V. Web page of Agora Center
27
Lk
Ai Aj
Relation (i ¹ j)
http://www.cs.jyu.fi/ai/vagan/index.html http://www.jyu.fi/agora-center/indexEng.html
refers_to
Resource
Relation
Resource
Subject Predicate object
URI of Terziyan V. employed_by
http://www.cs.jyu.fi/ai/vagan/#vagan
URI of Agora Center
http://www.jyu.fi/agora-center/#AC
Dereferenceable URI (“Hash vs. Slash”)
21. Semantic Property as RDF statement
(so called “datatype property”)
28
Personal web page of Terziyan V.
http://www.cs.jyu.fi/ai/vagan/index.html
Literal
Literal
Resource
has_birthday
Property
Subject Predicate object
Ai
Lk
Property (i = j)
15.02.2000
“Birthday” of the web-page
URI of Terziyan V. has_birthday
http://www.cs.jyu.fi/ai/vagan/#vagan
27.12.1958
Dereferenceable URI (“Hash vs. Slash”)
Birthday of Terziyan V.
22. Semantic Network of Web Resources
15.02.2000
has_birthday
Personal web page of Terziyan V. Web page of Agora Center
http://www.cs.jyu.fi/ai/vagan/index.html http://www.jyu.fi/agora-center/indexEng.html
29
refers_to
isWebPageOf
27.12.1958 isWebPageOf
URI of Terziyan V. employed_by URI of Agora Center
http://www.cs.jyu.fi/ai/vagan/#vagan
http://www.jyu.fi/agora-center/#AC
hasWebPage
hasWebPage
has_birthday
23. http://www.kture.kharkov.ua/
30
From Hyperlinks to Semantic Web
http://www.cs.jyu.fi/ai/ university
international
_contacts
http://www.cs.jyu.fi/ai/contacts.html
24. Resources and URIs
• A resource can be anything that has identity
• Uniform Resource Identifiers (URI)* provide
a simple and extensible means for identifying
a resource
• Not all resources are network "retrievable";
e.g., human beings, corporations, and books
in a library can also be considered resources
* The term "Uniform Resource Locator" (URL) refers to the subset of URI that identify
resources via a representation of their primary access mechanism (e.g., their network "location"),
rather than identifying the resource by name or by some other attribute(s) of that resource.
25. URI vs. URL
A URI represents a single concept or thing,
but many URIs can represent the same thing.
All URLs are URIs. Not all URIs are URLs.
URI identifies something uniquely.
URL not only identifies something, but also
describes where it is located.
Venn diagram of Uniform Resource Identifier (URI) scheme categories.
Schemes in the URL (locator) and URN (name) categories both
function as resource IDs, so URL and URN are subsets of URI. They
are also, generally, disjoint sets. However, many schemes can't be
categorized as strictly one or the other, because all URIs can be
treated as names, and some schemes embody aspects of both
categories – or neither.
26. URL vs. URI example
URI of Vagan Terziyan in Nordea Database:
http://www.NordeaDB.fi/#VaganTerziyan
URI of Vagan Terziyan in the Central Hospital
Database:
http://www.JKL_Central_Hospital_DB.fi/#VaganTerziyan
URL of Vagan Terziyan record in Nordea Database:
http://www.NordeaDB.fi/VaganTerziyanRecord
URL of Vagan Terziyan record in the Central Hospital Database:
http://www.JKL_Central_Hospital_DB.fi/VaganTerziyanRecord
?
27. Dereferenceable URI
The term Linked Data is used to describe a method of exposing, sharing,
and connecting data via dereferenceable* URIs on the Web.
Linked Data is about using the Web to connect related data that wasn’t
previously linked, or using the Web to lower the barriers to linking data
currently linked using other methods. More specifically, Wikipedia defines
Linked Data as a term used to describe a recommended best practice for
exposing, sharing, and connecting pieces of data, information, and knowledge
on the Semantic Web using URIs and RDF. Linked Data aims to extend the
Web with a data commons by publishing various open datasets as RDF on the
Web and by setting RDF links between data items from different data sources.
*A dereferenceable Uniform Resource Identifier or dereferenceable URI
is a resource retrieval mechanism that uses any of the internet protocols (e.g.
HTTP) to obtain a copy or representation of the resource it identifies. In the
context of traditional HTML web pages, this is the normal and obvious way of
working: A URI refers to the page, and when requested the web server
returns a copy of it. In other non-dereferenceable contexts, such as XML
Schema, the namespace identifier is still a URI, but this is simply an identifier
(i.e. a namespace name). There is no intention that this can or should be
dereferenced. There is even a separate attribute, schemaLocation, which
may contain a dereferenceable URI that does point to a copy of the schema
document. In the case of Linked Data, the representation takes the form of a
document (typically HTML or XML) that describes the resource that the URI
identifies. In either case, the mechanism makes it possible for a user (or
software agent) to "follow your nose" to find out more information related to
the identified resource.
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
28. Relationships between URIs illustrated
The picture shows a Big Lynx film team at work. Within the picture, Big Lynx Lead Cameraman Matt
Briggs as well as his two assistants, Linda Meyer and Scott Miller, are identified by HTTP URIs from
the Big Lynx namespace. The relationship, that they know each other, is represented by connecting
lines having the relationship type http://xmlns.com/foaf/0.1/knows.
[source: http://linkeddatabook.com/editions/1.0/ ]
29. RDF Statement
• Subject of an RDF statement is a
resource
• Predicate of an RDF statement is a
property of a resource
• Object of an RDF statement is the value
of a property of a resource
30. Example of RDF Statement
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
Subject (resource) http://www.w3.org/Home/Lassila
Predicate (property) Creator
Object (literal) “Ora Lassila”
Lets represent the above statement in RDF/XML in accordance with the
“serialization” and the “abbreviated” syntax (see next slides):
31. RDF Example in serialization syntax
(subject of statement)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:RDF>
<rdf:Description rdf:about=
"http://www.w3.org/Home/Lassila">
<s:Creator>Ora Lassila</s:Creator>
</rdf:Description>
</rdf:RDF>
Subject
32. RDF Example in serialization syntax
(predicate of statement)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:RDF>
<rdf:Description rdf:about=
"http://www.w3.org/Home/Lassila">
<s:Creator>Ora Lassila</s:Creator>
</rdf:Description>
</rdf:RDF>
Predicate
33. RDF Example in serialization syntax
(object of statement)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:RDF>
<rdf:Description rdf:about=
"http://www.w3.org/Home/Lassila">
<s:Creator>Ora Lassila</s:Creator>
</rdf:Description>
</rdf:RDF>
Object
34. RDF Example in serialization syntax
(reference to ontology)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:RDF>
<rdf:Description rdf:about=
"http://www.w3.org/Home/Lassila">
<s:Creator>Ora Lassila</s:Creator>
</rdf:Description>
</rdf:RDF> a specific namespace prefix as reference to
ontology where predicates are defined, e.g.
xmlns: s="http://description.org/schema/"
35. Full XML Document in serialization syntax
for the Example
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-
rdf-syntax-ns#"
xmlns:s="http://description.org/schema/"
>
<rdf:Description rdf:about=
"http://www.w3.org/Home/Lassila">
<s:Creator>Ora Lassila</s:Creator>
</rdf:Description>
</rdf:RDF>
Namespaces
36. Template/frame for RDF/XML statement (1)
Subject (URI)
p Ontology (URI)
Value
p Predicat p
e
Predicat
e
Value
Predicate
Subject
37. Template/frame for RDF/XML statement (2)
p Ontology (URI)
p
Subject (URI)
Object (URI)
Predicate
Subject Object
39. Abbreviated Syntax Example (1)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:RDF>
<rdf:Description rdf:about="http://www.w3.org/Home/Lassila"
s:Creator="Ora Lassila" />
</rdf:RDF>
<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:s="http://description.org/schema/">
<rdf:Description rdf:about="http://www.w3.org/Home/Lassila"
s:Creator="Ora Lassila"/>
</rdf:RDF>
40. Abbreviated Syntax Example (2)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila. Subject
<rdf:Description rdf:about="http://www.w3.org/Home/Lassila"
s:Creator="Ora Lassila" />
41. Abbreviated Syntax Example (3)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:Description rdf:about="http://www.w3.org/Home/Lassila"
s:Creator ="Ora Lassila" />
Predicate
42. Abbreviated Syntax Example (4)
Ora Lassila is the creator of the resource
http://www.w3.org/Home/Lassila.
<rdf:Description rdf:about="http://www.w3.org/Home/Lassila"
s:Creator="Ora Lassila" />
Object
43. Template/frame for RDF statement
<?xml version="1.0"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns: = " " >
<rdf:Description rdf:about= " “ "
: = " " />
</rdf:RDF>
Ontology (URI)
Predicat Value
e
prefix
Subject
(URI)
prefi
x
Value
Predicate
Subject
44. RDF N3 syntax
• Notation3, or N3 as it is more commonly known, is a
shorthand non-XML serialization of RDF models,
designed with human-readability in mind: N3 is much
more compact and readable than XML RDF notation.
The format is being developed by Tim Berners-Lee
and others from the Semantic Web community.
RDF sample in
XML notation
RDF sample
in N3 notation
http://www.w3.org/TeamSubmission/n3/
45. RDF N3 syntax
Some fast hints:
All URIs are quoted with angle brackets ( “<“ and “>” ).
Whitespace within the < > is to be ignored.
All values are within " " .
Each statement ends with “.”
@pref is used to define the namespace prefix.
[ ] are used for the “blank nodes”.
Check all in : http://www.w3.org/TeamSubmission/n3/
Shorthand:
Example:
@PREFIX dc: <http://purl.org/dc/elements/1.1/> .
<http://en.wikipedia.org/wiki/Tony_Benn>
dc:publisher
"Wikipedia" .
Square brackets for blank nodes:
[ pl ] means x, where there exists some x such that x has properties in the property list pl. For example:
[ :firstname "Ora" ] dc:wrote [ dc:title "Moby Dick" ] .
… is a statement which would be means in math:
exists x, y . firstname(x, "Ora") & dc:wrote(x,y) & dc:title (y, "Moby Dick")
… or in English "Some person who has a first name Ora wrote a book entitled "Moby Dick". Note not
"the book" or "the person".
46. Template/frame for RDF statement (N3)
Predicate
Subject Object
Predicate
OR
Subject Value
Subject
< (URI) > < Predicate (URI) > < Object (URI) > .
OR
Subject
(URI) < > < Predicate (URI) > " Value " .
OR
@prefix Onto:
< Ontology (URI) > .
Onto : Subject Onto : Predicate Onto : Object .
Onto : Subject Onto : Predicate " Value " .
47. Some N3 syntax specifics
http://www.w3.org/TeamSubmission/n3/
Ontological statements in N3
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
:Professor a rdfs:Class .
<http://www.cs.jyu.fi/ai/vagan> a :Professor .
48. RDF N3 examples
The story 4 RDF (N3) statements
1. Gender of Vagan Terziyan is male.
2. His mother is Svetlana Terziyan.
3. He is fun of fishing.
4. He is fun of Nordic walking.
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasGender >
"male" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasMother>
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#SvetlanaTerziyan
> .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"Fishing" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"NordicWalking" .
49. RDF N3 examples
1. Gender of Vagan Terziyan is male.
2. His mother is Svetlana Terziyan.
3. He is fun of fishing.
4. He is fun of Nordic walking.
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasGender >
"male" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasMother>
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#SvetlanaTerziyan
> .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"Fishing" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"NordicWalking" .
Reader-friendly version
@prefix On1: <http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#> .
@prefix On2: <http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#> .
On1:VaganTerziyan On1:hasGender "male" .
On1:VaganTerziyan On1:hasMother On1:SvetlanaTerziyan .
On1:VaganTerziyan On2:isFunOf "Fishing" .
On1:VaganTerziyan On2:isFunOf "NordicWalking" .
50. RDF N3 examples
1. Gender of Vagan Terziyan is male.
2. His mother is Svetlana Terziyan.
3. He is fun of fishing.
4. He is fun of Nordic walking.
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasGender >
"male" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasMother>
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#SvetlanaTerziyan
> .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"Fishing" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"NordicWalking" .
Compact version
@prefix On1: <http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#> .
@prefix On2: <http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#> .
On1:VaganTerziyan On1:hasGender "male" ; On1:hasMother On1:SvetlanaTerziyan ;
On2:isFunOf "Fishing" , "NordicWalking" .
54. RDF/N3 example (blank node with [ ])
1. Gender of Vagan Terziyan is male.
2. His mother is Svetlana Terziyan.
3. He is fun of fishing.
4. He is fun of Nordic walking.
5. He is fun of mother’s hobby.
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasGender >
"male" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#hasMother>
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#SvetlanaTerziyan
> .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"Fishing" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
"NordicWalking" .
<http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#VaganTerziyan> <
http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isFunOf>
[<http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#isHobbyOf> <
http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#SvetlanaTerziyan>].
Compact version
@prefix On1: <http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#> .
@prefix On2: <http://www.cs.jyu.fi/ai/vagan/ontologies/LifeInFinland.owl#> .
On1:VaganTerziyan On1:hasGender "male" ; On1:hasMother On1:SvetlanaTerziyan ;
On2:isFunOf "Fishing" , "NordicWalking" , [On2:isHobbyOf On1:SvetlanaTerziyan ] .
55. … lets translate it to RDF/XML …
http://rdf-translator.appspot.com/
56. … and check with RDF/XML validator
http://www.w3.org/RDF/Validator/
57. What is an ontology?
Studer(98): Formal, explicit specification of a shared conceptualization
Machine
readable
Concepts, properties,
functions, axioms
are explicitly defined
Consensual
knowledge
Abstract model of
some domain
58. 66
What is an Ontology?
A model of (some aspect of) the world
• Introduces vocabulary
relevant to domain, e.g.:
– Anatomy
From: Ian Horrocks “OWL 2:
The Next Generation”
59. 67
What is an Ontology?
A model of (some aspect of) the world
• Introduces vocabulary
relevant to domain
• Specifies meaning of terms
Heart is a muscular organ that
is part of the circulatory system
From: Ian Horrocks “OWL 2:
The Next Generation”
60. 68
What is an Ontology?
A model of (some aspect of) the world
• Introduces vocabulary
relevant to domain
• Specifies meaning of terms
Heart is a muscular organ that
is part of the circulatory system
• Formalised using suitable logic
From: Ian Horrocks “OWL 2:
The Next Generation”
61. Ontology Elements
• Concepts(classes) + their hierarchy
• Concept properties (slots/attributes) + their hierarchy
• Property restrictions (type, cardinality, domain …)
• Relations between concepts (disjoint, equality …)
• Instances
62. 70
DL Semantics
Semantics given by standard FO model theory:
John
Mary
Lawyer
Doctor
Vehicle
hasChild
owns
(Lawyer and Doctor)
From: Ian Horrocks “OWL: A
Description Logic Based
Ontology Language”
63. RDF Schema (language for simple ontologies)
RDF schema is a semantic extension of RDF used for simple ontologies’ design.
The RDF schema language is used for declaring basic class and types when
describing the terms used in RDF and are used to determine characteristics of
other resources, such as the domains and ranges of properties.
• rdfs:Resource
• rdfs:Class
• rdfs:domain
• rdfs:range
• rdfs:subClassOf
• rdfs:subPropertyOf
• …
RDF Schema provides a data-modelling
vocabulary for RDF data.
http://www.w3.org/TR/rdf-schema/
The RDF Schema class and property
system is similar to the type systems of
object-oriented programming
languages such as Java. RDF Schema
differs from many such systems in that
instead of defining a class in terms of
the properties its instances may have,
RDF Schema describes properties in
terms of the classes of resource to
The namespace is identified by: which they apply.
http://www.w3.org/2000/01/rdf-schema#
74. Dublin Core
• A set of fifteen basic properties for describing
generalised Web resources;
• ISO Standard 15836-2003 (February 2003) and
ANSI/NISO Z39.85-2012 (February 2013) :
http://www.niso.org/apps/group_public/download.php/10
256/Z39-85-2012_dublin_core.pdf
The Dublin Core Metadata Initiative is an open forum engaged in the development of interoperable online
metadata standards that support a broad range of purposes and business models.
Namespace:
@prefix dc: http://purl.org/dc/elements/1.0/ .
http://dublincore.org/
76. Dublin Core Example (RDF/XML)
<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:dc="http://purl.org/dc/elements/1.0/">
<rdf:Description rdf:about="http://www.ukoln.ac.uk/metadata/resources/dc/datamodel/WD-dc-rdf/">
<dc:title> Guidance on expressing the Dublin Core within the RDF </dc:title>
<dc:creator> Eric Miller </dc:creator>
<dc:creator> Paul Miller </dc:creator>
<dc:creator> Dan Brickley </dc:creator>
<dc:subject> Dublin Core; RDF; XML </dc:subject>
<dc:publisher> Dublin Core Metadata Initiative </dc:publisher>
<dc:contributor> Dublin Core Data Model Working Group </dc:contributor>
<dc:date> 1999-07-01 </dc:date>
<dc:format> text/html </dc:format>
<dc:language> en </dc:language>
</rdf:Description>
</rdf:RDF>
77. Dublin Core Example (RDF/N3)
Obtained with: http://rdf-translator.appspot.com/
78. RDF (as a Linked Data) Illustrated
C
Properties (predicate)
HUMAN
#Vagan Vagan Terziyan 27/12/58 #JyU
#John
#Aino
…
Resources (subject)
hasBirthday
isEmployedBy
hasName
hasSurname
:#JyU a UNIVERSITY
:#JyU :hasTitle “University of Jyvaskyla”
:#JyU :hasLocation “Finland”
:#JyU :hasWorldRank “204”
:#JyU :hasRector :#Aino
RDF triples
:#Vagan a HUMAN
:#Vagan :hasName “Vagan”
:#Vagan :hasSurname “Terziyan”
:#Vagan :hasBirthday “27/12/58”
:#Vagan :isEmployedBy :#JyU
C
UNIVERSITY
#MIT
#JyU University
of
Jyvaskyla
Finland 204 #Aino
#KNURE
…
hasWorldRank
hasRector
hasTitle
hasLocation
:#Aino a HUMAN
Each cell is an
RDF triple
RDF triples (continue)
… … …
!
79. Traditional RDF Statement
• Subject of an RDF statement is a resource
• Predicate of an RDF statement is a property of a
resource
• Object of an RDF statement is the value of a property
of a resource (either literal or resource)
Property_k
Resource_i Literal
Resource_i
Property_r
Resource_ j
OR
80. New semantics of RDF Statement in
S-APL (object - executable resource)
Resource_i
Property_m
exe: Resource_ j
Semantics of such statement executable property
means that the value of the
Property_m of the Resource_i
can be obtained as a result of
execution of the procedure (query,
service, function, etc.) represented
as Resource_ j
SS--AAPPLL
Semantic Agent Programming Language
(Designed by Industrial Ontologies Group)
82. RDFa: Combining human- and machine-readable
information within the same document !
RDFa (or Resource Description Framework – in – attributes) is a W3C Recommendation that
adds a set of attribute-level extensions to XHTML for embedding rich metadata within Web
documents. RDFa provides a set of XHTML attributes to augment visual data with machine-readable
hints and turns the existing human-visible text and links into machine-readable data
without repeating content. The great thing about RDFa is the ability to weave meaning and rich
data directly into a web page without having any impact on the front-end user experience.
XHTML
HTML5
Today's web is built predominantly for human readers. Even as machine-readable data begins to permeate
the web, it is typically distributed in a separate file, with a separate format, and very limited correspondence
between the human and machine versions. As a result, web browsers can provide only minimal assistance to
humans in parsing and processing web pages: browsers only see presentation information. RDFa is intended
to solve the problem of marking up machine-readable data in HTML documents. RDFa provides a set of
HTML attributes to augment visual data with machine-readable hints. Using RDFa, authors may turn their
existing human-visible text and links into machine-readable data without repeating content.
85. RDFa Example (2)
http://www.w3.org/TR/xhtml-rdfa-primer/
Read first:
Use:
(http://rdfa.info/play/)
Update your HTML with some RDFa inside and check it:
88. 96
Where to look next
• RDF:
http://www.w3.org/RDF/
• RDF Schema:
http://www.w3.org/TR/rdf-schema/
• RDFa :
http://www.w3.org/TR/xhtml-rdfa-primer/
89. DBpedia http://dbpedia.org/
DBpedia (DataBasePedia) is a project aiming to extract structured
content from the information created as part of the Wikipedia
project and making it available in the Web. DBpedia allows querying
relationships and properties associated with Wikipedia resources,
including links to other related datasets. Therefore DBpedia is one
of the more famous parts of the decentralized Linked Data effort.
The DBpedia data set uses a large multi-domain
ontology which has been derived
from Wikipedia. The English version of the
DBpedia 2014 data set currently describes
4.58 million “things” with 583 million “facts”.
In addition, localized versions of DBpedia
available in 125 languages. All these versions
together describe 38.3 million things,
out of which 23.8 million overlap (interlinked)
with concepts from the English DBpedia.
90. Concepts in DBpedia
DBpedia Ontology (2014)
The DBpedia Ontology is a
shallow, cross-domain
ontology, which has been
manually created based
on the most commonly
used infoboxes within
Wikipedia. The ontology
currently covers 685
classes which form
a subsumption hierarchy
and are described by 2,795
different properties.
91. Naming “things” in DBpedia
http://wiki.dbpedia.org/Datasets
Each thing in the DBpedia data set is denoted by a de-referenceable URI-based reference of the form:
http://dbpedia.org/resource/Name ,
where Name is derived from the URL of the source Wikipedia article, which has the form:
http://en.wikipedia.org/wiki/Name .
Thus, each DBpedia entity is tied directly to a Wikipedia article. Every DBpedia entity name resolves to a
description-oriented Web document (or Web resource). Read more in: http://wiki.dbpedia.org/Datasets
Kharkiv city in Wikipedia and DBpedia
http://dbpedia.org/resource/Kharkiv http://en.wikipedia.org/wiki/Kharki
v
92. Building personal graph as Linked Data
@base <http://www.cs.jyu.fi/ai/vagan/ontologies/university.owl#> .
@prefix db: <http://dbpedia.org/resource/> .
@prefix gs: <http://scholar.google.com/citations?user=> .
db:Kharkiv_National_University_of_Radioelectronics
db:Kharkiv
db:Ukraine
db:Finland
:hasPhDFrom
:hasPlaceOf Birth
db:University_of_Jyvaskyla
:isCitizenOf
:isResidentOf
:isEmployedBy
:hasPublicationRecord
:
VaganTerziyan
gs:4U8ydfgAAAAJ
93. OWL: Web Ontology Language
• The Web Ontology Language (OWL) is a family of knowledge
representation languages or ontology languages for engineering ontologies
or knowledge bases. The languages are characterized by formal semantics
and RDF/XML-based serializations for the Semantic Web.
• OWL DL (Description Logic) designed to provide the maximum
expressiveness possible while retaining computational completeness
(either φ or ¬φ belong), decidability (there is an effective procedure to
determine whether φ is derivable or not), and the availability of practical
reasoning algorithms. OWL DL includes all OWL language constructs, but
they can be used only under certain restrictions
http://www.w3.org/TR/owl-ref/ OWL 2004
http://www.w3.org/TR/owl2-overview/ OWL-2 2009
94. Why OWL? Limitations of RDFS
• RDFS is too weak in describing resources with
sufficient details, e.g.:
– No localised range and domain constraints
• Cannot say that the range of isToughtBy is only professor when applied to
professors and lecturer when applied to lecturers
– No cardinality constraints
• Cannot say that a course is taught by at least one professor, or persons have
exactly 2 parents
– No transitive, inverse or symmetrical properties
• Cannot say that isPartOf is a transitive property, that hasSupervisor is the
inverse of isSupervisorOf, and, that friendOf is symmetrical
– Disjoint classes
• Cannot say that Graduate and PhD. Students are two disjoint classes
– Boolean combinations of classes
• Sometimes we may need to build new classes by combining other classes using
union, intersection, and complement (e.g. person is the disjoint union of the
classes male and female)
95. OWL Class and OWL Properties
rdfs:Resource
rdfs:Class rdf:Property
owl:Class owl:ObjectProperty owl:DatatypeProperty
96.
97. OWL on one Slide
• Symmetric: if P(x, y) then P(y, x)
• Transitive: if P(x,y) and P(y,z) then P(x, z)
• Functional: if P(x,y) and P(x,z) then y=z
• InverseOf: if P1(x,y) then P2(y,x)
• InverseFunctional: if P(y,x) and P(z,x) then y=z
• allValuesFrom: P(x,y) and y=allValuesFrom(C)
• someValuesFrom: P(x,y) and y=someValuesFrom(C)
• hasValue: P(x,y) and y=hasValue(v)
• cardinality: cardinality(P) = N
• minCardinality: minCardinality(P) = N
• maxCardinality: maxCardinality(P) = N
• equivalentProperty: P1 = P2
• intersectionOf: C = intersectionOf(C1, C2, …)
• unionOf: C = unionOf(C1, C2, …)
• complementOf: C = complementOf(C1)
• oneOf: C = one of(v1, v2, …)
• equivalentClass: C1 = C2
• disjointWith: C1 != C2
• sameIndividualAs: I1 = I2
• differentFrom: I1 != I2
• AllDifferent: I1 != I2, I1 != I3, I2 != I3, …
• Thing: I1, I2, …
Legend:
Properties are indicated by: P, P1, P2, etc
Specific classes are indicated by: x, y, z
Generic classes are indicated by: C, C1, C2
Values are indicated by: v, v1, v2
Instance documents are indicated by: I1, I2, I3, etc.
A number is indicated by: N
P(x,y) is read as: “property P relates x to y”
98. An Example
• Woman ≡ Person ⊓ Female
• Man ≡ Person ⊓ ØWoman
• Mother ≡ Woman ⊓ $hasChild.Person
• Father ≡ Man ⊓ $hasChild.Person
• Parent ≡ Father ⊔ Mother
• Grandmother ≡ Mother ⊓ $hasChild.Parent
We can further infer (though not explicitly stated):
Grandmother ⊑ Person
Grandmother ⊑ Man ⊔ Woman
etc.
99. OWL-2 vs. OWL
OWL-2 adds new functionality with respect to OWL.:
• keys;
• disjoint union of classes
• property chains;
• richer datatypes, data ranges;
• qualified cardinality restrictions;
• asymmetric, reflexive, and disjoint properties;
• enhanced annotation capabilities .
100. Resources
• W3C Documents
– Guide: http://www.w3.org/TR/owl-guide/
– Reference: http://www.w3.org/TR/owl-ref/
– Semantics and Abstract Syntax:
http://www.w3.org/TR/owl-semantics/
• OWL Tutorials
– Ian Horrocks, Sean Bechhofer:
http://www.cs.man.ac.uk/~horrocks/Slides/Innsbruck-tutorial/
– Roger L. Costello, David B. Jacobs:
http://www.xfront.com/owl/
• Example Ontologies, e.g. here:
http://www.daml.org/ontologies/
101. Tutorial: Designing Ontologies with
Protégé
• Protégé is an ontology editor and a
knowledge-base editor (download
from:
http://protege.stanford.edu ).
• Protégé is also an open-source,
Java tool that provides an
extensible architecture for the
creation of customized knowledge-based
applications.
• Protégé's OWL Plug-in now
provides support for editing
Semantic Web ontologies.
http://www.cs.jyu.fi/ai/vagan/ProtegeOWL_1.ppt
http://www.cs.jyu.fi/ai/vagan/ProtegeOWL_2.ppt
PLEASE !!!
Download
version:
Protégé 3.5.
http://www.cs.jyu.fi/ai/vagan/Ontologies.ppt
104. Restrictions of Monotonic Logic
Semantic Web / Ontologies / OWL
are based on the Open World
Assumption: “everything is allowed
if not explicitly prohibited”
Databases / SQL / Prologue are
based on the Closed World
Assumption: “everything is
prohibited if not explicitly allowed”
Example:
John loves Mary
Q: John loves Mary?
A: yes
Q: Mary loves John?
A: I do not know …
Example:
John loves Mary
Q: John loves Mary?
A: yes
Q: Mary loves John?
A: no
Non-Monotonic Logic driven
by the “negation as failure” rule is
used for reasoning. For example
“belief revision” can be applied as
the process of changing beliefs to
accommodate a new belief that
might be inconsistent with the old
ones. In the assumption that the
new belief is correct, some of the
old ones have to be retracted in
order to maintain consistency.
Monotonic Logic assumes that adding a formula to a theory never produces a reduction of its set of
consequences, i.e., learning a new piece of knowledge cannot reduce the set of what is known. Therefore
monotonic logic cannot handle various reasoning tasks such as: reasoning by default (consequences may
be derived only because of lack of evidence of the contrary); abductive reasoning (consequences are only
deduced as most likely explanations); belief revision (when new knowledge may contradict old beliefs), etc.
105.
106. Example (formalised rules)
Atomic predicates from the story:
Let the initial state to be:
R1: IF (P1ÙP4 ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2 ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
1. Mary will love John if he loves her and if
he is not abusing Pete.
2. Pete will consider Mary as his friend if she
is not in love with John.
3. Pete will not consider Mary as his friend if
she is in love with John who is abusing him.
4. John will stop loving Mary if she does not
love him or she is a friend of Pete.
5. Mary will stop loving John if he is abusing
Pete.
6. John being in bad mood will abuse Pete.
7. John gets rid of bad mood if Mary loves
him or if she is not a friend of Pete.
8. John will fall in a bad mood if he loves
Mary and she does not love him or vice versa.
9. John will stop abusing Pete if he (John)
does not love Mary any more or if she is not a
friend of Pete.
P1 = :John:loves :Mary .
P2 = :Mary :loves :John .
P3 = :Pete :hasFriend :Mary .
P4 = :John :isAbusing :Pete .
P5 = :John :hasBadMood ”true”.
S(t0 ) =P1 Ù P2 ÙP3 Ù P4 Ù P5
Formalized rules from the story:
Rules as the story:
107. Hereinafter for the simplicity we will apply the rules synchronously !
Example (reasoning, 1-st step)
loves
John Mary
Pete
hasFriend
S(t0 ) =P1 Ù P2 ÙP3 Ù P4 Ù P5
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
0 1 2 3 4 5 R9: IF ((P1ÚP3)ÙP4) THEN P4. S(t +t ) = P ÙP ÙP Ù P ÙP
108. Example (reasoning, 2-nd step)
loves
John Mary
Pete
hasFriend
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
hasBadMood
S(t0 +t ) = P1 ÙP2 ÙP3 Ù P4 ÙP5
S(t0 + 2×t ) = P1 ÙP2 ÙP3 ÙP4 Ù P5
109. Example (reasoning, 3-rd step)
loves
John Mary
Pete
hasFriend
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
isAbusing
S(t0 + 2×t ) = P1 ÙP2 ÙP3 ÙP4 Ù P5
S(t0 + 3×t ) = P1 Ù P2 Ù P3 Ù P4 ÙP5
110. Example (reasoning, 4-th step)
John Mary
Pete
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
hasBadMood
S(t0 + 3×t ) = P1 Ù P2 Ù P3 Ù P4 ÙP5
S(t0 + 4×t ) = P1 Ù P2 ÙP3 ÙP4 Ù P5
111. Example (reasoning, 5-th step)
John Mary
isAbusing hasFriend
Pete
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
S(t0 + 4×t ) = P1 Ù P2 ÙP3 ÙP4 Ù P5
S(t0 + 5×t ) = P1 Ù P2 ÙP3 Ù P4 Ù P5
112. Example (reasoning, reaching terminal
state)
John Mary
Pete
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4.
S(t0 + 5×t ) = P1 Ù P2 ÙP3 Ù P4 Ù P5
hasFriend
terminal state
113. Example (final “terminal” state)
1. John does not love Mary.
2. Mary does not love John.
3. Mary is a friend of Pete.
4. John is not abusing Pete.
5. John is not in a bad mood.
S(t0 + 5×t ) = P1 Ù P2 ÙP3 Ù P4 Ù P5
114. Example (another initial and in the
same time terminal state)
1. John loves Mary.
2. Mary loves John.
3. Mary is not a friend of Pete.
4. John is not abusing Pete.
5. John is not in a bad mood.
to love
John Mary
S(t0 ) =P1 ÙP2 Ù P3 Ù P4 Ù P5
to love
Pete
R1: IF (P1ÙP4ÙP2) THEN P 2;
R2: IF (P2ÙP3) THEN P 3;
R3: IF (P2ÙP4ÙP3) THEN P3;
R4: IF ((P3ÚP2)ÙP1) THEN P1;
R5: IF (P2 ÙP4) THEN P2;
R6: IF (P5 ÙP4) THEN P 4;
R7: IF ((P2ÚP3)ÙP5) THEN P5;
R8: IF ((P1ÙP2ÚP1ÙP2)ÙP5) THEN P 5;
R9: IF ((P1ÚP3)ÙP4) THEN P4. terminal state
115. Example (two possible terminal
states of the “love triangle”)
to love
John Mary
to love
Pete
John Mary
Pete
to have a friend
119. SWRL Rule structure
Contains an antecedent part (body), and a consequent (head).
The body and head consist of positive conjunctions of atoms:
Atom Ù Atom … → Atom Ù Atom …
SWRL provides seven types of atoms:
- Class Atoms owl:Class e.g., Person (?x), …, Person (John)
- Individual Property atoms owl:ObjectProperty e.g., loves (?x, ?y), …, loves (John, Mary)
- Data Valued Property atoms owl:DatatypeProperty e.g., hasAge(?x, 19), …, hasAge(Mary, 19)
- Different Individuals atoms e.g., differentFrom(?x, ?y), …,
- Same Individual atoms e.g., sameAs(?x, ?y), …, sameAs(John, ?y)
- Built-in atoms (next slide)
Person(?x) Ù hasSibling(?x,?y) Ù Man(?y) → hasBrother(?x,?y)
antecedent consequent
120. SWRL built-ins
Some mathematical operations
Comparison of two datatypes swrlb:add (satisfied iff the first argument is equal to
swrlb:equal
swrlb:notEqual
swrlb:lessThan
swrlb:lessThanOrEqual
swrlb:greaterThan
swrlb:greaterThanOrEqual
swrlb:stringEqualIgnoreCase
(satisfied iff the first argument (string) is
the same as the second argument
(upper/lower case ignored))
the arithmetic sum of the second argument and the
third argument)
swrlb:subtract
swrlb:multiply
swrlb:divide
swrlb:pow (satisfied iff the first argument is equal
to the result of the second argument raised to the third
argument power)
swrlb:abs (satisfied iff the first argument is the
absolute value of the second argument)
swrlb:round (satisfied iff the first argument is equal
to the nearest number to the second argument with no
fractional part)
swrlb:booleanNot (satisfied iff the first argument is
true and the second argument is false, or vice versa.
Read more in:
http://www.w3.org/Submission/SWRL/
121. Examples of SWRL Rules (1)
Rule computes age of a person from the current year and
the date of birth:
Person(?x) Ù hasYearOfBirth(?x, ?yborn) Ù hasCurrentYear(?x,?ycurrent) Ù
Ù swrlb:subtract(?age, ?ycurrent, ?yborn) ®
® hasAgeYears(?x,?age)
122. Examples of SWRL Rules
A researcher, which Hirsch-index (citation index) is more
than amount of years in her academic career, is a good
researcher:
Researcher(?x) Ù hasHindexOfPublications(?x, ?hirschindex) Ù
Ù hasYearsInCareer(?x, ?years) Ù swrlb:greaterThan(?hirschindex,?years) ®
® GoodResearcher(?x)
123. Examples of SWRL Rules
A person of age between 29-55 would have an extra weight
equal to positive difference between his/her weight in kilos
and his/her height in centimeters (minus 100):
Person(?x) Ù hasAgeYears(?x,?age) Ù swrlb:greaterThan(?age, 28) Ù
Ù swrlb:lessThan(?age, 56) Ù hasWeightKg(?x, ?weight) Ù hasLengthSm(?x, ?length) Ù
Ù swrlb:subtract(?ideal, ?length, 100) Ù swrlb:subtract(?extra, ?weight, ?ideal) Ù
Ù swrlb:greaterThan(?extra, 0) ®
® hasExtraWeightKg(?x, ?extra)
124. Examples of SWRL Rules
A person of age between 29-55 would have a lacking weight
equal to positive difference between his/her height in
centimeters (minus 100) and his/her weight in kilos:
Person(?x) Ù hasAgeYears(?x,?age) Ù swrlb:greaterThan(?age, 28) Ù
Ù swrlb:lessThan(?age, 56) Ù hasWeightKg(?x, ?weight) Ù hasLengthSm(?x, ?length) Ù
Ù swrlb:subtract(?ideal, ?length, 100) Ù swrlb:subtract(?lacking, ?ideal, ?weight) Ù
Ù swrlb:greaterThan(?lacking, 0) ®
® hasLackingWeightKg(?x, ?lacking)
Editor's Notes
¥begin{array}{r@{¥,}c@{¥,}l} ¥forall x.[¥textsf{¥color{blue} Heart}(x) & {¥color{red}¥rightarrow} & ¥textsf{¥color{blue} MuscularOrgan}(x) ¥,¥wedge¥, ¥¥ & & ¥exists y.[¥textsf{¥color{magenta} isPartOf}(x,y) ¥,¥wedge¥, ¥¥&&¥textsf{¥color{blue} CirculatorySystem}(y)]] ¥end{array}