This document discusses the evolving semantic web. It defines the semantic web as making knowledge machine and human-readable by providing context and meaning for information on the web. The semantic web utilizes technologies like URIs, RDF, and OWL to describe relationships between web resources in a machine-readable way. Lighter semantic standards like RDFa, microformats, and microdata are also discussed as easier ways to add semantics to existing web pages. The status and potential future applications of the semantic web are outlined.
The document discusses the Semantic Web and metadata standards. It describes the Semantic Web as a web of data that can be processed by machines. It explains how the Semantic Web is being developed both top-down through more intelligent applications and bottom-up through increased use of structured data formats and standards like URIs, RDF, and OWL. It provides examples of applications using these standards and discusses metadata standards like RDA, DCMI, and their relationship.
The document introduces the concepts of the Semantic Web and its goals. It discusses how the Semantic Web aims to add meaning to documents on the World Wide Web through standards like XML, RDF and ontologies. It provides an example of how the Semantic Web could understand information about a person like their schedule and help manage their daily life. The document outlines the chapters of the book, which will cover topics like XML, RDF, ontologies, knowledge representation and applications of Semantic Web technologies.
The document discusses linked data and its use in libraries. It describes how linked data can make implicit information explicit by using vocabularies and ontologies. Linked data takes advantage of web standards to better describe resources and make them easier to find. It addresses the need for a "library shaped hole" on the internet and the benefits of moving library data out of silos and enabling reuse through a MARC replacement like BIBFRAME. Challenges in transforming data and transitioning to new terminology are also discussed.
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.
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.
The document discusses the semantic web, including its history and key components. It describes how the semantic web aims to make web content machine-readable through technologies like XML, URIs, RDF, RDFS, and OWL. This will allow computers to better understand web resources and their relationships, enabling more intelligent searching and use of web data than is possible on the traditional web. However, developing the semantic web also faces challenges such as complexity, lack of industry adoption, and needing further consensus on technical standards.
This document discusses live social semantics and monitoring online communities. It presents approaches to integrate physical presence data from RFID sensors with online social network and semantic web data. This would allow for semantic user profiling, logging face-to-face contacts, and analyzing how online and offline social networks converge. The live social semantics architecture extracts and links social media data to semantic web data sources and stores it in a triple store for analysis and applications like social network browsing.
The document discusses the Semantic Web and metadata standards. It describes the Semantic Web as a web of data that can be processed by machines. It explains how the Semantic Web is being developed both top-down through more intelligent applications and bottom-up through increased use of structured data formats and standards like URIs, RDF, and OWL. It provides examples of applications using these standards and discusses metadata standards like RDA, DCMI, and their relationship.
The document introduces the concepts of the Semantic Web and its goals. It discusses how the Semantic Web aims to add meaning to documents on the World Wide Web through standards like XML, RDF and ontologies. It provides an example of how the Semantic Web could understand information about a person like their schedule and help manage their daily life. The document outlines the chapters of the book, which will cover topics like XML, RDF, ontologies, knowledge representation and applications of Semantic Web technologies.
The document discusses linked data and its use in libraries. It describes how linked data can make implicit information explicit by using vocabularies and ontologies. Linked data takes advantage of web standards to better describe resources and make them easier to find. It addresses the need for a "library shaped hole" on the internet and the benefits of moving library data out of silos and enabling reuse through a MARC replacement like BIBFRAME. Challenges in transforming data and transitioning to new terminology are also discussed.
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.
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.
The document discusses the semantic web, including its history and key components. It describes how the semantic web aims to make web content machine-readable through technologies like XML, URIs, RDF, RDFS, and OWL. This will allow computers to better understand web resources and their relationships, enabling more intelligent searching and use of web data than is possible on the traditional web. However, developing the semantic web also faces challenges such as complexity, lack of industry adoption, and needing further consensus on technical standards.
This document discusses live social semantics and monitoring online communities. It presents approaches to integrate physical presence data from RFID sensors with online social network and semantic web data. This would allow for semantic user profiling, logging face-to-face contacts, and analyzing how online and offline social networks converge. The live social semantics architecture extracts and links social media data to semantic web data sources and stores it in a triple store for analysis and applications like social network browsing.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
The document discusses the Social Semantic Web and related technologies. It provides an overview of the growth of social networks and user-generated content online. It then discusses how semantic technologies can help connect isolated social communities and their data by adding machine-readable metadata. Key topics covered include the Semantic Web stack, linked data, ontologies for modeling social data like FOAF and SIOC, and applications like distributed identity and social recommendations.
The document discusses the history and development of the Semantic Web over the past 20 years. It begins with Tim Berners-Lee originally conceiving of the Semantic Web in 1994 with a vision of machines being able to understand web documents and perform tasks like property transfers. Since then, there has been over 200 talks on the Semantic Web but the focus was initially on technologies like XML, RDF, and OWL. More recently, Linked Data and RDFa have seen the most usage in applications while the ontology story remains unclear. Moving forward, bridging the gaps between linked data and formal ontology views will require addressing challenges like modeling incomplete and decentralized data at web-scale.
This document provides an overview of the Semantic Web vision. It discusses how currently most web content is designed for human consumption rather than machine processing. The Semantic Web aims to develop a web of data that can be understood and processed by machines through the use of common data formats and description of relationships. This will allow data from different sources to be linked and queried in new ways, enabling more automated use and integration of web information.
The document provides an overview of social semantics and the social semantic web. It discusses how social data on platforms like Facebook and Twitter can be represented semantically using ontologies and vocabularies. This includes representing people with FOAF, relationships with Schema.org, content with SIOC, and behavior with OUBO. Representing social data semantically allows it to be queried, linked across platforms, and analyzed with semantic web technologies. The social semantic web aims to overcome the siloed nature of social data and enable portability of social information.
Web 3.0 / Semantic Web: What it means for academic users, libraries and publi...Richard Wallis
The document discusses the concepts of Web 3.0 and the Semantic Web and their potential implications for academic users, libraries, and publishers. It introduces key ideas like linked data, using URIs to identify concepts and relationships, and the RDF data model. It notes that while the technology is still in the early adopter phase, publishers could help by making their data openly available in semantic formats and developing services that directly connect students to relevant resources through linked open data approaches. This may help break down silos between academic institutions and help students more easily access relevant information.
1) BIBFRAME is a new bibliographic framework developed by the Library of Congress to replace MARC standards and better integrate library data with the semantic web.
2) BIBFRAME uses linked data principles and RDF to make library data more extensible and interconnected on the web.
3) The main benefits of BIBFRAME are that it allows library data to be more discoverable online, integrates better with web standards, and is more flexible and reusable than MARC records. However, transforming existing data and training catalogers will be challenges in adopting BIBFRAME.
Open Data Management for Public Automated TranslationDave Lewis
A proposal developed jointly by FALCON (www.falcon-project.eu) and LIDER (www.lider-project.eu) projects for Open Data Management for Public Automated Translation Services. This was offered as input to the MLi project, which is capturing procurement requirements for future Automated Translation service under the EU’s Connecting Europe Facilities, CEF.AT
Evolution Towards Web 3.0: The Semantic WebLeeFeigenbaum
This was a lecture I presented at Professor Stuart Madnick's class, "Evolution Towards Web 3.0" at the MIT Sloan School of Management on April 21, 2011. Please follow along with the speaker notes which add significant commentary to the slides.
The document provides an overview of the semantic web and how it works. It discusses different approaches to semantics including tagging, statistics, linguistics, semantic web, and artificial intelligence. It describes standards for the semantic web like RDF, OWL, and SPARQL. The document outlines the future outlook for the semantic web and discusses an application called Twine that uses semantic web technologies to organize, share, and discover content around user interests.
Social Networks and the Semantic Web: a retrospective of the past 10 yearsPeter Mika
The document summarizes the past 10 years of social networks and the Semantic Web. It discusses how early visions of a decentralized, interoperable Social-Semantic Web did not fully materialize due to social networks consolidating user data into silos. However, work continues through standards bodies to develop vocabularies and building blocks that could still enable a federated social web. It also notes that while online social science is now widespread, challenges remain around access to social data and the ability to generalize findings over time and platforms.
A very brief introduction to the semantic web (web 3.0) and how it relates to the social web. Includes a video of the progression from Web 1.0 to Web 2.0 to Web 3.0 by robin fay, georgiawebgurl@gmail.com
The document discusses semantic search and summarizes some key points:
1. Semantic search aims to improve search by exploiting structured data and metadata to better understand user intent and content meaning.
2. It can make use of information extraction techniques to extract implicit metadata from unstructured web pages, or rely on publishers exposing structured data using semantic web formats.
3. Semantic search can enhance different stages of the information retrieval process like query interpretation, indexing, ranking, and evaluation.
Interlinking Online Communities and Enriching Social Software with the Semant...John Breslin
This document summarizes a presentation about interlinking online communities using Semantic Web technologies. It discusses:
1. The SIOC (Semantically-Interlinked Online Communities) project which aims to semantically connect online discussion sites through a common data model.
2. How SIOC represents the structure and content of communities using RDF properties and classes. Communities can then exchange and query data using common semantics.
3. Tools that export community data into RDF using SIOC, including for WordPress, vBulletin, and phpBB. This allows interlinking users, content, and activities across sites.
- The document is a slide presentation on semantic analysis in language technology that discusses the semantic web and ontologies. It provides examples of question answering systems like START, Siri, and IBM Watson and discusses the evolution of the web from Web 1.0 to Web 2.0 to the proposed Web 3.0. It also introduces key concepts like ontologies, semantic metadata, and the role of semantics in allowing machines to process information.
This document discusses the evolution of the web and the concept of the semantic web. It defines the semantic web as a web with meaning where data is interlinked and searchable. The key aspects of the semantic web include using technologies like RDF, OWL and SPARQL to embed relationships and definitions within data to make it more meaningful and accessible to machines. This allows for more personalized searches and access to accurate information. Challenges include developing open standards for ontologies.
From the Semantic Web to the Web of Data: ten years of linking upDavide Palmisano
This document discusses the concepts and technologies behind the Semantic Web. It describes how RDF, RDF Schema, and OWL allow structured data and relationships to be represented and shared across the web. It also discusses tools for working with semantic data in Java, such as Jena, Sesame, and Any23 for extracting and working with RDF. The document provides examples of representing data and relationships in RDF and querying semantic data with SPARQL.
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.
The document discusses the evolution of the semantic web from its origins in military technology to its current use in commercial applications. It describes how semantic web standards like RDF, RDFS, and OWL were developed and how the semantic web has transformed in areas like markets, linked data, and scaling. The talk outline focuses on the origins of the semantic web, key developments through 2010, transformations in three application areas, related markets and companies, and the linked data and scaling revolution.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
The document discusses the Social Semantic Web and related technologies. It provides an overview of the growth of social networks and user-generated content online. It then discusses how semantic technologies can help connect isolated social communities and their data by adding machine-readable metadata. Key topics covered include the Semantic Web stack, linked data, ontologies for modeling social data like FOAF and SIOC, and applications like distributed identity and social recommendations.
The document discusses the history and development of the Semantic Web over the past 20 years. It begins with Tim Berners-Lee originally conceiving of the Semantic Web in 1994 with a vision of machines being able to understand web documents and perform tasks like property transfers. Since then, there has been over 200 talks on the Semantic Web but the focus was initially on technologies like XML, RDF, and OWL. More recently, Linked Data and RDFa have seen the most usage in applications while the ontology story remains unclear. Moving forward, bridging the gaps between linked data and formal ontology views will require addressing challenges like modeling incomplete and decentralized data at web-scale.
This document provides an overview of the Semantic Web vision. It discusses how currently most web content is designed for human consumption rather than machine processing. The Semantic Web aims to develop a web of data that can be understood and processed by machines through the use of common data formats and description of relationships. This will allow data from different sources to be linked and queried in new ways, enabling more automated use and integration of web information.
The document provides an overview of social semantics and the social semantic web. It discusses how social data on platforms like Facebook and Twitter can be represented semantically using ontologies and vocabularies. This includes representing people with FOAF, relationships with Schema.org, content with SIOC, and behavior with OUBO. Representing social data semantically allows it to be queried, linked across platforms, and analyzed with semantic web technologies. The social semantic web aims to overcome the siloed nature of social data and enable portability of social information.
Web 3.0 / Semantic Web: What it means for academic users, libraries and publi...Richard Wallis
The document discusses the concepts of Web 3.0 and the Semantic Web and their potential implications for academic users, libraries, and publishers. It introduces key ideas like linked data, using URIs to identify concepts and relationships, and the RDF data model. It notes that while the technology is still in the early adopter phase, publishers could help by making their data openly available in semantic formats and developing services that directly connect students to relevant resources through linked open data approaches. This may help break down silos between academic institutions and help students more easily access relevant information.
1) BIBFRAME is a new bibliographic framework developed by the Library of Congress to replace MARC standards and better integrate library data with the semantic web.
2) BIBFRAME uses linked data principles and RDF to make library data more extensible and interconnected on the web.
3) The main benefits of BIBFRAME are that it allows library data to be more discoverable online, integrates better with web standards, and is more flexible and reusable than MARC records. However, transforming existing data and training catalogers will be challenges in adopting BIBFRAME.
Open Data Management for Public Automated TranslationDave Lewis
A proposal developed jointly by FALCON (www.falcon-project.eu) and LIDER (www.lider-project.eu) projects for Open Data Management for Public Automated Translation Services. This was offered as input to the MLi project, which is capturing procurement requirements for future Automated Translation service under the EU’s Connecting Europe Facilities, CEF.AT
Evolution Towards Web 3.0: The Semantic WebLeeFeigenbaum
This was a lecture I presented at Professor Stuart Madnick's class, "Evolution Towards Web 3.0" at the MIT Sloan School of Management on April 21, 2011. Please follow along with the speaker notes which add significant commentary to the slides.
The document provides an overview of the semantic web and how it works. It discusses different approaches to semantics including tagging, statistics, linguistics, semantic web, and artificial intelligence. It describes standards for the semantic web like RDF, OWL, and SPARQL. The document outlines the future outlook for the semantic web and discusses an application called Twine that uses semantic web technologies to organize, share, and discover content around user interests.
Social Networks and the Semantic Web: a retrospective of the past 10 yearsPeter Mika
The document summarizes the past 10 years of social networks and the Semantic Web. It discusses how early visions of a decentralized, interoperable Social-Semantic Web did not fully materialize due to social networks consolidating user data into silos. However, work continues through standards bodies to develop vocabularies and building blocks that could still enable a federated social web. It also notes that while online social science is now widespread, challenges remain around access to social data and the ability to generalize findings over time and platforms.
A very brief introduction to the semantic web (web 3.0) and how it relates to the social web. Includes a video of the progression from Web 1.0 to Web 2.0 to Web 3.0 by robin fay, georgiawebgurl@gmail.com
The document discusses semantic search and summarizes some key points:
1. Semantic search aims to improve search by exploiting structured data and metadata to better understand user intent and content meaning.
2. It can make use of information extraction techniques to extract implicit metadata from unstructured web pages, or rely on publishers exposing structured data using semantic web formats.
3. Semantic search can enhance different stages of the information retrieval process like query interpretation, indexing, ranking, and evaluation.
Interlinking Online Communities and Enriching Social Software with the Semant...John Breslin
This document summarizes a presentation about interlinking online communities using Semantic Web technologies. It discusses:
1. The SIOC (Semantically-Interlinked Online Communities) project which aims to semantically connect online discussion sites through a common data model.
2. How SIOC represents the structure and content of communities using RDF properties and classes. Communities can then exchange and query data using common semantics.
3. Tools that export community data into RDF using SIOC, including for WordPress, vBulletin, and phpBB. This allows interlinking users, content, and activities across sites.
- The document is a slide presentation on semantic analysis in language technology that discusses the semantic web and ontologies. It provides examples of question answering systems like START, Siri, and IBM Watson and discusses the evolution of the web from Web 1.0 to Web 2.0 to the proposed Web 3.0. It also introduces key concepts like ontologies, semantic metadata, and the role of semantics in allowing machines to process information.
This document discusses the evolution of the web and the concept of the semantic web. It defines the semantic web as a web with meaning where data is interlinked and searchable. The key aspects of the semantic web include using technologies like RDF, OWL and SPARQL to embed relationships and definitions within data to make it more meaningful and accessible to machines. This allows for more personalized searches and access to accurate information. Challenges include developing open standards for ontologies.
From the Semantic Web to the Web of Data: ten years of linking upDavide Palmisano
This document discusses the concepts and technologies behind the Semantic Web. It describes how RDF, RDF Schema, and OWL allow structured data and relationships to be represented and shared across the web. It also discusses tools for working with semantic data in Java, such as Jena, Sesame, and Any23 for extracting and working with RDF. The document provides examples of representing data and relationships in RDF and querying semantic data with SPARQL.
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.
The document discusses the evolution of the semantic web from its origins in military technology to its current use in commercial applications. It describes how semantic web standards like RDF, RDFS, and OWL were developed and how the semantic web has transformed in areas like markets, linked data, and scaling. The talk outline focuses on the origins of the semantic web, key developments through 2010, transformations in three application areas, related markets and companies, and the linked data and scaling revolution.
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.
This talk introduces the concepts of web 3.0 technology and how they relate to related technologies such as Internet of Things (IoT), Grid Computing and the Semantic Web:
• A short history of web technologies:
o Web 1.0: Publishing static information with links for human consumption.
o Web 2.0: Publishing dynamic information created by users, for human consumption.
o Web 3.0: Publishing all kinds of information with links between data items, for machine consumption.
• Standardization of protocols for description of any type of data (RDF, N3, Turtle).
• Standardization of protocols for the consumption of data in “the grid” (SPARQL).
• Standardization of protocols for rules (RIF).
• Comparison with the evolution of technologies related to data bases.
• Comparison of IoT solutions based on web 2.0 and web 3.0 technologies.
• Distributed solutions vs centralized solutions..
• Security
• Extensions of Peer-to-peer protocols (XMPP).
• Advantages of solutions based on web 3.0 and standards (IETF, XSF).
Duration of talk: 1-2 hours with questions.
The document discusses the semantic web and how it can potentially disrupt or benefit online commerce. It provides definitions and explanations of key concepts related to the semantic web including RDF, ontologies, linked data, and semantic search. It outlines how search engines and websites are increasingly adopting and leveraging semantic web technologies like RDFa to provide richer search results and experiences for users.
The document discusses the Semantic Web, which aims to make web data more easily processable by machines through linking related information. It has four main components - URIs for identification, RDF for describing data, RDF Schema for describing data properties, and OWL for adding reasoning. This allows machines to better interpret and draw conclusions from web data. Challenges include dealing with the vastness, vagueness, uncertainty and inconsistency of web data. The document outlines benefits like more precise information retrieval and simplified application integration. It encourages contributions to developing Semantic Web languages and applications.
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.
The document discusses the vision of the Semantic Web and how it allows data to be shared and reused across applications. It outlines some of the key components of the Semantic Web like ontology, RDF, and URIs. It also discusses some common misconceptions about the Semantic Web, including that it is not about building AI applications or that it requires large ontologies. The Semantic Web is envisioned to seamlessly integrate with the existing Web to allow easier sharing and integration of data.
There has been plenty of hype around the Semanic Web, but will we ever see the vision of intelligent agents working on our behalf? This talk introduces the concepts of the Semantic Web as envisioned by Tim Berners-Lee over 10 years ago and compares that vision to where we have come since then. It includes a discussion of implementations such as XML, RDF, OWL (web ontology language), and SPARQL. After reviewing the design principles and enabling technologies, I plan to show how these techniques can be implemented in WebGUI.
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
The document discusses metadata and semantic web technologies. It provides an example of using RDFa to embed metadata in a web page about a book. It also shows how schema.org, microformats, and microdata can be used to add structured metadata. Finally, it discusses linked data and how semantic web technologies allow sharing and linking data on the web.
The document discusses the Semantic Web, which aims to extend the current web by adding meaning to data through technologies like XML, RDF, and ontologies. This will allow machines to better understand and process the meaning of information on the web. The key components that enable the Semantic Web are described, along with its potential uses and examples of implementation. Overall, the Semantic Web seeks to evolve the web into a more machine-readable format to improve knowledge management, information retrieval, and automation.
The speaker discusses the semantic web and its potential to make data on the web smarter and more connected. He outlines several approaches to semantics like tagging, statistics, linguistics, semantic web, and artificial intelligence. The semantic web allows data to be self-describing and linked, enabling applications to become more intelligent. The speaker demonstrates a prototype semantic web application called Twine that helps users organize and share information about their interests.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
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.
This document discusses the Semantic Web and Linked Open Data. It explains how the Semantic Web helps integrate data by using shared vocabularies and URIs to normalize meanings between data sources. As more datasets adopt Semantic Web principles by exposing structured data through URIs and RDF formats, individual datasets become less isolated and are interconnected to form a large knowledge base. The document provides examples of querying and exploring Linked Open Data through SPARQL and the LOD Cloud. It also offers recommendations for publishing and working with Linked Open Data.
- The speaker discusses how the semantic web connects all types of information like people, companies, products, etc. using richer semantics to enable better search, targeted ads, collaboration, and personalization.
- Semantic technologies will play a key role in transforming the web from just a file server to an intelligent database over the next decade.
- The speaker demonstrates his company Twine's semantic web platform which allows users to organize, share, and discover content around their interests.
The document discusses the Semantic Web, which aims to develop the current web so that machines can understand the meaning of information and not just display it. It outlines some key technologies being used like XML, RDF, and ontologies to add structure and meaning to web content. This will allow software agents to perform more sophisticated tasks by processing structured, machine-readable information based on defined ontologies. The Semantic Web represents an evolution from today's web designed primarily for humans to one where machines can also comprehend and utilize web content.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
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The Evolving Semantic Web
1. THE EVOLVING SEMANTIC
WORLD
Barbara McGlamery
Taxonomist
Martha Stewart Living Omnimedia
2. ABOUT ME
Masters in Library and Information Science
Long Island University
New York Public Library
Branch librarian
NYPL for the Performing Arts – Drama reference
Entertainment Weekly
Data Manager
Time Inc.
Senior Data Manager, Taxonomist, Metadata Architect, Ontologist
Martha Stewart Living Omnimedia
Taxonomist
4. The Semantic Web is a web of data…. (it) provides a
common framework that allows data to be shared and
reused across applications, enterprise, and community
boundaries.
--w3c
5. "The Semantic Web is not a separate Web but an
extension of the current one, in which information is
given well-defined meaning, better enabling computers
and people to work in cooperation.”
--Tim Berners-Lee, James Hendler, and Ora Lassila,
Scientific American, 2001
6. The Semantic Web is about making knowledge
machine and human-readable
10. BIG S SEMANTIC WEB
…big "S" web technologies provide a
framework for describing data on a web page when
the data on the website is published. If data is read
or captured, because the data's semantic meaning
has already been described, you don't have to go
through the process of understanding the meaning
of the data after the fact.
--Sean Martin, CEO of Cambridge Semantics
11. LITTLE S SEMANTICS
Little "s" web technologies capture and filter data with no
description or understanding of the data provided after
the capture process. The process of understanding the
meaning of that data starts once data capture has
happened. People have to intervene to provide the
context and meaning for language on the web.
--Sean Martin, CEO of Cambridge Semantics
12. Big S–
W3C approved
standard
Little s
Looser groups of unaffiliated
standards
14. ESSENTIALS OF BIG S SEMANTIC WEB
URI – Uniform Resource Identifier
RDF – Resource Description Framework
OWL – Web Ontology Language
Semantic reasoner (inference engine)
15. URI – UNIFORM RESOURCE IDENTIFIER
Way to identify things
Images, pages of text, locations
De-referenceable
Freebase
http://www.freebase.com/view/en/will_smith
• URI’s are unique, no two are the same
• Will Smith
http://www.freebase.com/view/en/will_smith
16. RDF – RESOURCE DESCRIPTION FRAMEWORK
Framework used to describe relationships between
objects
Extends and formalizes XML
Subject>Predicate>Object
17. RDF – RESOURCE DESCRIPTION FRAMEWORK
Subject>Predicate>Object
>> >>>
is the lead
actor
>>>>>>
Will
Smith Bad Boys
http://ew.com/PersonsTax/Will_Smith
http://ew.com/EntertainmentOnt/leadPe
rformanceIn
http://ew.com/EntertainmentTax/Mo
vies/Bad_Boys
18. OWL – WEB ONTOLOGY LANGUAGE
…designed to be used by applications that need to
process the content of information instead of just
presenting it to humans
-- W3C
19. OWL – WEB ONTOLOGY LANGUAGE
Metadata model
Extends RDF to further define properties
Ex: Equivalent relationships
>> >>>
is married to
>>>>>>
>> >>>
is married to
>>>>>>
20. SEMANTIC REASONER
Software able to infer logical consequences from a set
of asserted facts
Follows inference rules specified by OWL properties
Inverse
Transitive
Symmetric
Functional/Inverse functional
Equivalent
21. PUTTING IT ALL TOGETHER
Ontology
Rule set
Classes and Properties
Taxonomy
Application of Rule Set
Tags and Relationships
Everything is a statement
Subject>Predicate>Object
Ex: Will Smith is lead performer
in Bad Boys
22. BENEFITS OF RDF/OWL
Persistent URIs
Verifiable XML
Unambiguous Relationships
Polyhierarchy
Interoperability
23. LIMITATIONS OF RDF/OWL
Difficult to propagate across web
Challenge to integrate with legacy systems
Expensive queries
No “Killer App”
26. RDFa - Resource Description Framework (in) Attributes
W3C recommendation that adds a set
of attribute-level extensions to XHTML
for embedding rich metadata within
Web documents
Easy to implement
Not HTML 5 compliant
32. MICRODATA
A WHATWG HTML5 specification used to nest
semantics within existing content on web pages
Officially supported by Bing, Yahoo, & Google
Can imbed other markup languages like
RDFa, microformats, and Dublin Core
Not well-known (yet)
34. OPEN GRAPH PROTOCOL
Facebook-created markup language that turns any
web page into an Open Graph Objects allowing for
any page to become a Facebook page
I “Like” you
Good for targeted advertising
Limited in scope
36. BACK-OF-THE-NAPKIN COMPARISON
Features RDF/OW RDFa MF MD OGP
L
W3C X X X
standard
Extensible X X X
Pre-existing X X
Vocabs
Uses URIs X X
Easy to X X X X
implement
HMTL 5 X X X
compliant
Inferencing X
37. STATUS REPORT ON S SEMANTIC WEB
Linked Open Data graph growing
Many countries have developed government sites with
rich semantics
Development of Semantic search
More widespread adoption of lighter semantics
38. WHERE WE MIGHT BE GOING
Pharmaceutical industry identifies trends across clinical
studies, and not just within them
News industry better targets content by locale
Department of Defense using it to make better decisions
in the field
Utilized in advertising to drive more and more revenue
40. Barbara McGlamery
Taxonomist
Martha Stewart Living Omnimedia
(212)827-8817
bmcglamery@marthastewart.com
Editor's Notes
**The landscape of the semantic web is changing. Early adopters learned the hard lessons for all of us, that semantic web solutions can be difficult to implement and perhaps not vital to every organization’s interests. Barbara McGlamery, of Martha Stewart Living Omnimedia will share her experiences of building a Semantic Web tool from scratch for Time Inc. and how a smaller more manageable initiative has been undertaken at Martha Stewart. She’ll share case studies and lessons learned as well as give a glimpse as to how she sees the industry evolving.
Hello my name is blah. I am not a technical librarian, I am a librarian and when I was practicing it was in reference, not systems or back-end. So most of you out there have my respect and awe at knowing how the inside of a cataloging terminal works or TK (find out something LITA librarians do). My foray into the more technical aspects of librarianship came through html and web development.
**always refer to acronyms by full names: Resource Description Framework (RDF)Maybe a grid comparing RDF, Microformats, etcLandscape of SW – who created it and why?
In brief, The data is machine readable.
In short
Mention same as
Extends and formalizes XMLLinking structure of the Web to use URIs to name the relationship between thingsEx:
s designed for use by applications that need to process the content of information instead of just presenting information to humans.
Dif between a semantic reasoner and a regular inference engine is that a semantic reasoner knows the rules of owl. It is a more specific use.Inverse – Indicating the reciprocal property. For example, “owner of” is the inverse of the property “is owned by.”Transitive – Indicating that if this property applies between item 1 and item 2, and between item 2 and item 3, then it also applies between item 1 and item 3. For example, if Albuquerque “is located in” New Mexico, and New Mexico “is located in” the USA, then Albuquerque “is located in” the USA.Symmetric – Indicating that the inverse of this property is itself. For example, if the Time/Life Building “is near” Rockefeller Center, then it is also true that Rockefeller Center “is near” the Time/Life Building.Functional – Indicating that there can be only one value for this property for a given resource. For example “has birth mother” – the implication is that if a resource called Bob “has birth mother” Jane and also “has birth mother” Mrs. Smith, then we can assume that Jane and Mrs. Smith are the same person.Inverse Functional – Indicating that only one resource can have a given value for this property – which allows you to make assumptions that if two or more resources have that value, then they are really just two names for the same thing. This is very much like Functional, but in the opposite direction. For example, if there are two names with the same value for “has Social Security number,” we can assume that those are two names for the same person. Equivalent Property – like Equivalent Class, indicates that this property can be extended to the same set of resources that use another property. For example, EW.com’s “lead performance” would be an equivalent property to People’s “starring role.”
The bottom layers contain technologies that are well known from hypertext web and that without change provide basis for the semantic web.Middle layers contain technologies standardized by W3C to enable building semantic web applicationsTop layers contain technologies that are not yet standardized or contain just ideas that should be implemented in order to realize Semantic Web.Rules further extend OWL’s capabilitiesProof and Logic establish truth of statements, infer unstated factsTrust – Cryptology, authentication, trustworthiness of statementsSemantic Web FoundationsURI/IRI URI is an acronym for Uniform Resource Identifier; a compact string of characters used to identify or name a resource. The URL to a web site (e.g. http://www.semanticfocus.com) is a popular example of a URI. IRI is an acronym for Internationalized Resource Identifier which is a form of URI that uses characters beyond ASCII, thus becoming more useful in an international context. Unicode Unicode is the universal standard encoding system and provides a unified system for representing textual data. 1 million characters can be encoded to specify any character in any language without a single escape sequence or control code. Before Unicode, there were several different encoding systems which made communication and integration across borders a big pain. Now it's so much easier. Shout out to my peeps in Bangalore, 'haaaay' (अरे, दोस्त)! XML XML is an acronym for Extensible Markup Language. With XML, we have a standard way to compose information so that it can be more easily shared. At the same time, it still affords the freedom to structure that information however the heck we want. It's kind of like HTML - only, you get to make up your own tags and attributes. How cool is that? Namespaces Namespaces (aka XML Namespaces) are integral to XML. Namespaces provide a means to qualify the tags and attributes in an XML document with URIs which then makes them truly unique on the Web and thus, universal (among other things). XML Schema XML Schema describes the structure of XML documents just like DTDs, only better. An XML Schema is known as an XML Schema Definition (XSD). Basically, if you're going to use XML to invent your own document structures, XSD provides the way to define your rules (like guidelines) so that people and machines can understand them, adhere to them, and integrate with them. XML Query XML Query (aka XQuery) is a standardized language for combining documents, databases, Web pages and almost anything else. It is very widely implemented, powerful, and easy to learn. XQuery is replacing proprietary middleware languages and Web Application development languages. XQuery is replacing complex Java or C++ programs with a few lines of code. Personally, I think it is sufficient to refer to these foundational items with just a few broad concepts: Unicode, URI, and XML. Unicode gives us a universal system for encoding information in all of the world's writing systems. URI gives us a standard way to identify and locate resources. XML gives us a way to model information uniquely, yet still share it and integrate it in consistent ways. All together, they help us integrate content and services throughout the Web.
Really this should exist with the big S semantics, but it’s here bc the implementation is so light and doesn’t require an inference engine or the use of unambiguous relationships at all. IT is basically using URIs and imbedding structured metadata into the htmlAdds structured metadata to any XML based languageXHTML
LOD is a web initiative for orgs to share information in a rdf or rdfa format. It describes the resource that the URI identifies. This makes it possible for a user (or software agent) to "follow your nose" to find out more information related to the identified resource. -- wikipedia
Citation!
The Web Hypertext Application Technology Working Group (WHATWG) is a community of people interested in evolving HTML
Schema.org is the standards body that is promoting the adoption of the microdata format
Semantic search -- contextual meaning of terms as they appear in the searchable dataspace