A possible future role of schema.org for business reportingsopekmir
The presentation demonstrates a vision for the “reporting extension” that could enhance the processes related to business reporting and the role it could have for the SBR vision.
This document describes Schema.org and its potential uses beyond search engine optimization. Schema.org was created in 2011 by major search engines to provide a set of shared vocabularies for structured data on web pages. It has since grown to include over 2000 terms covering entities, relationships, and actions. The document discusses how Schema.org data can be used for analytics by extracting metadata from web pages and sending it to Google Analytics for additional dimensions and metrics. This enables analysis of user behavior at a more granular level than is normally possible from web analytics alone.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
The document discusses using semantic web technologies to make big data smarter. It provides an overview of key concepts in semantic web, including linked data and ontologies. It describes how semantic web can add structure and meaning to unstructured data through modeling data as graphs and defining relationships and properties. The goal is to publish and query interconnected data at scale to enable new types of queries and inferences over big data.
Big Data and the Semantic Web: Challenges and OpportunitiesSrinath Srinivasa
The document discusses challenges and opportunities at the intersection of big data and the semantic web. It notes that while semantic web technologies can help make sense of large, diverse datasets, building semantic models from big data poses challenges. A global ontology cannot capture all perspectives, and semantic queries rely on contextual relevance and assumptions. Storing and querying large semantic graphs efficiently also presents technological hurdles.
Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues.
Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data.
Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain.
They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable.
But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise.
In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences.
We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.
A possible future role of schema.org for business reportingsopekmir
The presentation demonstrates a vision for the “reporting extension” that could enhance the processes related to business reporting and the role it could have for the SBR vision.
This document describes Schema.org and its potential uses beyond search engine optimization. Schema.org was created in 2011 by major search engines to provide a set of shared vocabularies for structured data on web pages. It has since grown to include over 2000 terms covering entities, relationships, and actions. The document discusses how Schema.org data can be used for analytics by extracting metadata from web pages and sending it to Google Analytics for additional dimensions and metrics. This enables analysis of user behavior at a more granular level than is normally possible from web analytics alone.
Using the Semantic Web Stack to Make Big Data SmarterMatheus Mota
The document discusses using semantic web technologies to make big data smarter. It provides an overview of key concepts in semantic web, including linked data and ontologies. It describes how semantic web can add structure and meaning to unstructured data through modeling data as graphs and defining relationships and properties. The goal is to publish and query interconnected data at scale to enable new types of queries and inferences over big data.
Big Data and the Semantic Web: Challenges and OpportunitiesSrinath Srinivasa
The document discusses challenges and opportunities at the intersection of big data and the semantic web. It notes that while semantic web technologies can help make sense of large, diverse datasets, building semantic models from big data poses challenges. A global ontology cannot capture all perspectives, and semantic queries rely on contextual relevance and assumptions. Storing and querying large semantic graphs efficiently also presents technological hurdles.
Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues.
Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data.
Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain.
They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable.
But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise.
In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences.
We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.
The document discusses ideas for enhancing the LEI infrastructure using semantic technologies. It proposes developing an LEI resolver, adding LEI to schema.org to popularize it, and exploring blockchain technologies for identity services. It outlines MakoLab's LEI resolver, which creates URIs for LEIs and provides visual, machine-readable, and QR code representations when dereferenced. It also discusses plans to develop the GLEIO ontology to represent LEI data and relationships, and explore incorporating LEVEL2 concepts. MakoLab demonstrates a blockchain proof-of-concept for LEIs using Ethereum smart contracts and proposes a potential proof-of-concept for GLEIF.
Industry Ontologies: Case Studies in Creating and Extending Schema.org for In...MakoLab SA
The presentation introduces listeners into the details of the most important global semantic vocabulary build jointly by Google, Yahoo, Microsoft and Yandex: schema.org. It then discusses the experiences related to the creation of “hosted” extensions for the automotive industries (existing: auto.schema.org) and for the financial industries (in making: fibo.schema.org). The two extensions, built by an international team of specialists managed by MakoLab with full respect to the community processes, have two different creation strategies which will be presented and discussed.
The use cases for both vocabularies will be demonstrated. They are related to both “external” business effects (better visibility of the websites using them on the web) and “internal” effects (new kind of analytics and search capacities).
The presentation will also invite to participate to two W3C Community Groups responsible for the open communication activities around the two extensions.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
The document discusses Thomson Reuters' (TR) efforts to build an enterprise content platform to manage their large and growing collection of structured and unstructured data. TR currently stores over 60,000 terabytes of data and processes millions of data points daily. They aim to modernize their infrastructure, break down content silos, and unlock new commercial opportunities through their platform. Their approach involves developing unique entity identifiers, intelligent tagging tools, a knowledge graph, and analytics capabilities. This will allow them to better integrate client data, create new insights, and facilitate innovative uses of their diverse content.
Ethics & (Explainable) AI – Semantic AI & the Role of the Knowledge ScientistStratos Kontopoulos
Presentation for the NexTech Experts Panel II during the NexTech 2021 Congress (https://www.iaria.org/conferences2021/NexTech21.html).
Discusses the emerging and versatile role of the Knowledge Scientist in designing and developing explainable SemanticAI applications.
This document discusses linked data life cycles, including modeling, publishing, discovery, integration, and use cases. It describes key concepts like dataspaces, DSSPs, linked data principles, and the linked open data cloud. Challenges with linked data include schema mapping, write-enablement, authentication, and dataset dynamics as data sources change over time.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
(http://lod2.eu/BlogPost/webinar-series) In this Webinar Michael Martin presents CubeViz - a facetted browser for statistical data utilizing the RDF Data Cube vocabulary which is the state-of-the-art in representing statistical data in RDF. This vocabulary is compatible with SDMX and increasingly being adopted. Based on the vocabulary and the encoded Data Cube, CubeViz is generating a facetted browsing widget that can be used to filter interactively observations to be visualized in charts. Based on the selected structure, CubeViz offer beneficiary chart types and options which can be selected by users.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
LOD2 is a 4-year European Commission project comprising Linked Data researchers and companies from 12 countries. The project aims to integrate Linked Data into existing large-scale applications in media, publishing, corporate intranets, and eGovernment. The webinar series offers monthly free webinars on tools and services for acquiring, editing, composing, connecting, and publishing Linked Data.
How google is using linked data today and vision for tomorrowVasu Jain
In this presentation, I will discuss how modern search engines, such as Google, make use of Linked Data spread inWeb pages for displaying Rich Snippets. Also i will present an example of the technology and analyze its current uptake.
Then i sketched some ideas on how Rich Snippets could be extended in the future, in particular for multimedia documents.
Original Paper :
http://scholar.google.com/citations?view_op=view_citation&hl=en&user=K3TsGbgAAAAJ&authuser=1&citation_for_view=K3TsGbgAAAAJ:u-x6o8ySG0sC
Another Presentation by Author: https://docs.google.com/present/view?id=dgdcn6h3_185g8w2bdgv&pli=1
RDF and OWL are powerful tools for making data smart. RDF uses a simple triple format to represent metadata and link data using unique identifiers, allowing for data integration. OWL builds on RDF by adding more formal semantics and defining concepts, properties, and relationships to allow for automated reasoning and inference over data. Combining OWL and RDF results in smart data that computers can understand, enabling intelligent automation and decision making.
1) Entity-centric data management stores information at the entity level and integrates information by interlinking entities. This provides advantages over keyword-based and relational database approaches.
2) The XI Pipeline extracts mentions from text and performs named entity recognition, entity linking, and entity typing to associate entities with text.
3) Approaches like ZenCrowd and TRank leverage both algorithms and human computation through crowdsourcing to improve entity linking and fine-grained entity typing.
The document discusses a webinar presented by LOD2 on creating knowledge from interlinked data. It describes LOD2 as an EU-funded project involving leading linked open data organizations. The webinar agenda includes discussing SIREn, a plugin for Elasticsearch that allows indexing and searching of JSON documents. It provides an overview of Elasticsearch and describes how to install SIREn, create an index, index documents, and perform searches on nested JSON data.
Within the course, we will present Linked Data as a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the past years, leading to the creation of a global data space that contains many billions of assertions – the Web of Linked Data.
In this Webinar Lorenz Bühmann presents the ontology repair and enrichment tool ORE and also the DL-Learner , a machine learning tool to solve supervised learnings tasks and support knowledge engineers in constructing knowledge. Those two beneighbored tools in the LOD2 Stack are for classification and the following quality analysis of Linked Data.
"Semantic Integration Is What You Do Before The Deep Learning". dev.bg Machine Learning seminar, 13 May 2019.
It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
DCMI Keynote: Bridging the Semantic Gaps and InteroperabilityMike Bergman
M. Bergman's presentation, 'Bridging the Gaps: Adaptive Approaches to Data Interoperabiity,' was a keynote at the DCMI's DC 2010 International Conference in Pittsburgh, PA, on October 22, 2010.
In the presentation, Bergman points to the Dublin Core Metadata Initiative as a unique and key player in plugging the semantics "gap" within the semantic Web. Some specific activities and roles are suggested.
What are the different types of web scraping approachesAparna Sharma
The importance of Web scraping is increasing day by day as the world is depending more and more on data and it will increase more in the coming future. And web applications like Newsdata.io news API that is working on Web scraping fundamentals. More and more web data applications are being created to satisfy the data-hungry infrastructures. And do check out the top 21 list of web scraping tools in 2022
The Web Data Commons Microdata, RDFa, and Microformat Dataset Series @ ISWC2014Robert Meusel
The document describes a series of datasets created by parsing HTML pages to extract structured data in the form of Microdata, RDFa, and Microformats. It provides an overview of the datasets created in 2010, 2012, and 2013, which contain over 30 billion RDF quads extracted from over 1.7 million domains. The datasets are hosted online and provide insights into the usage of different vocabularies and markup languages as well as opportunities for applying and analyzing the large-scale structured web data.
The document discusses ideas for enhancing the LEI infrastructure using semantic technologies. It proposes developing an LEI resolver, adding LEI to schema.org to popularize it, and exploring blockchain technologies for identity services. It outlines MakoLab's LEI resolver, which creates URIs for LEIs and provides visual, machine-readable, and QR code representations when dereferenced. It also discusses plans to develop the GLEIO ontology to represent LEI data and relationships, and explore incorporating LEVEL2 concepts. MakoLab demonstrates a blockchain proof-of-concept for LEIs using Ethereum smart contracts and proposes a potential proof-of-concept for GLEIF.
Industry Ontologies: Case Studies in Creating and Extending Schema.org for In...MakoLab SA
The presentation introduces listeners into the details of the most important global semantic vocabulary build jointly by Google, Yahoo, Microsoft and Yandex: schema.org. It then discusses the experiences related to the creation of “hosted” extensions for the automotive industries (existing: auto.schema.org) and for the financial industries (in making: fibo.schema.org). The two extensions, built by an international team of specialists managed by MakoLab with full respect to the community processes, have two different creation strategies which will be presented and discussed.
The use cases for both vocabularies will be demonstrated. They are related to both “external” business effects (better visibility of the websites using them on the web) and “internal” effects (new kind of analytics and search capacities).
The presentation will also invite to participate to two W3C Community Groups responsible for the open communication activities around the two extensions.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
The document discusses Thomson Reuters' (TR) efforts to build an enterprise content platform to manage their large and growing collection of structured and unstructured data. TR currently stores over 60,000 terabytes of data and processes millions of data points daily. They aim to modernize their infrastructure, break down content silos, and unlock new commercial opportunities through their platform. Their approach involves developing unique entity identifiers, intelligent tagging tools, a knowledge graph, and analytics capabilities. This will allow them to better integrate client data, create new insights, and facilitate innovative uses of their diverse content.
Ethics & (Explainable) AI – Semantic AI & the Role of the Knowledge ScientistStratos Kontopoulos
Presentation for the NexTech Experts Panel II during the NexTech 2021 Congress (https://www.iaria.org/conferences2021/NexTech21.html).
Discusses the emerging and versatile role of the Knowledge Scientist in designing and developing explainable SemanticAI applications.
This document discusses linked data life cycles, including modeling, publishing, discovery, integration, and use cases. It describes key concepts like dataspaces, DSSPs, linked data principles, and the linked open data cloud. Challenges with linked data include schema mapping, write-enablement, authentication, and dataset dynamics as data sources change over time.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
(http://lod2.eu/BlogPost/webinar-series) In this Webinar Michael Martin presents CubeViz - a facetted browser for statistical data utilizing the RDF Data Cube vocabulary which is the state-of-the-art in representing statistical data in RDF. This vocabulary is compatible with SDMX and increasingly being adopted. Based on the vocabulary and the encoded Data Cube, CubeViz is generating a facetted browsing widget that can be used to filter interactively observations to be visualized in charts. Based on the selected structure, CubeViz offer beneficiary chart types and options which can be selected by users.
If you are interested in Linked (Open) Data principles and mechanisms, LOD tools & services and concrete use cases that can be realised using LOD then join us in the free LOD2 webinar series!
The Power of Semantic Technologies to Explore Linked Open DataOntotext
Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity.
The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by:
- graphically explorinh the connectivity patterns in big datasets;
- building new links between identical entities residing in different data silos;
- getting insights of what type of queries can be run against various linked data sets;
- reliably filtering information based on relationships, e.g., between people and organizations, in the news;
- demonstrating the conversion of tabular data into RDF.
Learn more at http://ontotext.com/.
LOD2 is a 4-year European Commission project comprising Linked Data researchers and companies from 12 countries. The project aims to integrate Linked Data into existing large-scale applications in media, publishing, corporate intranets, and eGovernment. The webinar series offers monthly free webinars on tools and services for acquiring, editing, composing, connecting, and publishing Linked Data.
How google is using linked data today and vision for tomorrowVasu Jain
In this presentation, I will discuss how modern search engines, such as Google, make use of Linked Data spread inWeb pages for displaying Rich Snippets. Also i will present an example of the technology and analyze its current uptake.
Then i sketched some ideas on how Rich Snippets could be extended in the future, in particular for multimedia documents.
Original Paper :
http://scholar.google.com/citations?view_op=view_citation&hl=en&user=K3TsGbgAAAAJ&authuser=1&citation_for_view=K3TsGbgAAAAJ:u-x6o8ySG0sC
Another Presentation by Author: https://docs.google.com/present/view?id=dgdcn6h3_185g8w2bdgv&pli=1
RDF and OWL are powerful tools for making data smart. RDF uses a simple triple format to represent metadata and link data using unique identifiers, allowing for data integration. OWL builds on RDF by adding more formal semantics and defining concepts, properties, and relationships to allow for automated reasoning and inference over data. Combining OWL and RDF results in smart data that computers can understand, enabling intelligent automation and decision making.
1) Entity-centric data management stores information at the entity level and integrates information by interlinking entities. This provides advantages over keyword-based and relational database approaches.
2) The XI Pipeline extracts mentions from text and performs named entity recognition, entity linking, and entity typing to associate entities with text.
3) Approaches like ZenCrowd and TRank leverage both algorithms and human computation through crowdsourcing to improve entity linking and fine-grained entity typing.
The document discusses a webinar presented by LOD2 on creating knowledge from interlinked data. It describes LOD2 as an EU-funded project involving leading linked open data organizations. The webinar agenda includes discussing SIREn, a plugin for Elasticsearch that allows indexing and searching of JSON documents. It provides an overview of Elasticsearch and describes how to install SIREn, create an index, index documents, and perform searches on nested JSON data.
Within the course, we will present Linked Data as a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the past years, leading to the creation of a global data space that contains many billions of assertions – the Web of Linked Data.
In this Webinar Lorenz Bühmann presents the ontology repair and enrichment tool ORE and also the DL-Learner , a machine learning tool to solve supervised learnings tasks and support knowledge engineers in constructing knowledge. Those two beneighbored tools in the LOD2 Stack are for classification and the following quality analysis of Linked Data.
"Semantic Integration Is What You Do Before The Deep Learning". dev.bg Machine Learning seminar, 13 May 2019.
It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.
DCMI Keynote: Bridging the Semantic Gaps and InteroperabilityMike Bergman
M. Bergman's presentation, 'Bridging the Gaps: Adaptive Approaches to Data Interoperabiity,' was a keynote at the DCMI's DC 2010 International Conference in Pittsburgh, PA, on October 22, 2010.
In the presentation, Bergman points to the Dublin Core Metadata Initiative as a unique and key player in plugging the semantics "gap" within the semantic Web. Some specific activities and roles are suggested.
What are the different types of web scraping approachesAparna Sharma
The importance of Web scraping is increasing day by day as the world is depending more and more on data and it will increase more in the coming future. And web applications like Newsdata.io news API that is working on Web scraping fundamentals. More and more web data applications are being created to satisfy the data-hungry infrastructures. And do check out the top 21 list of web scraping tools in 2022
The Web Data Commons Microdata, RDFa, and Microformat Dataset Series @ ISWC2014Robert Meusel
The document describes a series of datasets created by parsing HTML pages to extract structured data in the form of Microdata, RDFa, and Microformats. It provides an overview of the datasets created in 2010, 2012, and 2013, which contain over 30 billion RDF quads extracted from over 1.7 million domains. The datasets are hosted online and provide insights into the usage of different vocabularies and markup languages as well as opportunities for applying and analyzing the large-scale structured web data.
Schema.fiware.org: FIWARE Harmonized Data ModelsFIWARE
Schema.fiware.org: FIWARE Harmonized Data Models presentation, by Jose Manuel Cantera Fonseca.
How-to sessions. 1st FIWARE Summit, Málaga, Dec. 13-15, 2016.
Information Management & Sharing in Digital Era Liaquat Rahoo
The document discusses essential skills for information professionals in the digital era, including various information management and sharing tools. It covers websites like static and dynamic sites, as well as software tools including web-based and desktop software. Specific tools covered include UNESCO information storage and retrieval tools, configuring a library catalog using the Library of Congress Z39.50 server, and using Google Drive on desktop computers. The objectives are to learn about various information management tools and technologies useful for information professionals.
What is the current status quo of the Semantic Web as first mentioned by Tim Berners Lee in 2001?
Not only 10 blue links can drive you traffic anymore, Google has added many so called Knowlegde cards and panels to answer the specific informational need of their users. Sounds complicated, but it isn’t. If you ask for information, Google will try to answer it within the result pages.
I'll share my research from a theoretical point of view through exploring patents and papers, and actual testing cases in the live indices of Google. Getting your site listed as the source of an Answer Card can result in an increase of CTR as much as 16%. How to get listed? Come join my session and I'll shine some light on the factors that come into play when optimizing for Google's Knowledge graph.
The document contains a presentation about MarkLogic and its capabilities. It discusses how MarkLogic can be used for DITA authoring and publishing, content management systems, dynamic content delivery across channels, and healthcare applications. It provides examples of customers like Elsevier, MModal, and ICA Informatics that use MarkLogic for applications like medical record search, clinical decision support, and health information exchange. The presentation also covers MarkLogic capabilities for search, analytics, social applications, and multi-channel delivery of information to users.
Google announced in May 2009 that it would parse microformats like hCard, hReview, and hProduct from webpages and display structured information from those pages in search results. This included information about people, reviews, products, businesses, and more. Over time, Google expanded support for additional metadata standards and formats. In 2011, Google, Bing, and Yahoo introduced Schema.org as a shared vocabulary for annotating pages with microdata, covering many domains. While Schema.org and microdata are useful for businesses, their long term success is still unclear as other formats like RDFa remain viable options.
Overview of modern software ecosystem for big data analysisMichael Bryzek
Brief summary of modern software available today to provide the core infrastructure to provide collection and analysis of big data collected from sensors (internet of everything). Presented at the Dec 2015 Trillion Sensors Summit in Orlando FL.
This document provides a summary of a presentation on using jQuery with SharePoint. It discusses:
1) Why jQuery is useful for SharePoint - it allows dynamic updates without custom code, improves visuals and usability, and can work around limitations like the list view threshold.
2) The basics of using jQuery with SharePoint, including common methods to interact with elements, attributes, and SharePoint list data via APIs.
3) Best practices for jQuery development, such as putting code in document ready functions, debugging techniques, and chaining methods to concisely select and update elements.
Technologies and Innovation – The Internet of ValueLee Schlenker
The document discusses digital technologies and innovation, including the building blocks of innovation, digital economics, and the internet of value. It covers topics like the data revolution, time and space organization, analytical methods, and decision making with data ethics. The agenda includes an introduction and sessions on these various topics related to digital innovation.
This document discusses developing a content model for machine-actionable links to enable hypermedia applications. It reviews current web architecture and link usage. Example scenarios for data discovery, access, and processing are examined. An initial proposal is made for a link content model that includes properties like link, title, type, rel, overlayAPI, template, and profile. The goal is to allow hypermedia applications to direct machine agents through semantic links. Further development of vocabularies and example links is suggested.
Ashok Kumar Subramaniam has over 12 years of experience as a project lead and senior developer working on web applications using .NET technologies. He has extensive experience developing applications for industries such as insurance, healthcare, and telecommunications. Some of his responsibilities include requirements analysis, design, development, testing, and maintenance of applications as well as leading teams and coordinating with clients.
Domino and AWS: collaborative analytics and model governance at financial ser...Domino Data Lab
The document discusses how financial services firms use analytics for tasks like predictive modeling, validation, pricing, and research. It notes the challenges of legacy systems, collaboration across teams, and reproducibility. It then provides an example of how DBRS, a credit rating agency, uses Domino and AWS for securitization analysis. Models are developed in Jupyter notebooks and governed via a GitHub repository, with analysts interacting through Excel/R Shiny frontends on Domino. This allows for an auditable, scalable, and collaborative workflow while developers maintain control. The document concludes that collaborative platforms like Domino enable subject matter experts to focus on models rather than infrastructure.
Slides from a webinar on webware presented by Mike Qaissaunee and Gordon F. Snyder, Jr. (both of nctt.org). The webinar was hosted by MATEC NetWorks (http://www.matecnetworks.org/) and delivered via Elluminate. Visit MATEC NetWorks to watch the webinar.
Open Source, The Natural Fit for Content Management in the EnterpriseMatt Hamilton
This is a talk I gave at "Adopting Open Source Software within the corporate ICT strategy" in London on 5th December 2013.
* How OSS reduces long term risk for CM
* Integrating with the unknown
* Authentication in heterogeneous environments
* Case study - NHS Health and Social Care Information Centre Intranet
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
The document discusses web services and related technologies. It provides background on web services, describing them as modular applications that can be published, located, and invoked across the web. It also discusses technologies related to web services, such as XML, SOAP, WSDL, UDDI, and REST. The document contains sections on introduction, context, building blocks, and challenges related to web services.
How to Optimize Your Drupal Site with Structured ContentAcquia
<p>With the advent of real-time marketing technologies and design methodologies like atomic design, web pages are no longer just “pages” – they are collections of modular, dynamic data that can be rearranged according to the context of the user.</p>
<p>To provide optimized user experiences, marketers and publishers need to enrich websites with additional structure (taxonomy and metadata). By adding metadata, content becomes machine-understandable, which leads to better interoperability, SEO, and accessibility.</p>
<p>Structured content is also one of the foundations of real-time personalization; By tagging and describing content with metadata, personalization engines like Acquia Lift can provide more relevant content to individual users.</p>
<p>In this webinar, we will discuss:</p>
<ul>
<li>How to further enrich your Drupal website with structure</li>
<li>Taxonomy best practices for dynamic content and how to configure auto-tagging in your Drupal site</li>
<li>How to leverage Microdata and the schema.org vocabulary to improve SEO through rich results</li>
<li>How to improve the social shareability of your content through the use of Twitter Cards and OpenGraph tags</li>
<li>Why Drupal 8 is the best CMS platform for managing structured content</li>
</ul>
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2. • It is impossible to forget that Semantic Web
and „Semantics” as we use it in our track title,
owes much to Sir Tim Berners Lee –
the inventor of Web and the Semantic Web
• Tim received yesterday ACM Turing Award,
nicked named: Nobel of Computing
3. • Schema.org (2011), sponsored by the most important search engines: Google,
Microsoft, Yahoo and Yandex, is a large scale collaborative activity with a mission to
create, maintain, and promote schemas for structured data on the WEB pages and
beyond.
• It contains more than 2000 terms: 753 types, 1207 properties and 220 enumerations.
• Schema.org covers entities, relationships between entities and actions.
• Today, about 15 million sites use Schema.org. Random yet representative crawls (Web
Data Commons) show that about 30% of URLs on the web return some form of triples
from schema.org.
• Many applications from Google (Knowledge Graph), Microsoft (like Cortana), Pinterest,
Yandex and others already use schema.org to power rich experiences.
• Think of schema.org as a global Vocabulary for the web transcending domain and
language barriers.
5. • The Industry Ontologies are the subclass of the DOMAIN ONTOLOGIES.
• They are created to represent concepts that are used in a given industry
• They define valid meanings of concepts that are used in the industry
• The essential character of the Industry Ontologies is pragmatism – they
must be useful, practical and easy to use.
• Some examples of the Industrial Ontologies:
FIBO (Finance), GoodRelations (e-commerce), VVO (Volkswagen Vehicle Ontology), UCO (Used
Cars Ontology), GSPAS Ontology (Ford Ontology for Global Study Process Allocation System),
POPE (Purdue Ontology for Pharmaceutical Engineering) …
7. Design Decisions
• „The driving factor in the design of
Schema.org was to make it easy for
webmasters to publish their data. In
general, the design decisions place more
of the burden on consumers of the
markup.”
R.V. GUHA, D. DAN BRICKLEY, S. MACBETH – „Schema.org
- Evolution of Structured Data on the Web”
Data Model
• Derived from RDFS (RDF Schema)
• Multiple inheritance hierarchy
• POLYMORPHIC PROPERTIES - Each property
may have one or more types as its domain
and its range („domainincludes” and
„rangeincludes”)
8. Usage models
• Under full control of site/messages/data
publishers
• Data EMBEDDED into page, data
representation or into message markup
(HTML, XML)
• Harvested during standard crawling,
message or data processing
Serializations
• RDFa - CANONICAL
• Microdata (native to HTML5)
• JSON-LD
9. Extension mechanism: sequence of specificity
CORE HOSTED EXTENSIONS EXTERNAL EXTENSIONS
CORE – „Core, basic vocabulary for describing the kind of entities the most common web
applications need”*
(Built by schema.org team, extended by proposals from community, managed by a community
process with the leading role of schema.org steering committee.)
HOSTED/REVIEWED EXTENSIONS – Domain specific basic vocabularies. The hosted extensions
are reviewed, versioned and published as part of schema.org itself to ensure consistency with the
core and its flat namespace. (Built by the specific interest groups respecting the community
process, reviewed by the schema.org community and approved by the steering committee).
EXTERNAL EXTENSIONS – More specialized, fully independent domain specific vocabularies.
Built by a third party. May go through a feedback process, yet they are hosted and controlled by
the third party to serve its specific application needs.
* http://schema.org/docs/extension.html
11. Examples - MICRODATA
div itemscope itemtype="http://schema.org/BankTransfer">
<h1>If you want to donate</h1>
Send <span itemprop="amount" itemscope itemtype="http://schema.org/MonetaryAmount">
<span itemprop="amount">30</span>
<span itemprop="currency" content="USD">$</span>
</span>
via bank transfer to the <span itemprop="beneficiaryBank">European ExampleBank, London</span>
Put "<i itemprop="name">Donate wikimedia.org</i>" in the transfer title.
</div>
12. Examples - RDFa
<div vocab="http://schema.org" typeof="BankTransfer">
<h1>If you want to donate</h1>
Send <span property="amount" typeof="MonetaryAmount">
<span property="amount">30</span>
<span property="currency" content="USD">$</span>
</span>
via bank transfer to the <span property="beneficiaryBank">European ExampleBank,London</span>
Put "<i property=’name’>Donate wikimedia.org</i>" in the transfer title.
</div>
15. Automotive Extension
• Extension URI: auto.schema.org
• Designed as the first phase of the GAO project
(Generic Automotive Ontology -
http://automotive-ontology.org)
• First step: extending core vocabulary by a
minimal set of new terms (May 2015)
• Second step: creating auto.schema.org hosted
extension (May 2016)
• Third step: creating POC of the external
extension (March 2017)
Financial extension
• Extension URI: fibo.schema.org
• Inspiration from FIBO project (Financial
Industry Business Ontology – http://fibo.org )
• Going through BOC (Bag-Of-Concept) phase
and using an „Occam Razor” approach.
• First step: extending core vocabulary by a
minimal set of new terms (May 2016)
• Second step: creating fibo.schema.org hosted
extension (published in pending.schema.org
(March 2017))
• Third step: creating POC of the external
extension (March 2017)
16. May 13, 2015
– official introduction
of the Automotive extension
to schema.org
Collaborative project
of Hepp Research GmbH,
MakoLab SA
and many other individuals.
17. … can now be brought to the Web
with the auto.schema.org extension:
See: http://carinsearch.org
for more information
18. • Extension URI:
http://ontologies.makolab.com/gao/
• Based on GAO project (Generic
Automotive Ontology) ontology
• More than 300 classes and 40
properties
• Used to drive SMART search for
an automotive client
• See:
http://ontologies.makolab.com/gao/CarUsageType
http://ontologies.makolab.com/gao/ActiveOrPassiveSafetySystem
19. Extension of the core vocabulary
by a minimal set of new terms
(May 2016)
The hosted extension (published
March 2017) as
pending.schema.org
Collaborative project
of an international group of individuals
lead by MakoLab SA.
Described in: http://schema.org/docs/financial.html
20. The financial extension of schema.org
refers to the most important real world
objects related to banks and financial
institutions:
• A bank and its identification
mechanism
• A financial product
• An offer to the client
• Described in:
http://schema.org/docs/financial.html
Thing CLASSES
Action
TransferAction
MoneyTransfer
Intangible
Service
FinancialProduct
BankAccount
DepositAccount
CurrencyConversionService
InvestmentOrDeposit
BrokerageAccount
DepositAccount
InvestmentFund
LoanOrCredit
CreditCard
MortgageLoan
PaymentCard +
PaymentService
StructuredValue
ExchangeRateSpecification
MonetaryAmount
RepaymentSpecification
21. The financial extension of schema.org
refers to the most important real world
objects related to banks and financial
institutions:
• A bank and its identification
mechanism
• A financial product
• An offer to the client
• Described in:
http://schema.org/docs/financial.html
Thing PROPERTIES
Property
annualPercentageRate
feesAndCommissionsSpecification
interestRate
identifier
leiCode
duration
loanTerm
requiredCollateral
accountMinimumInflow
accountOverdraftLimit
amount
bankAccountType
beneficiaryBank
cashBack
contactlessPayment
currency
currentExchangeRate
domiciledMortgage
downPayment
earlyPrepaymentPenalty
exchangeRate
exchangeRateSpread
floorLimit
gracePeriod
loanMortgageMandateAmount
loanPaymentAmount
loanPaymentFrequency
loanRepaymentForm
loanType
monthlyMinimumRepaymentAmount
numberOfLoanPayments
recourseLoan
renegotiableLoan
23. • Extension URI:
http://fibo.org/voc/
• Based on FIBO project
(Financial Industry Business
Ontology) ontology –
Business Entities
• Used in the POC for SEO,
analytics and search.
24. • Flat namespace (moderate requirement)
• schema.org views (showing super- and sub- types for a given type, showing
properties that can be used)
• References to schema.org for common types and properties
• URI stability and persistence
• Good taxonomy
• Good and comprehensive labels
• Not many restrictions, e.g. property polymorphism not required
Many ontologies can qualify for the transformation !!!
25. The Web Structured Data Revolution
Knowledge Graphs, Rich Snippets,
Conversational Search, Info Boxes, Knowledge Panels,
Semantic Search, Answer Boxes, RankBrain,
Semantic SEO, Rich Cards, Enhanced Analytics
and more …
26.
27. I. DATA analytics for Websites using schema.org
II. Intelligent/Smart search based on schema.org markup
III. Enterprise taxonomies & vocabularies
• Work for both Intra-, Extra- and Inter-net portals
• Does not need Google to cooperate
• Not limited to „core” or „hosted extensions”
• Works with all serializations, but the easiest is JSON-LD.
• Minimal skills required to create relevant markup
28.
29. Markup in
website’s code
• Schema.org
or external
extension
Google Tag
Manager*
• Additional
setup
Google
Analytics**
• Additional
Dimensions
and Metrics
How does it work?
* Other Tag Managers possible
** Other analytics platforms possible
30. Proof-Of-Concept:
Auto
Model 1
- Name
- Brand
Version1
Model,
fuelConsumption,
fuelType,
numberOfDoors, Color
Version 2
Version 3
Model 2
- Name
- Brand
Version 1
Version 2
Version 3
Model 3
- Name
- brand
Version 1
Version 2
Version 3
36. • Mark your product data
with schema.org markup
• Run the smart Search Crawler
for an Enterprise Website
• Check for schema.org
markup (Microdata or JSON-LD)
• When markup found, create
property map and assign values
• Display enhanced search results
39. The real values taken from existing data found
by crawler within the marked website pages
40.
41.
42. • External extensions to schema.org
are ideal for exposing enterprise
taxonomies
• OWL ontologies can be “projected”
onto external schema.org format
• No loss of ontology expressivity
• The best example: “GS1 Web
Vocabulary” http://gs1.org/voc/
• GAO, FIBO external extension POCs
“A well-constructed enterprise taxonomy is central to multiple
business functions, including Business Intelligence, Content Strategy a
nd Management, Digital Asset Management, Knowledge Management,
and User Experience.” Strategic Content (http://strategiccontent.com)
43.
44. • Schema.org is an extensible framework to build (convert) industrial ontologies
• Extremely easy to use
• It’s principal use is to enable Structured Data Revolution
• It can also be used for an enterprise’s own needs:
• Enhancing enterprise data quality and meaning by delivering easy to use
vocabulary/taxonomy solution
• Enabling data analytics
• Enabling smart search
• External extensions to schema.org can be used to express most of the industrial
ontologies (easy to match requirements)
• Bridges the gap between enterprise data formats and public web data
45. Robert Trypuz
MakoLab SA
Rzgowska 30
93-172 Łódź
Poland
robert.trypuz@makolab.com
Dominik Kuziński
MakoLab SA
Rzgowska 30
93-172 Łódź
Poland
dominik.kuzinski@makolab.com
MakoLab USA Inc.
20 West University Ave.,
Gainesville, FL 32601
USA
+1 551 226 5488
MakoLab SA
Demokratyczna 46
93-430 Lodz
Poland
+48 600 814 537
Dr Mirek Sopek
sopek@makolab.com