Practical Applications of Semantic Web in Retail -- Semtech 2014 Jay Myers
This presentation explores a year of experimentation, POCs, and research of Linked Data and Semantic Web technologies within Best Buy. It highlights three use cases where a small team approached internal data problems with semantics -- reviewing high level technical methodologies, application details, and technical and business metrics (ROI, conversion, etc.)
Transforming your application with ElasticsearchBrian Ritchie
Brian Ritchie will give a presentation on transforming applications with Elastic Search. Elastic Search is an open source, distributed search and analytics engine that can be used to add powerful search capabilities to applications. It allows storing and searching large volumes of data quickly and flexibly scales. The presentation will cover introducing Elastic Search, bringing application data into it, security considerations, and an example of putting it all together to build a searchable application using Elastic Search.
The document discusses purpose-built databases and how developers need to be able to use multiple databases within their applications. It provides examples of companies like Airbnb and Expedia using different databases for different purposes. The rest of the document outlines common data models, use cases, and Amazon database offerings and provides a demo and additional resources.
Semantic seo and the evolution of queriesBill Slawski
This document summarizes how Google search results are evolving to include more semantic data through direct answers, structured snippets, and rich snippets. It provides examples of direct answers being extracted from authoritative sources using natural language queries and intent templates. It also discusses how including structured data like tables, schemas, and markup can help search engines understand and display page content in a more standardized way. While knowledge-based trust is an interesting concept, current search ranking still primarily relies on link analysis and does not consider factual correctness.
This document provides an introduction to linked data and the semantic web. It discusses how the current web contains documents that are difficult for computers to understand, but linked data publishes structured data on the web using common standards like RDF and URIs. This allows data to be interlinked and queried using SPARQL. Publishing data as linked data makes the web appear as one huge global database. There are now many incentives for organizations to publish their data as linked data, as it enables data sharing and integration in addition to potential benefits like semantic search engine optimization. Linked data is a growing trend with many large organizations and governments now publishing data.
This document discusses different search strategies for finding information, including quick searches, building blocks searches, and pearl-growing searches. It compares searching Google versus academic library resources, noting databases and library guides can help find full-text sources. Boolean operators and other search tips are provided to help refine searches. The document also discusses keeping up to date using alerts and RSS feeds.
The document provides tips and tricks for various tasks related to online searching, including:
1) Conducting date-range searches on search engines to find older webpages, though determining the true date of a webpage can be challenging.
2) Using tools like GooFresh to search for websites added on specific dates.
3) Finding expert sources on topics by searching databases and directories of experts.
4) Capturing screenshots using applications like Snag IT.
5) Searching news archives on platforms like Google News.
Linked Data is a set of best practices for publishing data on the Web using standardized data models (RDF) and access methods (HTTP), enabling easier integration of data from different sources compared to proprietary APIs. The Linked Data architecture is open and allows discovery of new data sources at runtime, allowing applications to take advantage of new available data. When publishing Linked Data, considerations include linking to other datasets, and providing provenance, licensing, and access metadata using common vocabularies. Linked Data principles can also be applied within intranets for data integration.
Practical Applications of Semantic Web in Retail -- Semtech 2014 Jay Myers
This presentation explores a year of experimentation, POCs, and research of Linked Data and Semantic Web technologies within Best Buy. It highlights three use cases where a small team approached internal data problems with semantics -- reviewing high level technical methodologies, application details, and technical and business metrics (ROI, conversion, etc.)
Transforming your application with ElasticsearchBrian Ritchie
Brian Ritchie will give a presentation on transforming applications with Elastic Search. Elastic Search is an open source, distributed search and analytics engine that can be used to add powerful search capabilities to applications. It allows storing and searching large volumes of data quickly and flexibly scales. The presentation will cover introducing Elastic Search, bringing application data into it, security considerations, and an example of putting it all together to build a searchable application using Elastic Search.
The document discusses purpose-built databases and how developers need to be able to use multiple databases within their applications. It provides examples of companies like Airbnb and Expedia using different databases for different purposes. The rest of the document outlines common data models, use cases, and Amazon database offerings and provides a demo and additional resources.
Semantic seo and the evolution of queriesBill Slawski
This document summarizes how Google search results are evolving to include more semantic data through direct answers, structured snippets, and rich snippets. It provides examples of direct answers being extracted from authoritative sources using natural language queries and intent templates. It also discusses how including structured data like tables, schemas, and markup can help search engines understand and display page content in a more standardized way. While knowledge-based trust is an interesting concept, current search ranking still primarily relies on link analysis and does not consider factual correctness.
This document provides an introduction to linked data and the semantic web. It discusses how the current web contains documents that are difficult for computers to understand, but linked data publishes structured data on the web using common standards like RDF and URIs. This allows data to be interlinked and queried using SPARQL. Publishing data as linked data makes the web appear as one huge global database. There are now many incentives for organizations to publish their data as linked data, as it enables data sharing and integration in addition to potential benefits like semantic search engine optimization. Linked data is a growing trend with many large organizations and governments now publishing data.
This document discusses different search strategies for finding information, including quick searches, building blocks searches, and pearl-growing searches. It compares searching Google versus academic library resources, noting databases and library guides can help find full-text sources. Boolean operators and other search tips are provided to help refine searches. The document also discusses keeping up to date using alerts and RSS feeds.
The document provides tips and tricks for various tasks related to online searching, including:
1) Conducting date-range searches on search engines to find older webpages, though determining the true date of a webpage can be challenging.
2) Using tools like GooFresh to search for websites added on specific dates.
3) Finding expert sources on topics by searching databases and directories of experts.
4) Capturing screenshots using applications like Snag IT.
5) Searching news archives on platforms like Google News.
Linked Data is a set of best practices for publishing data on the Web using standardized data models (RDF) and access methods (HTTP), enabling easier integration of data from different sources compared to proprietary APIs. The Linked Data architecture is open and allows discovery of new data sources at runtime, allowing applications to take advantage of new available data. When publishing Linked Data, considerations include linking to other datasets, and providing provenance, licensing, and access metadata using common vocabularies. Linked Data principles can also be applied within intranets for data integration.
Deciding which of the many database options to choose from in Azure can be overwhelming. There are many options, and it’s impossible for everyone to know all of them. Traditionally, the choice has been to select which relational database to choose. But with all the NoSql databases available, there are many more choices that may be a better fit for your application. What are the trade offs among all the choices? Why pick just one? I will give some practical examples of how to combine different types of databases. Microsoft released Document DB a couple of years ago, which was their first managed NoSql cloud database. Just recently Cosmos DB has expanded those offerings and made it easier than ever to use. Cosmos DB is a service that contains several types of databases: Relational, Key Value Pair, Document and Graph. I will explain what each of these are, along with some code samples for each one to get you started. You will leave this session with a greater understanding of the different types of NoSql databases and what kinds of problems each of them solves best.
This document discusses semantic search and how thesauri can improve search experiences. It describes different types of semantic searches and demands for smarter searches. PoolParty Semantic Search is presented as a solution that leverages thesauri to provide auto-complete, query expansion, faceted search, and integration of linked data from multiple sources. A live demo of PoolParty Semantic Search is available online.
Search engines like Google, Bing, and Yahoo use algorithms and spider programs to index the web and provide search results. Keywords or search terms are important for effective searches. More specific search terms will provide better results. Boolean operators like AND, OR, and NOT can be used to combine search terms and limit or expand search results.
Search engines are designed to help users find information stored digitally. They aim to minimize the time and amount of information needed to find what users are looking for. Major methods of information retrieval for search engines include Boolean, vector space model, probabilistic, and meta search. Designing the perfect search engine requires dealing with challenges like the web's huge and constantly changing document set that is loosely organized through hyperlinks. Effective search requires components like crawlers to discover pages, repositories to store them, indexes for efficient searching, and ranking algorithms to order results.
This document provides an overview of how to effectively search for information using Google search. It discusses formulating search queries, using Boolean operators and search modifiers, filtering search results, and utilizing advanced search features. Examples of search engines, operators, and modifiers are given. Tips are provided for analyzing topics, using synonyms, describing needs concisely, and quoting phrases. Methods for saving useful websites located through searches are also outlined.
This document discusses changes in search engine optimization (SEO) and how to cut through noise. It summarizes patents related to ranking news articles over time and how they show changes in what signals are used to evaluate news sources. It recommends optimizing content for things and voice search by adding structured data for entities and speakable schema to help digital assistants answer questions about the content. Additional reading on entity-oriented search, voice search, and leaving no valuable data behind is also provided.
Introduction to Elasticsearch for Business Intelligence and Application InsightsData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Elasticsearch for Business Intelligence and Application Insights
Speaker: Sean Donnelly
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights.
Summary of a course on how to find information on the Web. People usually do not search in a systematic way and mostly rely upon intuition.
This presentation provides a guideline on how to find information taking into account various ways.
Vrinda Davda, Rakesh Maski & Nicholas DiPiazza, Lucidworks. Presentation from ACTIVATE 2019, the Search and AI Conference. http://www.activate-conf.com
These are slides of a tutorial at ECIR by Gerard de Melo and Katja Hose.
Search is currently undergoing a major paradigm shift away from the traditional document-centric “10 blue links” towards more explicit and actionable information. Recent advances in this area are Google’s Knowledge Graph, Virtual Personal Assistants such as Siri and Google Now, as well as the now ubiquitous entity-oriented vertical search results for places, products, etc. Apart from novel query understanding methods, these developments are largely driven by structured data that is blended into the Web Search experience. We discuss efficient indexing and query processing techniques to work with large amounts of structured data. Finally, we present query interpretation and understanding methods to map user queries to these structured data sources.
This document provides an overview of search engines and how they work. It discusses the major components of search engines including spiders that crawl websites to index their content, the indexing process that analyzes websites and stores essential information, and the search engine program that matches user queries to indexed content. It also describes common search options available on most search engines like phrase searching, boolean operators, and searching by date or file type. Finally, it discusses related tools like meta search engines, desktop search programs, and ways to stay up-to-date on search engine developments.
Google began in 1996 as a research project by Larry Page and Sergey Brin. It uses PageRank, an algorithm that assigns websites a numerical weight based on the number and quality of links to it, to index websites and determine search results. When a user searches on Google, it examines its index of websites and returns the most relevant results based on the user's search terms and PageRank. Google crawls the web with its bots to constantly update and improve its index of websites to provide the most useful search results to users.
The document provides tips and strategies for effectively searching the internet to find needed information. It discusses using advanced search features like Boolean operators, phrase searching with quotation marks, and limiting searches to specific domains. Search engines like Google index websites differently than directories. Refining searches with operators, phrases, and domain limits can help attract the "needle" of needed information from the large "haystack" of the internet.
This document discusses Calais, a semantic metadata generation service that extracts entities, facts, and events from unstructured text. It provides examples of how Calais is currently being used and proposes some potential applications of Calais in digital advertising, including context-driven ad placement, topic hubs and microsites, mashup ads, and contextual customer profiling based on behavioral data.
Advertising with Linked Data in Web ContentMartin Hepp
Advertising with Linked Data in Web Content: From Semantic SEO to E-Commerce on the Web 3.0
Slides and audio from my talk given at the Knowledge Engineering Group of the University of Economics Prague.
http://keg.vse.cz/seminar.php?datetime=2011-04-06
William slawski-google-patents- how-do-they-influence-searchBill Slawski
Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.
MongoDB and Hadoop: Driving Business InsightsMongoDB
This document discusses using MongoDB and Hadoop together to drive business insights. It provides an overview of the evolving data landscape, with Hadoop used for large datasets and analytics and MongoDB used for operational workloads. Example use cases shown are combining MongoDB for real-time applications with Hadoop for analysis in domains like commerce, insurance, and fraud detection. The MongoDB Connector for Hadoop is described, allowing MongoDB to act as a data source and sink for tools like MapReduce, Pig, Hive, and Spark. A demo is shown of a movie recommendation application that uses Spark running on Hadoop to generate recommendations from a MongoDB dataset and store the results back in MongoDB.
This document provides information on how to effectively search for information online. It discusses the differences between general search engines and databases, when each is most appropriate to use, and how search engines work. It also provides tips for using search tools like Boolean operators, phrase searching, and limiting searches. The document recommends developing search strategies and having a plan when searching for academic or project-related information.
This document discusses different ways to extend semantics on the web through microdata, microformats, RDFa, and schema.org. It explains the basic syntax for using microdata to embed machine-readable data in HTML documents. Microdata provides a simple way to do this while being standardized in HTML5. It also recommends using schema.org as a unified vocabulary for semantic markup.
This document summarizes a presentation on data feed SEO. It discusses how data feeds are not unique content, the potential "affiliate penalty", and generating unique content matrices. It also provides a case study on automatically generating product descriptions and discusses different types of user generated content. Finally, it lists various data sources and APIs that can be used to build quick SEO tools and provides some resources on site architecture and leveraging outsourced labor.
Deciding which of the many database options to choose from in Azure can be overwhelming. There are many options, and it’s impossible for everyone to know all of them. Traditionally, the choice has been to select which relational database to choose. But with all the NoSql databases available, there are many more choices that may be a better fit for your application. What are the trade offs among all the choices? Why pick just one? I will give some practical examples of how to combine different types of databases. Microsoft released Document DB a couple of years ago, which was their first managed NoSql cloud database. Just recently Cosmos DB has expanded those offerings and made it easier than ever to use. Cosmos DB is a service that contains several types of databases: Relational, Key Value Pair, Document and Graph. I will explain what each of these are, along with some code samples for each one to get you started. You will leave this session with a greater understanding of the different types of NoSql databases and what kinds of problems each of them solves best.
This document discusses semantic search and how thesauri can improve search experiences. It describes different types of semantic searches and demands for smarter searches. PoolParty Semantic Search is presented as a solution that leverages thesauri to provide auto-complete, query expansion, faceted search, and integration of linked data from multiple sources. A live demo of PoolParty Semantic Search is available online.
Search engines like Google, Bing, and Yahoo use algorithms and spider programs to index the web and provide search results. Keywords or search terms are important for effective searches. More specific search terms will provide better results. Boolean operators like AND, OR, and NOT can be used to combine search terms and limit or expand search results.
Search engines are designed to help users find information stored digitally. They aim to minimize the time and amount of information needed to find what users are looking for. Major methods of information retrieval for search engines include Boolean, vector space model, probabilistic, and meta search. Designing the perfect search engine requires dealing with challenges like the web's huge and constantly changing document set that is loosely organized through hyperlinks. Effective search requires components like crawlers to discover pages, repositories to store them, indexes for efficient searching, and ranking algorithms to order results.
This document provides an overview of how to effectively search for information using Google search. It discusses formulating search queries, using Boolean operators and search modifiers, filtering search results, and utilizing advanced search features. Examples of search engines, operators, and modifiers are given. Tips are provided for analyzing topics, using synonyms, describing needs concisely, and quoting phrases. Methods for saving useful websites located through searches are also outlined.
This document discusses changes in search engine optimization (SEO) and how to cut through noise. It summarizes patents related to ranking news articles over time and how they show changes in what signals are used to evaluate news sources. It recommends optimizing content for things and voice search by adding structured data for entities and speakable schema to help digital assistants answer questions about the content. Additional reading on entity-oriented search, voice search, and leaving no valuable data behind is also provided.
Introduction to Elasticsearch for Business Intelligence and Application InsightsData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Elasticsearch for Business Intelligence and Application Insights
Speaker: Sean Donnelly
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights.
Summary of a course on how to find information on the Web. People usually do not search in a systematic way and mostly rely upon intuition.
This presentation provides a guideline on how to find information taking into account various ways.
Vrinda Davda, Rakesh Maski & Nicholas DiPiazza, Lucidworks. Presentation from ACTIVATE 2019, the Search and AI Conference. http://www.activate-conf.com
These are slides of a tutorial at ECIR by Gerard de Melo and Katja Hose.
Search is currently undergoing a major paradigm shift away from the traditional document-centric “10 blue links” towards more explicit and actionable information. Recent advances in this area are Google’s Knowledge Graph, Virtual Personal Assistants such as Siri and Google Now, as well as the now ubiquitous entity-oriented vertical search results for places, products, etc. Apart from novel query understanding methods, these developments are largely driven by structured data that is blended into the Web Search experience. We discuss efficient indexing and query processing techniques to work with large amounts of structured data. Finally, we present query interpretation and understanding methods to map user queries to these structured data sources.
This document provides an overview of search engines and how they work. It discusses the major components of search engines including spiders that crawl websites to index their content, the indexing process that analyzes websites and stores essential information, and the search engine program that matches user queries to indexed content. It also describes common search options available on most search engines like phrase searching, boolean operators, and searching by date or file type. Finally, it discusses related tools like meta search engines, desktop search programs, and ways to stay up-to-date on search engine developments.
Google began in 1996 as a research project by Larry Page and Sergey Brin. It uses PageRank, an algorithm that assigns websites a numerical weight based on the number and quality of links to it, to index websites and determine search results. When a user searches on Google, it examines its index of websites and returns the most relevant results based on the user's search terms and PageRank. Google crawls the web with its bots to constantly update and improve its index of websites to provide the most useful search results to users.
The document provides tips and strategies for effectively searching the internet to find needed information. It discusses using advanced search features like Boolean operators, phrase searching with quotation marks, and limiting searches to specific domains. Search engines like Google index websites differently than directories. Refining searches with operators, phrases, and domain limits can help attract the "needle" of needed information from the large "haystack" of the internet.
This document discusses Calais, a semantic metadata generation service that extracts entities, facts, and events from unstructured text. It provides examples of how Calais is currently being used and proposes some potential applications of Calais in digital advertising, including context-driven ad placement, topic hubs and microsites, mashup ads, and contextual customer profiling based on behavioral data.
Advertising with Linked Data in Web ContentMartin Hepp
Advertising with Linked Data in Web Content: From Semantic SEO to E-Commerce on the Web 3.0
Slides and audio from my talk given at the Knowledge Engineering Group of the University of Economics Prague.
http://keg.vse.cz/seminar.php?datetime=2011-04-06
William slawski-google-patents- how-do-they-influence-searchBill Slawski
Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.
MongoDB and Hadoop: Driving Business InsightsMongoDB
This document discusses using MongoDB and Hadoop together to drive business insights. It provides an overview of the evolving data landscape, with Hadoop used for large datasets and analytics and MongoDB used for operational workloads. Example use cases shown are combining MongoDB for real-time applications with Hadoop for analysis in domains like commerce, insurance, and fraud detection. The MongoDB Connector for Hadoop is described, allowing MongoDB to act as a data source and sink for tools like MapReduce, Pig, Hive, and Spark. A demo is shown of a movie recommendation application that uses Spark running on Hadoop to generate recommendations from a MongoDB dataset and store the results back in MongoDB.
This document provides information on how to effectively search for information online. It discusses the differences between general search engines and databases, when each is most appropriate to use, and how search engines work. It also provides tips for using search tools like Boolean operators, phrase searching, and limiting searches. The document recommends developing search strategies and having a plan when searching for academic or project-related information.
This document discusses different ways to extend semantics on the web through microdata, microformats, RDFa, and schema.org. It explains the basic syntax for using microdata to embed machine-readable data in HTML documents. Microdata provides a simple way to do this while being standardized in HTML5. It also recommends using schema.org as a unified vocabulary for semantic markup.
This document summarizes a presentation on data feed SEO. It discusses how data feeds are not unique content, the potential "affiliate penalty", and generating unique content matrices. It also provides a case study on automatically generating product descriptions and discusses different types of user generated content. Finally, it lists various data sources and APIs that can be used to build quick SEO tools and provides some resources on site architecture and leveraging outsourced labor.
This document provides tutorials and hands-on labs for developing mobile apps using Appspresso. It begins with tutorials for creating new Appspresso projects and explaining the project structure. It then covers topics like the HTML structure for Appspresso apps, using jQuery Mobile widgets like headers, lists and events, and accessing device capabilities like the accelerometer and gallery. The document concludes with instructions for exporting and uploading an app to app stores.
This document provides tutorials and hands-on labs for developing mobile apps using Appspresso. It begins with tutorials for creating new Appspresso projects and explaining the project structure. It then covers topics like the HTML structure for Appspresso apps, using jQuery Mobile for the UI, accessing device APIs, and publishing apps to app stores. The document aims to help developers learn the Appspresso platform through practical examples and step-by-step instructions.
This document discusses YQL (Yahoo Query Language) which allows users to query and access data from various web services through a simple SQL-like syntax. It describes how YQL provides a standardized way to access data without having to read documentation for each individual API. The document provides examples of common data queries and lists some of the benefits of using YQL, such as consolidating multiple HTTP requests into a single request. It also notes that YQL simply rewrites queries into HTTP calls under the hood rather than using "voodoo magic".
This document discusses Yahoo Query Language (YQL), which allows users to query and retrieve data from various web services through a simple SQL-like syntax. YQL acts as an API for services that may not otherwise have exposed data through APIs. The document provides examples of YQL queries to retrieve data from services like Google, Twitter, Foursquare and the New York Times. It highlights how YQL simplifies accessing web data by allowing complex operations to be performed with single HTTP requests.
This document summarizes a presentation on Spring Data by Eric Bottard and Florent Biville. Spring Data aims to provide a consistent programming model for new data stores while retaining store-specific features. It uses conventions over configuration for mapping objects to data stores. Repositories provide basic CRUD functionality without implementations. Magic finders allow querying by properties. Pagination and sorting are also supported.
#NoXML: Eliminating XML in Spring Projects - SpringOne 2GX 2015Matt Raible
Many Spring projects exist that leverage XML for their configuration and bean definitions. Most Java web applications use a web.xml to configure their servlets, filters and listeners. This session shows you how you can eliminate XML by configuring your Spring beans with JavaConfig and annotations. It also shows how you can remove your web.xml and configure your web components with Java.
Presented on 10/11/12 at the Boston Elasticsearch meetup held at the Microsoft New England Research & Development Center. This talk gave a very high-level overview of Elasticsearch to newcomers and explained why ES is a good fit for Traackr's use case.
Your Content, Your Search, Your DecisionAgnes Molnar
This document discusses enterprise search and how to optimize search experiences. It covers how content is growing exponentially and becoming more unstructured. It then discusses information architecture, search scenarios, metadata, managed properties, debugging search results, and using PowerShell for search administration tasks like managing properties and starting/stopping crawls. The goal is to help users connect with the right information by providing a powerful yet customizable enterprise search solution.
This document discusses semantic web technologies like microformats and microdata that allow machines to better understand web content. Microformats use existing HTML tags to add metadata through attributes like class and rel. Commonly used microformats include hCard for contact information, hCalendar for events, and hReview for reviews. Microdata also adds metadata but is only supported in HTML5. It uses new attributes like itemscope, itemtype and itemprop. Examples show how to mark up people, products, movies and other types using microformats and microdata.
The document discusses how structured data can be used to enhance search engine results and user experiences across websites, mobile apps, and other interfaces. It provides examples of using schemas like Microdata and JSON-LD to define relationships in structured data that power rich snippets, app deep links, personalized search cards, and more. The use of structured data from emails and events is also highlighted as a way to deliver pushed search results and populate the knowledge graph.
The document discusses RDFa, which is a way to embed Resource Description Framework (RDF) data within HTML pages. It provides examples of using RDFa to annotate HTML elements with metadata like titles, authors and dates. It also shows a full example of using RDFa to annotate an XHTML page with FOAF and Dublin Core properties.
Most Rails users are familiar with ActiveRecord. But what does that mean? What is ActiveRecord's approach to object relational mapping? And what are the alternatives?
Course Tech 2013, Sasha Vodnik, A Crash Course in HTML5Cengage Learning
Over the past few years, HTML5 has changed web browsers and coding alike with a stream of new elements,
attributes, and possibilities. In this session we’ll explore the major features that HTML5 offers developers, including
semantic elements, form enhancements, and browser-native audio and video. We’ll also survey the landscape of
browser support and get familiar with strategies for maintaining compatibility with legacy browsers like IE 7 and 8.
Finally, we’ll look at the fundamental changes happening to the process of revising HTML as a language and we’ll
consider some of the likeliest scenarios for the evolution of HTML in coming years.
The FamilySearch Reference Client is an open-source implementation of the Family Tree user interface that was developed to:
1) Make it easy for partners to access the FamilySearch tree using an extensible framework
2) Provide reusable components for partners to use
3) Demonstrate how to access the FamilySearch Tree using the Javascript SDK
This document summarizes a presentation about using linked data to improve library discovery. It discusses linking library data to non-library data sources to provide a richer context about materials. It introduces key concepts of linked data like identifying entities, using URIs, and standard vocabularies. The presentation also provides examples of how linked data is being applied in library catalogs by connecting catalog records to sources like VIAF, DBpedia, and Wikidata.
The document describes a presentation about rapidly prototyping with Solr. It will demonstrate ingesting documents into Solr, adjusting Solr's schema, and showcasing data in a flexible search UI. The presentation will cover faceting, highlighting, spellchecking, and debugging. Time will also be spent outlining next steps to develop and take the search application to production.
The document provides an overview of important on-page SEO elements and best practices, including meta tags, URLs, links, images, social metadata, structured data, internationalization, and responsive design. It covers topics like the meta description tag, image alt text, HTTP status codes, XML sitemaps, canonicalization, pagination, and more. User agents, robots.txt, and meta robots tags are also discussed for controlling crawlers.
Semelhante a Linked Data Presentation at TDWI Mpls (20)
SMX Advanced Seattle -- Structured Web of DataJay Myers
Best Buy has made progress in enriching their website data with semantic markup. Their initial efforts used RDFa and ontologies like GoodRelations and FOAF to publish structured data from their stores. Now they use schema.org and Microdata. This additional structured data is helping to drive more customer engagement by powering experiences like personalized recommendations. Best Buy is also exploring opportunities to further enrich product information on the web through initiatives like Gmail Actions and by "feeding knowledge" to other applications and services.
The Next Web of Linked Data -- University of St Thomas SEIS 708Jay Myers
The document discusses the evolution of the web from human-centric to machine-driven. It describes how linked open data and semantic technologies like RDF, schema.org, and JSON-LD are creating a web of data that is meaningful to computers. This machine-driven web unleashes new possibilities by enabling inferences across datasets that allow machines to discover and explore new knowledge. Many large companies and governments have adopted these technologies to publish structured data and power new applications like knowledge graphs and rich search results.
Next Web of Linked Data at Minnebar9. I chat about open data, "raw data now", JSON-LD and Hydra, and schema.org. A basic high-level overview of what is going on in the open data and semantic web world.
Presentation to product retailers, manufacturers and vendors examining the application of Linked Data and schema.org in publishing data to the web, with short examination of a GS1 initiative to publish GTIN's/ digital ids using schema.org markup
The Web Comes Alive with Data! Schema.org and Structured Data on the Web: Pas...Jay Myers
The document summarizes the development of structured data and schema.org on the web. Early attempts included microformats and ontology models like FOAF, SKOS, and GoodRelations. Schema.org was later introduced in 2010 to provide common vocabularies for search engines and lower the bar for webmasters. It has led to widespread adoption with over 15% of sites using it. This structured data has enabled additional features in search engines like knowledge graphs at Google and rich pins at Pinterest. It has also allowed applications in areas like recommendations, actions, and reservations.
This document discusses how linking data and using semantic web standards can benefit businesses. It provides examples of representing products as complex objects with related properties and relationships using RDF and ontologies. This machine-readable linked data allows for deeper queries across product information to enable personalized experiences for customers through recommendations and discovery of related products.
GS1: Better retailing through linked dataJay Myers
This document discusses how linking product data using semantic web technologies can benefit businesses. It presents products as complex objects with related attributes and relationships. Linking this data in a machine-readable format allows for deeper queries about products. Examples provided include searching for a music group and related album, finding similar products, and displaying mood-matching products based on weather. The benefits are seen as improved search engine optimization, better product discovery, more informed customers, and utilizing all product catalog information.
This document discusses how linked data can help improve retail experiences by connecting product information and relationships. It notes that products have complex attributes and relationships that linked data can represent. Linked data allows querying across all products to surface relevant combinations and filter results based on customer questions. By making product data structured and linked, retailers can power personalized recommendations and gain insights from trend analysis. The talk advocates publishing machine-readable formats to enable smart services that augment human knowledge and drive helpful visualizations for customers.
RDFa can be used to add semantic metadata to web pages that helps search engines and other applications understand the structure and meaning of content. This additional data allows for more advanced search, comparison shopping, and other automated tasks beyond just keyword searching. The document discusses how adding semantic tags like RDFa to product pages allows for more detailed product discovery and extraction of rich data about items for sale.
The Offspring of SEO and Semantic Web: SEO++ Jay Myers
The document discusses how semantic technologies like RDFa, microdata, and open graph can be used to enrich web pages with structured data that is readable to both humans and machines. It provides examples of how product information, reviews, and other structured data can be annotated on web pages and extracted via APIs to power rich snippets and other semantic applications.
NYC Lotico Semantic Web Meetup describing Best Buy's usage of RDFa, Good Relations, and other semantic technologies to drive traffic and gain insight in order to better serve our customers
Jay Myers argues that linked open data and the semantic web can provide business insights. By making external company data openly linked, businesses can gain insights from analyzing relationships across disparate data sources. Combining open external data with internal linked data allows companies to generate even deeper insights for strategic decision making.
MIT Sloan Linked Data Ventures - Jay MyersJay Myers
The document discusses several problems facing large retail companies, including siloed data across stores and brands, shrinking margins and attachment rates, declining customer service, and challenges staying connected in an increasingly digital world. It proposes using a global graph of data to break down these silos, better understand product relationships and margins, improve customer insights and service, and power consistent experiences across channels. This graph would integrate data from the company's 1,100+ stores, 10 brands, and millions of customer touchpoints to help solve problems and capture new opportunities in retail.
Increasing product and service visibility through front-end semantic webJay Myers
This document discusses increasing product and service visibility through semantic web technologies by making product data accessible to both humans and machines. It provides examples of how semantic web could be used to enhance online and in-store shopping experiences. The goal is to provide more visibility of products, services, and locations through exposing structured data on the web to enable new applications and opportunities. This could provide business benefits like improved search engine optimization, reduced proprietary data feeds, more personalized marketing, and opportunities beyond direct sales.
SES Chicago "Developments in Information Retrieval on the Web"Jay Myers
This document discusses Best Buy's use of Semantic Web technologies like RDFa and the GoodRelations ontology to provide structured data about their stores and products on the web. By adding RDFa markup to store and product pages, Best Buy is able to give meaningful context to both machines and humans about location details, pricing, and more. This structured data improves search engine visibility and allows for more informed customers. Over 460,000 Best Buy product pages have been marked up using this method.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
4. What if we could use these
webs of data as a global DB?
5. Linked Data
“A new form of Web content that is meaningful to
computers
will unleash a revolution of new possibilities” - TBL
6. Linked Data is:
A set of standards for publishing
and connecting structured data
on the web.
7. Linked Data
• Built on common web principles: HTTP, URIs,
hyperlinks
• URIs to identify data entities and
relationships between things
• Easily combine data sources
8.
9. Built on RDF
• “Resource Description Framework”
• Model for data exchange on the web
• Expresses relationships between things
11. Linked Data Vocabularies
• Schemas for the web of data
• Distributed over the web (via URIs!)
• Resolvable on the web for people to discover
and learn how to use
12. Popular Open Vocabularies
Name URI Description
Bio http://purl.org/vocab/bio/
0.1/
Describes biographical
information about
people, both living and
dead
FOAF http://xmlns.com/foaf/0.
1/
“Friend of a Friend”,
describes social
networks and person
relationships
FIBO In development “Financial Industry
Business Ontology”,
common vocabulary for
financial terminology
Good Relations http://purl.org/goodrelati
ons/v1
Annotates product
offers and products
vCard http://www.w3.org/2006/
vcard/ns#
Describes People and
Organizations
13. Make Your Own
@prefix gsp: <http://gs1.org/ns/product#> .
<http://gs1.org/ns/product>
a owl:Ontology ;
rdfs:label "GS1 Global Structured Commerce Classification Ontology"@en ;
rdfs:comment "GS1 Product Ontologies based off structured Commerce Classification work" ;
dct:creator [foaf:name "Jay Myers"] .
gsp:Product a rdfs:Class, owl:Class ;
rdfs:isDefinedBy <http://gs1.org/vocab/product> ;
rdfs:label "Product"@en ;
rdfs:comment "A GS1 recoginzed product" .
gsp:Book a rdfs:Class, owl:Class ;
rdfs:isDefinedBy <http://gs1.org/vocab/product> ;
rdfs:subClassOf gsp:BooksMusicMovies ;
rdfs:label "Book"@en ;
rdfs:comment "A product that is classified as a book" .
https://github.com/jaymyers/gs1-ontology
18. Let’s query the web of
data!SPARQL: SPARQL
Protocol and RDF Query
Language
19. SELECT Query
prefix foaf: <http://xmlns.com/foaf/0.1/>
select ?firstname ?lastname ?phonenumber
from <http://jaymyers.com/jay/>
from <http://davidwormald.com/david/>
from <http://arunbatchu.net/arun/>
where{
?person foaf:givenname ?firstname ;
foaf:lastname ?lastname ;
foaf:phone ?phonenumber .
}
LIMIT 2
Namespace
prefix
Three fields
Three data
sources
Specify
conditions
Return two
20. DBPedia Query
PREFIX dbo: <http://dbpedia.org/ontology/>
SELECT DISTINCT ?name ?person ?artist ?birth WHERE {
?person dbo:birthDate ?birth .
?person foaf:name ?name .
?person dbo:hometown <http://dbpedia.org/resource/Republic_of_Ireland> .
?person rdf:type <http://dbpedia.org/ontology/MusicalArtist> .
?person <http://dbpedia.org/ontology/associatedMusicalArtist> ?artist .
}
ORDER BY ?name
Music artists whose birthplace is Ireland
21. DBPedia Query
SELECT distinct ?episode ?chalkboard_gag WHERE
{
?episodeno <http://purl.org/dc/terms/subject>
<http://dbpedia.org/resource/Category:The_Simpsons> .
?episode dbpedia2:blackboard ?chalkboard_gag .
}
All the phrases Bart Simpson wrote on the school
blackboard in the beginning of the Simpsons
22. SPARQL nuggets
• With SPARQL you can query knowledge graphs
• SPARQL is to the Semantic Web and the Web in general what SQL is to
relational databases
• SPARQL is a W3C recommendation and is supported by many different
database vendors (no vendor lock-in)
• With SPARQL you benefit from the potential to make a collection of data
sources look and query like one big database
• SPARQL is also a standardized update and graph traversal language
• SPARQL allows you to explore data
• With SPARQL you can define inference rules to gain new information
from existing facts
“SPARQL is the new King of all Data Scientist’s tools”, Andreas Blumauer
27. schema.org
• Common vocabs and markup that search engines
can understand
• Ease the friction of publishing Linked/ Structured
Data to the web
• Linked, open data as a platform to build cool stuff
on the web, improve user experience through data
• Over 1200 schema objects and counting
28. Richly Annotated HTML
<div itemscope itemtype="http://data-vocabulary.org/Person">
My name is <span itemprop="name">Jay Myers</span>,
but people call me <span itemprop="nickname">Professor Jaymond Myers</span>.
Here is my homepage:
<a href="http://jaymmyers.tumblr.com" itemprop="url">http://jaymmyers.tumblr.com</a>.
I live in
<span itemprop="address" itemscope
itemtype="http://data-vocabulary.org/Address">
<span itemprop="locality">Minneapolis</span>,
<span itemprop="region">MN</span>
</span>
and work as a <span itemprop="title">Technical Product Manager</span>
at <span itemprop="affiliation">Best Buy, Co., Inc</span>.
</div>
29. Richly Annotated HTML
<div itemscope itemtype="http://data-vocabulary.org/Product">
<span itemprop="brand">ACME</span> <span itemprop="name">Executive
Stapler</span>
<img itemprop="image" src="http://upload.wikimedia.org/wikipedia/commons/thumb/2/2d/Swingline-stapler.jpg/220px-Swingline-
stapler.jpg" />
<span itemprop="description">Sleeker than ACME's Classic Stapler, the
Executive Stapler is perfect for the business traveler
looking for a compact stapler to staple their papers.
</span>
Category: <span itemprop="category" content="Office Supplies > Tools > Staplers">Staplers</span>
Product #: <span itemprop="identifier" content="mpn:925872">
925872</span>
<span itemprop="review" itemscope itemtype="http://data-vocabulary.org/Review-aggregate">
<span itemprop="rating">4.4</span> stars, based on <span itemprop="count">89
</span> reviews
</span>
<span itemprop="offerDetails" itemscope itemtype="http://data-vocabulary.org/Offer">
Regular price: $179.99
<meta itemprop="currency" content="USD" />
$<span itemprop="price">119.99</span>
(Sale ends <time itemprop="priceValidUntil" datetime="2010-11-05">
5 November!</time>)
Available from: <span itemprop="seller">Executive Objects</span>
Condition: <span itemprop="condition" content="used">Previously owned,
in excellent condition</span>
<span itemprop="availability" content="in_stock">In stock! Order now!</span>
</span>
</div>
39. DBPedia < > Best Buy Mashups
Query: “Find me a description of the band Abba from
the web of open
data and an album for sale by them at Best Buy”
Result: ABBA was a Swedish pop/rock group formed
in
Stockholm in 1972, comprising Agnetha Fältskog,
Benny
Andersson, Björn Ulvaeus and Anni-Frid Lyngstad.
AND
Best Buy Sells the CD: ABBAMania: Tribute to ABBA
– Various
40. DBPedia < > Best Buy Mashups
Query: “Find me music artists from Ireland and album
for sale by them at Best Buy”
Business result: 6% higher purchase conversion
compared to commerce site
41. Emotional Weather Report POC
SPARQL query of a collection of data sources,
display Best Buy products that match the mood
people are in due to weather/ environment
43. Linked Data Biz Benefits
• New avenues of customer personalization
• Deeper, more relevant and contextual customer
experiences
• Utilize all of your product catalog – the product
“long tail”