#WebTables

[cssmonster]

Table Talk: Creating Beautiful Tables with CSS

Introduction

Welcome to the fascinating world of table design in web development! Tables play a crucial role in organizing and presenting data on websites, and with the power of CSS, we can transform them into visually stunning elements that enhance the overall user experience. In this blog post, we will embark on a journey to explore the art of creating beautiful tables with CSS. Whether you're a beginner seeking to understand the basics or an experienced developer looking to refine your skills, this guide will cover everything from the fundamentals to advanced styling techniques, responsive design considerations, accessibility best practices, and tips for optimizing performance. Join us as we delve into the realm of table talk, where we unravel the secrets of crafting tables that not only convey information effectively but also captivate and engage your website visitors. Let's bring life to your tables and make them an integral part of your website's visual appeal!

Understanding Table Structure

Before we embark on the exciting journey of styling tables with CSS, it's essential to have a solid understanding of the underlying structure of HTML tables. Tables are constructed using a combination of various HTML elements, each serving a specific purpose in organizing and presenting data. The primary elements involved in table structure include: - : The fundamental container for the entire table. - : Stands for "table row" and is used to define a row within the table. - : Represents a table header cell, typically used to label columns or rows. - : Signifies a standard table cell containing data. Let's break down the structure further: ElementDescriptionThe outermost element, encapsulating the entire table.Contained within the element, representing a row in the table.Used for header cells, providing labels for columns or rows. It is placed within the element.Represents a standard data cell within the table, residing within the element. Understanding this basic structure lays the foundation for effective table styling. As we proceed, we'll explore how CSS can be applied to these elements to create visually appealing and well-organized tables on your website.

Basic CSS Styling for Tables

See the Pen CSS3 pricing table by Arkev (@arkev) on CodePen. Now that we have a grasp of the fundamental structure of HTML tables, let's dive into the exciting realm of CSS styling to enhance their visual appeal. Basic CSS properties can be employed to customize the appearance of tables, making them more aesthetically pleasing and aligned with your website's design. Here are some key CSS properties you can use for basic table styling: - border: Defines the border of table cells, allowing you to control the thickness, style, and color. - padding: Adds space within table cells, enhancing the overall spacing and readability of your table. - margin: Sets the margin outside the table, influencing its positioning within the surrounding elements. Let's explore a simple example of applying these properties: PropertyDescriptionborderDefine the border of table cells using properties like border-width, border-style, and border-color.paddingEnhance cell spacing by applying padding using the padding property.marginControl the table's positioning with the margin property, setting the space outside the table. For instance, to add a solid border to all table cells, you can use: CSStable { border-collapse: collapse; } td, th { border: 1px solid #dddddd; padding: 8px; text-align: left; }

Advanced Styling Techniques

As we continue our exploration of CSS table styling, it's time to elevate our design game with advanced techniques that go beyond the basics. These techniques allow you to unleash your creativity and transform your tables into visually stunning elements that capture the attention of your website visitors. Here are some advanced CSS styling techniques for tables: - Customization of Table Headers: Tailor the appearance of table headers by using CSS properties like background-color, color, and font-weight. This helps in creating a distinct visual hierarchy within the table. - Row and Cell Customization: Apply different styles to specific rows or cells using pseudo-classes such as :nth-child. This is particularly useful for highlighting important data or creating alternating row colors. - Background Colors, Gradients, and Shadows: Infuse life into your tables by incorporating background colors, gradients, and box shadows. These elements add depth and dimension to the table, enhancing its overall aesthetic appeal. Let's delve into an example to illustrate these concepts: TechniqueDescriptionCustomization of Table HeadersStyle headers with properties like background-color, color, and font-weight to make them visually distinct.Row and Cell CustomizationUse pseudo-classes like :nth-child to apply different styles to specific rows or cells, enhancing readability and organization.Background Colors, Gradients, and ShadowsIntegrate background colors, gradients, and shadows to add a touch of sophistication and depth to your table design. For example, to create alternating row colors, you can use the following CSS: CSStr:nth-child(even) { background-color: #f2f2f2; }

Responsive Tables

In the ever-evolving landscape of web design, responsiveness is key to ensuring a seamless user experience across various devices and screen sizes. Responsive tables adapt to different viewport dimensions, allowing your tables to remain functional and visually appealing on both desktops and mobile devices. Here are some essential considerations and techniques for creating responsive tables: - Importance of Responsive Design: Understand why responsive design is crucial for tables and how it enhances accessibility and usability on smaller screens. - Media Queries: Implement media queries in your CSS to apply different styles based on the device's screen size. This allows you to tailor the table's appearance for specific breakpoints. - Stacking and Hiding: Explore techniques like stacking and hiding table elements to optimize the layout for smaller screens. This involves rearranging and prioritizing content to maintain clarity. Let's delve into an example of using media queries to create a responsive table: TechniqueDescriptionImportance of Responsive DesignExplain why responsive design is essential for tables, emphasizing the diverse range of devices and screen sizes used by website visitors.Media QueriesIntroduce media queries in CSS to conditionally apply styles based on the device's screen size. This allows for a tailored and optimized table layout for each breakpoint.Stacking and HidingDiscuss techniques like stacking and hiding elements to rearrange and prioritize content, ensuring a user-friendly experience on smaller screens without sacrificing information. For instance, the following media query adjusts the font size for better readability on smaller screens: CSS@media only screen and (max-width: 600px) { td { font-size: 14px; } }

Accessibility Considerations

Ensuring that your tables are accessible is not just a best practice but a fundamental aspect of web development. Accessibility ensures that all users, including those with disabilities, can perceive, navigate, and interact with your tables. Let's delve into key considerations and best practices for creating accessible tables with CSS: - Importance of Accessibility: Understand the significance of accessibility and its impact on providing an inclusive user experience. Emphasize the diversity of users, including those with visual or cognitive impairments. - Semantic HTML: Utilize semantic HTML elements to enhance the structure and meaning of your tables. Use for headers, for table captions, and ensure proper attributes. - Contrast and Color: Pay attention to contrast ratios and avoid relying solely on color to convey information. Ensure that text and background colors provide sufficient contrast for users with visual impairments. - Keyboard Navigation: Test and optimize your table for keyboard navigation. Users who rely on keyboard input should be able to navigate and interact with the table efficiently. - Use of ARIA Attributes: Leverage Accessible Rich Internet Applications (ARIA) attributes to enhance the accessibility of dynamic content and interactions within your tables. Include attributes like aria-describedby and aria-labelledby. Let's illustrate the importance of semantic HTML and ARIA attributes with an example: ConsiderationDescriptionImportance of AccessibilityHighlight the significance of creating tables that are accessible to users with diverse abilities, fostering inclusivity and a positive user experience.Semantic HTMLEmphasize the use of semantic HTML elements, such as and , to provide meaningful structure and context to assistive technologies.Use of ARIA AttributesIntroduce ARIA attributes like aria-describedby and aria-labelledby to enhance the accessibility of dynamic content within tables, ensuring proper information relay for screen readers. By prioritizing accessibility considerations, you contribute to a web environment that is welcoming and usable for everyone, regardless of their abilities or disabilities.

Optimizing Performance

Efficient table styling not only contributes to a visually appealing website but also plays a crucial role in optimizing performance. Ensuring that your tables load quickly and smoothly is essential for providing a seamless user experience. Let's explore key tips and techniques for optimizing the performance of your CSS-styled tables: - Minimization of Styles: Strive for minimalism in your CSS styles. Avoid overloading your tables with unnecessary styles and prioritize only the essential design elements. This reduces the overall file size and improves loading times. - Consolidation of Stylesheets: If your website uses multiple stylesheets, consider consolidating them. Combining stylesheets into a single file reduces the number of HTTP requests, resulting in faster loading times. - Utilization of Browser Cache: Leverage browser caching to store frequently used styles and assets locally on the user's device. This reduces the need for repeated downloads, enhancing the overall performance of your tables. - Optimized Image Usage: If your tables include images, ensure they are optimized for the web. Compress images without sacrificing quality to reduce file sizes and accelerate loading times. - Browser Compatibility: Test your table styles across different browsers to ensure compatibility. Address any issues that may arise, preventing performance bottlenecks on specific browsers. Let's delve into an example that emphasizes the importance of minimizing styles for optimal performance: Optimization TechniqueDescriptionMinimization of StylesHighlight the significance of streamlining CSS styles to include only essential design elements. Avoid unnecessary styles to reduce file size and enhance loading speed.Consolidation of StylesheetsEncourage the consolidation of multiple stylesheets into a single file. This reduces HTTP requests and contributes to a more efficient loading process.Utilization of Browser CacheExplain the benefits of browser caching in storing frequently used styles and assets locally, reducing the need for repeated downloads and improving overall performance. By implementing these optimization techniques, you not only enhance the performance of your CSS-styled tables but also contribute to a faster and more enjoyable user experience on your website.

FAQ

Explore common questions and solutions related to CSS table styling in this Frequently Asked Questions section. Whether you're a beginner or an experienced developer, find answers to queries that may arise during your journey of creating beautiful tables with CSS. Q: How can I center-align text within table cells? A: To center-align text in table cells, you can use the CSS property text-align: center;. Apply this property to the or elements within your table. Q: What is the significance of the border-collapse property? A: The border-collapse property is crucial for controlling the spacing and appearance of borders between table cells. Setting it to collapse ensures a single border is shared between adjacent cells, creating a cleaner and more cohesive table layout. Q: How can I create alternating row colors for better readability? A: You can use the :nth-child(even) and :nth-child(odd) pseudo-classes in CSS to apply different background colors to alternating rows. This enhances readability and adds a visually appealing touch to your tables. Q: What role do media queries play in creating responsive tables? A: Media queries are instrumental in responsive design. They allow you to apply different styles based on the device's screen size. By using media queries, you can optimize your tables for various breakpoints, ensuring they remain user-friendly on both desktop and mobile devices. Q: How can I ensure my tables are accessible to all users? A: Prioritize semantic HTML, use ARIA attributes, and pay attention to contrast and color choices. Implementing these practices ensures that your tables are accessible to users with disabilities, providing an inclusive experience for all. Feel free to explore these FAQs to troubleshoot common issues and enhance your understanding of CSS table styling. If you have additional questions, don't hesitate to reach out for further assistance.

Conclusion

Congratulations on completing your journey into the world of creating beautiful tables with CSS! Throughout this comprehensive guide, we've covered essential concepts, techniques, and best practices to empower you in enhancing the visual appeal and functionality of your tables. As a quick recap, we started by understanding the basic structure of HTML tables, delving into the roles of elements such as , , , and . From there, we explored basic CSS styling to customize the appearance of tables, incorporating properties like border, padding, and margin. We then ventured into advanced styling techniques, learning how to customize headers, rows, and cells, as well as integrating background colors, gradients, and shadows to create visually captivating tables. The importance of responsive design was highlighted, with insights into media queries and techniques for optimizing tables on various devices. Accessibility considerations played a crucial role, emphasizing semantic HTML, contrast and color choices, keyboard navigation, and the use of ARIA attributes to ensure an inclusive experience for all users. We also explored performance optimization tips, focusing on minimizing styles, consolidating stylesheets, and leveraging browser cache. In the FAQ section, common queries were addressed, providing solutions to challenges you may encounter in your table styling endeavors. Whether you're a novice or an experienced developer, these FAQs serve as a valuable resource for troubleshooting and expanding your knowledge. As you continue refining your skills in CSS table styling, remember that practice and experimentation are key. Feel free to explore, test, and implement these techniques to create tables that not only convey information effectively but also contribute to the overall aesthetics and usability of your website. Thank you for joining us on this journey. May your tables be both functional and visually stunning, enhancing the user experience on your website! Read the full article

[jordankennedy]

i want u to know yours is the first tma blog i've interacted with since i finished listening to the podcast for the first time (im a latecomer to the fandom :']) and i love your takes sm TvT do you recommend any blogs in particular for me to follow? i have felt the brainrot setting in for MONTHS now

hell yeah brother (sister?) glad you like my posts cause i kinda do just say whatever. i was really into tma back in 2020 and have only recently gotten back into it so a lot of the blogs i used to interact with a lot have moved on or deactivated but i do think everybody should follow my besties @webtable and @loverboy-ish

[webtable]

mag-003 -> webtable

hi. please read my carrd (click on the bio). other than that i'm usually around and willing to talk about whatever. 

tallahassee/leigh/french | they/them and one/one's | 19

ask for my discord if you want to chat

[confusioncorridor]

Jon is so galaxy brain genious listening specifically to story about how the only way to safely contain not them was webtable chant tpose ritual and he goes and chops it to splinters immediately Jon please avoid morosexual conventions lest you be eaten alive

Michael is so @w@

[meadowmines]

MAG77-79

Jon? My dude? My buddy? My guy? My pal? My companion? My bromigo?

WTF the fuck was that?

MAG77: hoo. It’s hard to say, not having heard nearly as much of Gertrude reading statements as Jon doing it, but... she didn’t seem to get as deeply into “character” as he does. Still, she knew her shit, she refers to The Stranger by name and all. 

Jon... oof. Jeez. Poor Jon. I mean, yay for him finally getting a gotdamn clue but... oooof

MAG78: *deflating balloon noise* jon why.

You shoulda burned the tapes, Not-Sasha. Lucky for you, Jon just came down with a bad case of Shit Idiot Brain Fungus.

Statement: so this guy Adelard Dekker came knocking on my door asking about my Not-Cousin Not-Carl, he brought this very familiar sounding table in and I’m p. sure he trapped my Not-Cousin Not-Carl in it. like. there were spiderwebs coming out of it and p. obviously tangling Not-Cuz up and shit

Jon: hmm it seems like this table is at least partially keeping the Not-Them restrained

Jon: better go break it

Me: Uhhhhhhhh... that doesn’t sound right but I don’t know enough about Not-Thems and webtables to dispute it so go off I guess

Aaaaaaand sure enough, that wasn’t right, all hell breaks loose, “Michael” is there, everything goes entirely to shit nice job breaking it hero

MAG79: God bless Tim and Martin for providing a little comic relief! Sorry about ur poetry tape, Martin. And then...

Martin: You don’t think the Institute is haunted do you?

Tim: I used to, now I think it’s worse.

Martin: Worse how?

[LOUD CRASH AND MONSTER NOISES]

Also. Martin has a backbone! Good for him!

Okay, “Michael” is clearly Bad Fucking News but he did give Jon an exit and helped Actually-Sasha figure out how to kill flesh hives so... 

...nah, fuck him extremely

And the bit with Not-Sasha taunting Jon. Um. I have several questions but let’s start with:

EX? FUCKING? CUSE? ME??? Yeah, sure, that could just be a figure of speech but given all the Eye-adjacent bullshit that seems to follow Archivists around, I ain’t buying that for one second. I think at this point we are way past just thinking of “Archivist” as Jon’s job title, there’s something Very Heavy attached to that title and goddamn fucking Elias knows it, doesn’t he? 

And then... mystery dude, possibly mystery tunnel dweller.

*opens up Spotify, looks at blurb for next episode* Statement of Jurgen Leitner?

...

...

...

ARE YOU FUCKING KIDDING ME!?

[seleniumtraining01]

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- Introduction to Pytest - Grouping & Parameterizing test cases - Fixtures & Dependency - Pytest Allure Reports - Customizing Allure Report

Python with Selenium

- Introduction to Selenium Webdriver - Objects and Object Identification - Selenium with Python Concepts -1 - Selenium with Python Concepts -2 - Handling Frames & WebTables - Handling Ajax Auto Suggests, JSE & AutoIT - Exercises

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[mariathaterh]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

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[janiceclaudetteo]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana https://ift.tt/2JONMGh

[elenaturnerge1]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana https://ift.tt/2JONMGh

[samiaedithg]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana https://ift.tt/2JONMGh

[lindasharonbn1]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

Copyright © 2018 SEO by the Sea ⚓. This Feed is for personal non-commercial use only. If you are not reading this material in your news aggregator, the site you are looking at may be guilty of copyright infringement. Please contact SEO by the Sea, so we can take appropriate action immediately. Plugin by Taragana https://ift.tt/2JONMGh

[jordankennedy]

@webtable bonus naomi

[miettawilliemk1]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

frame>

Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

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[mariaajamesol]

Schema, Structured Data, and Scattered Databases such as the World Wide Web

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

This is a view of the Farmers Market from the side. I wish I had the chance to come back later in the day, and see what it was like other than in the morning.

This was a nice little park next to Pike Place Market, which looked like a place to take your dog for a walk while in the area, and had a great view of Elliot Bay (the central part of Puget Sound.)

This is a view of the waterfront from closer to the conference center.

You can see Mount Ranier from the top of the Conference Center.

My presentation for SMX Advanced 2018:

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Schema, Structured Data & Scattered Databases Such as the World Wide Web. My role in this session is to introduce Schema and Structured Data and how Google is using them on the Web.

Google is possibly best known for the PageRank Algorithm invented by founder Lawrence Page, whom it is named after. In what looks like the second patent filed by someone at Google was the DIPRE (Dual interative pattern relation expansion) patent, invented and filed by Sergey Brin. He didn’t name it after himself (Brinrank) like Page did with PageRank.

The provisional patent filed for this invention was the whitepaper, “Extracting Patterns and Relations from Scattered Databases such as the World Wide Web.” The process behind it is set out in the paper, and it involves a list of 5 books, titles, their authors, Publishers, Year published. Unlike PageRank, it doesn’t involve crawling webpages, and indexing links from Page to page and anchor text. Instead, it involves collecting facts from page to page, and when it finds pages that contain properties and attributes from these five books, it is supposed to collect similar facts about other books on the same site. And once it has completed, it is supposed to move on to other sites and look for those same 5 books, and collect more books. The idea is to eventually know where all the books are on the Web, and facts about those books, that could be used to answer questions about them.

This is where we see Google being concerned about structured data on the web, and how helpful knowing about it could be.

When I first started out doing inhouse SEO, it was for a Delaware incorporation business, and geography was an important part of the queries that my pages were found for. I had started looking at patents, and ones such as this one on “Generating Structured Data caught my attention. It focused on collecting data about local entities, or local businesses, and properties related to those. It was built by the team led by Andrew Hogue, who was in charge of the Annotation framework at Google, who were responsible for “The Fact Repository”, an early version of Google’s Knowledge Graph.

If you’ve heard of NAP consistency, and of mentions being important to local search, it is because Local search was focusing on collecting structured data that could be used to answer questions about businesses. Patents about location prominence followed, which told us that a link counted as a mention, and a patent on local authority, which determined which Website was the authoritative one for a business. But, it seemed to start with collecting structured data about businesses at places.

The DIPRE Algorithm focused upon crawling the web to find facts, and Google Maps built that into an approach that could be used to rank places and answer questions about them.

If you haven’t had a chance to use Google’s experimental table search, it is worth trying out. It can answer questions to find answers from data-based tables across the web, such as “what is the longest wooden pier in California”, which is the one in Oceanside, a town next to the one I live in. It is from a Webtables project at Google.

Database fields are sometimes referred to as schema and table headers which tell us what kind of data is in a table column may also be referred to as “schema”. A data-based web table could be considered a small structured database, and Google’s Webtable project found that there was a lot of information that could be found in web tables on the Web.

Try out the first link above (the WebTables Project Slide) when you get the chance, and do some searches on Google’s table search. The second paper is one that described the WebTables project when it first started out, and the one that follows it describes some of the things that Google researchers learned from the Project. We’ve seen Structured Snippets like the one above grabbing facts to include in a snippet (in this case from a data table on the Wikipedia page about the Oceanside Pier.)

When a data table column contains the same data that another table contains, and the first doesn’t have a table header label, it might learn a label from the second table (and this is considered a way to learn semantics or meaning from tables) These are truly scattered databases across the World Wide Web, but through the use of crawlers, that information can be collected and become useful, like the DIPRE Algorithm described.

In 2005, the Official Google Blog published this short story, which told us about Google sometimes answering direct questions in response to queries at the top of Web results. I don’t remember when these first started appearing, but do remember Definition results about a year earlier, which you could type out “Define:” and a word or ask “What is” before a word and Google would show a definition, and there was a patent that described how they were finding definitions from glossary pages, and how to ideally set up those glossaries, so that your definitions might be the ones that end up as responses.

In 2012, Google introduced the Knowledge Graph, which told us that they would be focusing upon learning about specific people, places and things, and answering questions about those instead of just continuing to match keywords in queries to keywords in documents. They told us that this was a move to things instead of strings. Like the books in Brin’s DIPRE or Local Entities in Google Maps.

We could start using the Web as a scattered database, with questions and answers from places such as Wikipedia tables helping to answer queries such as “What is the capital of Poland”

And Knowledge bases such as Wikipedia, Freebase, IMDB and Yahoo Finance could be the sources of facts about properties and attributes about things such as movies and actors and businesses where Google could find answers to queries without having to find results that had the same keywords in the document as the query.

In 2011, The Schema.org site was launched as a joint project from Google, Yahoo, Bing, and Yandex, that provided machine-readable text that could be added to web pages. This text is provided in a manner that is machine readable only, much like XML sitemaps are intended to be machine-readable, to provide an alternative channel of information to search engines about the entities pages are about, and the properties and attributes on those pages.

While Schema.org was introduced in 2011, it was built to be extendable, and to let subject matter experts be able to add new schema, like this extension from GS1 (the inventors of barcodes in brick and mortar stores) If you haven’t tried out this demo from them, it is worth getting your hands on to see what is possible.

In 2014, Google published their Biperpedia paper, which tells us about how they might create ontologies from Query streams (sessions about specific topics) by finding terms to extract data from the Web about. At one point in time, Search engines would do focused crawls of the web starting at sources such as DMOZ, so that the Index of the Web they were constructing contained pages about a wide range of categories. By using query stream information, they are crowdsourcing the building of resources to build ontologies about. This paper tells us that Biperpedia enabled them to build ontologies that were larger than what they had developed through Freebase, which may be partially why Freebase was replaced by wiki data.

The Google+ group I’ve linked to above on the Schema Resources Page has members who work on Schema from Google, such as Dan Brickley, who is the head of schema for Google. Learning about extensions is a good idea, especially if you might consider participating in building new ones, and the community group has a mailing list, which lets you see and participate in discussions about the growth of Schema.

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[amitysoft]

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