Knowledge Graphs, Markup, and Enterprise
Knowledge Graphs, Markup, and Enterprise
H/T Teodora Petkova Aaron Bradley
Must Read
Writing for the web involves both the reader and search. This combination should be at the back of your head whenever you write for the web.
Originally shared by Teodora Petkova
How Linked Data Changes Search
From ten blue links to 10 blue things
Aaron Bradley explains the workings behind Google’s knowledge graph and how linked data technologies are changing search and are to change our approach to search engine optimisation.
Here you will learn not only about structured data and schema.org but about RDFs, SKOS, ontologies and more, in an easy to digest way:. Here's the link to the video and its the script together with takeaways nicely highlighted: https://goo.gl/QXcy6m
Some of my favourite moments:
Informing search engines (and other data consumers) what a website is about
[…] We now have is a web of things kind of one of Tim Berner Lee’s great visions for the web, so that rather than being in index of documents Google is increasingly becoming an index of things, and facts related to those things. You can’t possibly have that without those sort of linked data technologies working in the background including ontologies, schemas, taxonomy solve that sort of thing.
On Querying
Querying is really core to any sort of many of these processes, right, whether you’re querying your analytics or whether you’re querying your content or where whether you’re querying user events. If they’re described in the same ways, that starts to really present powerful possibilities of joining one with the other and when you’re dealing with data, you always have to think from it think of it from a machine data consumer perspective, right? So you’re not going to be able to get answers to questions that your…your data isn’t aware of, right? So it has to be structured in such a way that it supports those queries, and in building a lot of the systems that are worked on, that’s really where a lot of the MVP (Minimum Viable Product) requirements start with, its can it satisfy this query and this query and this query and if not, then you modify your architecture in order to satisfy that.
Deeply Understanding Schema.org
I think to truly understand something like schema.org you do need to know a little bit about link data and how it works. What we would call the Semantic Web but link data is now more of the term. There’s a couple of canonical documents about that if you. Anyone watching this, go ahead and google Tim Berner Lee’s work. I forget the year but it’s called Link Data Design Issues [https://goo.gl/8L3tKY] in which he provides the five rules of link data. If you understand those 43 words, you’ll really, really understand linked data and the Semantic Web.
Thanks Martha van Berkel for putting this together and Aaron for sharing so much information n such an accessible way.
H/T Teodora Petkova Aaron Bradley
Must Read
Writing for the web involves both the reader and search. This combination should be at the back of your head whenever you write for the web.
Originally shared by Teodora Petkova
How Linked Data Changes Search
From ten blue links to 10 blue things
Aaron Bradley explains the workings behind Google’s knowledge graph and how linked data technologies are changing search and are to change our approach to search engine optimisation.
Here you will learn not only about structured data and schema.org but about RDFs, SKOS, ontologies and more, in an easy to digest way:. Here's the link to the video and its the script together with takeaways nicely highlighted: https://goo.gl/QXcy6m
Some of my favourite moments:
Informing search engines (and other data consumers) what a website is about
[…] We now have is a web of things kind of one of Tim Berner Lee’s great visions for the web, so that rather than being in index of documents Google is increasingly becoming an index of things, and facts related to those things. You can’t possibly have that without those sort of linked data technologies working in the background including ontologies, schemas, taxonomy solve that sort of thing.
On Querying
Querying is really core to any sort of many of these processes, right, whether you’re querying your analytics or whether you’re querying your content or where whether you’re querying user events. If they’re described in the same ways, that starts to really present powerful possibilities of joining one with the other and when you’re dealing with data, you always have to think from it think of it from a machine data consumer perspective, right? So you’re not going to be able to get answers to questions that your…your data isn’t aware of, right? So it has to be structured in such a way that it supports those queries, and in building a lot of the systems that are worked on, that’s really where a lot of the MVP (Minimum Viable Product) requirements start with, its can it satisfy this query and this query and this query and if not, then you modify your architecture in order to satisfy that.
Deeply Understanding Schema.org
I think to truly understand something like schema.org you do need to know a little bit about link data and how it works. What we would call the Semantic Web but link data is now more of the term. There’s a couple of canonical documents about that if you. Anyone watching this, go ahead and google Tim Berner Lee’s work. I forget the year but it’s called Link Data Design Issues [https://goo.gl/8L3tKY] in which he provides the five rules of link data. If you understand those 43 words, you’ll really, really understand linked data and the Semantic Web.
Thanks Martha van Berkel for putting this together and Aaron for sharing so much information n such an accessible way.
Thanks Teodora Petkova. :)
ReplyDeleteAaron Bradley always! :)
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