If I have an in-depth article, what schema should I use? I have multiple articles with over 4,500 words written. It's not a news article.
http://schema.org/Article points out this to cover them all, but I suspect that this kind of article is not supported.
Article is the right type for any article, which would cover your use case. It's not absolutely necessary to use a subtype. I would recommend you just use the Article type.
Alternatively, you can use Report, a subtype of Article, which may be appropriate depending on the style of your ‘article’.
Related
I want to use json ld micro-data from schema.org to reference images on my website but at the same time retrieve the json ld object in my Javascript to use it for others things. The problem is that I want to add custom fields that do not match any type in Schema.org. My question is, will search engines just ignore the fields they don't recognize or discard the whole micro-data ?
Short answer, yes they’ll ignore properties they don’t define, and it’s quite normal to use JSON-LD for other purposes, such as to drive your UI. That said, it’s best if these properties and types come from a known vocabulary, or even resolve to your own. It’s always good to test your data patterns first, using any of the online tools available.
Also, it’s not JSON-LD micro-data, they are actually two different things, both of which (along with RDFa) can be used to provide schema.org markup. JSON-LD is contained in a script element, the others are expressed using HTML attributes.
Are there any advantages/disadvantages in using a specific format for http://www.schema.org/Product? Something like "Searchengines understand Microdata better than JSON-LD"? I would like to use JSON-LD, because it doesn't mess-up with your html-code, but I'm not sure if it would be better concerning the searchengines to use Microdata.
There is no general answer, it depends on the consumer of the data.
A specific consumer supports a specific set of syntaxes, and might or might not recommend a subset of these supported syntaxes.
Because search engines usually try to make sure not to get lead astray (e.g., a page about X claims via its Schema.org use to be about Y), it seems natural that they would prefer a syntax that couples the Schema.org metadata to the visible content of the page (in HTML5, this would be Microdata or RDFa); for the same reasons why many meta tags are dead for SEO.
However, this is not necessarily always the case. Google, for example, recommends the use of JSON-LD for a few of their features (bold emphasis mine):
Promote Critic Reviews:
Though we strongly recommend using JSON-LD, Google can also read schema.org fields embedded in a web page with the microdata or RDFa standards.
Sitelinks Search Box:
We recommend JSON-LD. Alternatively, you can use microdata.
I'm building a site that will let users take practice tests and I was wondering... Is there some schema to define the question and answers that will make it easier for search engines to understand my content.
I'm trying to do everything possible to make the site rank well with search engines and I've been told schema's are a great way to do that.
I looked at the ones on schema.org but I can't find anything similar. Is there any other resource I could use?
I think the closest types would be Question and Answer. While they are probably primarily intended for Q&A sites (like Stack Overflow) or FAQs, their description doesn’t seem to exclude quizzes.
You could use the suggestedAnswer property to link from the Question to each possible Answer.
I was wondering if there exists any open source frameworks that will help me include the following type of functionality to my website:
1) If I am viewing a particular product, I would like to see what other products may be interesting to me. This information may be deduced by calculating for example what other people in my region (or any other characteristic of my profile) bought in addition to the product that I am viewing. Kind of like what Amazon.com does.
2) Deduce relationships between people based on their profile, interaction with one another on the website (via commenting on one another´s posts for example), use of the website in terms of areas most navigated, products bought in common etc.
I am not looking for a open source website with this functionality, but something like an object model into which I can feed information about users and their use of the site including rules about relationships and then at a later point ask it questions described in (1) and (2) above.
Any pointers to white papers / general information about best approaches to do this, or any related links will really help too.
(I am the developer of Taste, which is now part of Apache Mahout)
1) You're really asking for two things here:
a) Recommend items I might like
b) Favor items that are similar to the thing I am currently looking at.
Indeed, Mahout Taste is all about answering a). Everything it does supports systems like this. Take a look at the documentation to get started, and ask any questions to mahout-user#apache.org.
For 1b) in particular, Mahout has two answers:
If you are only interested in what items are similar to the current item, you would be interested in the ItemSimilarity abstraction in Mahout (org.apache.mahout.cf.taste.similarity.ItemSimilarity) and its implementations, like PearsonCorrelationSimilarity. Based on a set of user-item ratings, this could tell you an estimated similarity between any two items. You'd then just pick the most similar items. In fact, look at the TopItems class in Mahout which can just figure this for you quickly.
But also, you can combine a) and b) by computing recommendations, then applying a Rescorer implementation which then favors items that are similar to the currently-viewed item.
2) Yes likewise, you would be interesting the UserSimilarity abstraction, implementations, etc. This would deduce similarities based on item ratings. Mahout however does not help you deduce these ratings by, say, looking at user behavior. This is domain-specific and up to you.
Sound confusing -- read the docs and feel free to follow up on mahout-user#apache.org where I can tell you more.
I am researching the same topic, as I'm working on a project to help people decide how to vote on California's complicated ballot measures. Here are some open-source collaborative filtering engines that I've found:
Vogoo (PHP)
acts_as_recommendable (Ruby on Rails)
Mahout (formerly Taste) (Java)
There's also a good overview of these engines here.
There are also the Duine framework and OpenSlopeOne.
But in my opinion, Mahout is still the best.
You can find a survey about Open Source Recommender Systems here:
http://girlincomputerscience.blogspot.com.br/2012/11/open-source-recommendation-systems.html
Hope it helps!
You can find a List of Recommender Systems here
some time ago I found an article (Roles: Composable Units of Object Behavior) describing the pros of using Roles versus Interfaces or other ways of dealing with behavior requirements. Does any of you knows where I can find more literature about that, or knows more about Roles?
I know that that's almost a research topic, but maybe someone (maybe some Perl programmer) has tried something with it (Moose?).
Note: the reason for adding tag "perl" is that maybe Perl programmers are more likely to give an answer.
For Moose based examples, you should check this and that example and this specification.
ETA: For the theoretical aspects, see this page