This is a creative one :-)
I'll be receiving a list of hundreds of new URLs regularly and want to find out if they are linking to a blog or not - between 80% and 95% accuracy would be sufficient.
Obviously I need to analyze the HTML of the page - but how exactly would you approach this (e.g. meta tags, structural analysis, pattern matching, machine learning ...)?
I would look at the generator <meta> tag for known blog editors. For example here's how it looks for Wordpress:
<meta name="generator" content="WordPress.com" />
Building on Darin's solution, I would look for the generator <meta> tag for known blog editors and combine it with a lookup table of common sites, ie. WordPress.com, Blogspot.com, Livejournal.com, and so forth. That should give you 80-95% in the near term, though it won't be robust enough for an ongoing process over an extended period of time.
An extended solution is much harder, given the amorphous definition of the term "blog". In which case, you'll want to consider breaking the list down into its hosting site and defining characteristics and create hard and fast rules on what constitutes a blog:
Is it hosted by a blogging service provider?
Is it listed in a blog aggregator, such as Technorati?
Does it include blog-like services, such as user-generated articles, tags, and the ability to comment?
Does it provide meta information that I can use to easily identify it as a blog?
Does it otherwise identify itself as a blog, via the inclusion of the term "blog" or some other criteria?
I can easily see a neural network constructed to determine if a page is a blog or not, but this serverely oversteps the bounds of your requirements. I'd say start simple, then extend your solution relative to the proposed lifetime of your system.
The above suggestions are good, and probably will work if you're aiming for 80-90% accuracy.
I would go one step further and look for any .xml RSS feed in either a meta tag, or as a link. Then check the feed to see if there are any comment tags (since there are feeds for other purposes too). I would OMIT this for certain blog platforms that don't give you a feed such as Tumblr.
Related
I need to create a universal web scraper to parse articles on the different websites. Of course, I know about XPath, but I want to try to make it universal for any website despite the HTML markup of a page.
I need to determine whether there is an article on the page and if it is - parse a text of title, body and tags (if exists).
Frankly speaking, my knowledge in DS is not very huge, but I assume this task (determine whether it is article, and parsing only needed parts) is possible to solve.
What tools should I use? Any help?
Actually, for the second task, I need to implement something similar that google chrome mobile does. When page is not optimised for mobile, then propose to show the page in adaptive mode (just title, and main content).
If you are using Python, some libraries to look at are:
scrapy, which scrapes data and can extract some of the results) and,
BeautifulSoup, which is more geared towards the extraction part itself.
It is possible to request a version of a website (e.g. for Chrome, Safari, Mobile, old-school systems) by creating a custom header for your scraper.
HAve a look at the relevant documentation, and you can get an idea of how to use headers in scrapy here.
I do not know of any more specialised tools. Your tasks are more analytical and are typically not performed with the use of models for estimating e.g. what content is where on a webpage. This might be an intersting research direction though; to see if you can create a model that generalises across many websites to extract the desired content.
That leads me on to my last point, which is to say that creating a single scraper that works for any website *containing your artile type) is not usually possible. People create websites differently, however they see fit, which means they also change them. This usually leads to a good scraper requiring constant updates as time (and developers) moves on.
EDIT:
Then if you have lots of labelled examples, it might be possible to train a model. The challenge might be the look-back range of the model. For example, a typical LSTM model is given a parameter that tells it how far to look back into the past. It is stored within its memory internally. In your case, you might be looking for a start and end HTML tag of an article, to then extract just that part. These tahs could be thousands of words apart. Something a standard LSTM might not be fit to retain and use.
If you could pose your problem a little differently, then there are other approaches that might be plausible. E.g., you could make it a "question-answer" problem, by saying: I have this HTML, where is the article content? If that sounds ok for your use-case, have a look here for some model based approaches.
I'm willing to use microdata/microformat/etc. for the part of my website which is an online dictionary. Basically I just want to tag word and definition to help search engines to grab the most important data in every page belonging to the dictionary, and maybe have Google use them as "rich snippets" in results page.
Main problem is it's hard to find dedicated vocabulary for words and definitions (no problem for recipes, movies and hotels though) and I'm not sure if I have to use the "http://schema.org/Article" tree for my lexicographic work. (To my mind, it makes sense to tag something when it's specific enough).
I have found something interesting at Yandex, for words and encyclopedia, I want to ask what to do with. See there :
https://yandex.ru/support/webmaster/microdata/what-is-microdata.xml?lang=en
https://yandex.com/support/webmaster/microdata/term-definition-markup.xml
It looks like it is very close to my request. But I'm sorry I dont know what is Yandex... will it work with Google ?
I'm asking here if that page, from Yandex, is a working model, is still on use, what are the pros and cons ? Will Google be able to use the specific vocabulary from Yandex and understand my Yandex-tagged data ? is it worth using that vocabulary for an online dictionary, or is something else I have missed of better use ?
(http://webmaster.yandex.ru/vocabularies/term-def.xml, which should be the vocabulary url, gives me a 404).
One more question, please : am I allowed to write (duplicate) the most important data in the header, something like (I believe I am, because Google microdata testing tool prooves to be able to extract the data from that code) :
<html itemscope itemtype="http://webmaster.yandex.ru/vocabularies/term-def.xml">
<meta itemprop="term" content="My term" />
<meta itemprop="definition" content="My definition" />
Just to mention I was interested, though not happy with these close discussions :
https://webmasters.stackexchange.com/questions/55073/what-meta-tag-or-structured-data-should-i-use-for-a-dictionary-web-application
schema.org and an online dictionary
Yandex is Russia's version of Google, and typically they both recognize and honor each other's search engine result implementations.
These articles you are referencing are incredibly outdated; I recommend you seeking out fresher sources, preferably where the term being defined uses the proper HTML element.
Here's the Yandex URL that is 404ing, the Wayback Machine is your friend!
Back to fresher documentation/resources, in this case the correct element as of 2016-10-05 is the <dfn> element. I know you want added semantics, but semantics is the proper place to start, and I'd follow that up by marking the entire dictionary up within a Definition List element, and placing the definition wrapped in the definition element into the <dt>, and the definition's of the term in the corresponding <dd>s.
I wouldn't waste time trying to find the perfect ontology here; implement [rel="tag" Microformat on all of the definitions], you can always come back and add a more desired one.
I've written a blog post about this, but a much more valuable resource is HTML5 Doctor's Glossary impementation, More importantly, view source - view-source:http://html5doctor.com/element-index/ (why stackoverflow doesn't recognize 'view-source' schema is beyond me)
More References/Resources:
Microformats Definition Examples has some very interesting ideas/code snippets
Utilizing the Underused by Semantically Awesome Definition List - Written Prior to HTML5's Redefinition of <dl> but Relevant
I'm new to OSM querying, but would like to query vector data for a large area. Thus I need to limit the results I would like to get by tagging the request.
http://www.informationfreeway.org/api/0.6/way[tag=value][bbox=x,y,z,j]
I'd like to filter for specific tag/values when querying for a way. Though I don't know which tags/values exist. Is there a list listing the most common of them?
You are approaching your problem from the wrong direction. The number of different tags is almost unlimited. According to taginfo there are currently 75 380 856 different tags. I'm pretty sure you are not interested in most of them. Likewise you are probably not even interested in many of the most common tags.
What data do you want to query?
The OSM wiki should be your starting point for generating a list of tags you are interested in. For a generic overview take a look at the map features. Are you interested in streets? Then visit at the highway key. Routing? Then take a look at the routing wiki page.
Always remember that these lists aren't complete. People can use any tag they like (but should use well-established tags whenever possible of course).
Also consider using Overpass API instead of XAPI. Overpass API is much more powerful.
We would like to generate a PDF document for a single page. While only this link talks about this subject (and the other discussion linked from there), the information given is quite slim.
Could anybody share any success stories made so far including source-code?
Has someone succeeded in using wkhtmltopdf?
(we plan to use Magnolia 4.5.6)
After evaluating both Aspose.pdf (commercial product) and iText, we went to use LaTex. We had quite some specific requirements (e.g. two column layout with footnotes, very large table), which were not possible with the two above mentioned products.
We are very happy with this solution, but there are some things to be noted: first and foremost you leave the JVM, and second LaTex is itself another macro language to be learned. The quality of the outcome is very good, although, and we are very happy with that solution.
wkhtmltopdf is used in another project, and the outcome is also good, for more straight forward formatting.
I want in an application with a simple text input, enriched with some marks to include formatting or semantic labeling. I want the syntax as easy as possible and I want to include self-defined labels.
Example:
[bold]Stackoverflow[/bold] is a [tag]good[/tag] resource for programmers.
Tables would be needed too.
HTML/XML and LaTeX are mighty enough to allow this, but too complicated. Wiki-Syntax seems simple, but uses another symbol for each markup, has unclear quoting and every Wiki seems to have another syntax. For tables and similar stuff Wiki becomes very complicated.
Exists a language/syntax, that matches my needs or can be slightly changed to do so? Or do I have to invent something myself? In that case, do you have suggestions?
Definitely do NOT invent your own. There are plenty of simple markup languages already, and users HATE learning new ones. Trust me on this!
I would suggest using one of the following:
Textile
Markdown
BBCode
Make your decision based on your userbase, as well as what tools and parsers are available in your chosen language. For my site, we went with Textile, but I've found that BBCode tends to be the language that most people already know. However, this will vary with different user demographics.
StackOverflow, along with several other sites, uses Markdown. I think it will give you the best balance between features and simplicity.
Let me add ReStructuredText to the list.
An additional benefit of using it is given by the availability of ReStructuredText to Anything service that makes extremely easy to create HTML or PDF versions of the document.
As already pointed out there are a lot of lightweight markup languages (many are listed here: wikipedia article), there should be no need of creating your own.