I want to design a Semantic Search engine for my final year Master's degree. I have been doing a fair amount of reading both casually on the web and academic papers so I am not a total noob in this field.
My aim is to build a semantic search engine, which parses out the HTML content into its equivatlent RDF triples,stores the triples in a triplestore, through which the engine will try to respond to the query fired using SPARQL. I want to do something out of the box unlike the other students . So, I decided to build a semantic search engine.
Right now, I had a running search engine using Solr which performs keyword search, what I want to do is the semantic search. I know some open source tools regarding Web 3.0 but not sure whether they will be compatible with Solr or not.
So, can you please provide me some help for building the same.
Thanks.
Regards
Although it sounds hard, but you will not be able to capture everything.
You need a lot of data. Of course, there already is a lot of data arranged in formats like owl and rdf which you may use (e.g. WordNet, Yago, GeoNames etc), but although they are of huge size, they only focus on very small portions of a possible discourse universe.
Developing a good semantic search takes a lot of resources and brain power. Projects, like for example KompParse at the German Research Center for Artificial Intelligence, which only focus on a small part of human conversation (gossip or buying furniture) have been running for several years with several employees by now and are still just "ok".
Understanding semantics has already been implemented in different search engines, take google for example, or wolfram alpha. So this topic might not even be as much "out of the box" as you think.
So I will go with user723630 and strongly advise you, to focus on a smaller topic. You will still achieve a lot, but you will not get frustrated.
Related
I am new in development and I need your advice.
Our student team is going to develop an application for online restaurant booking, where also will be search tool (restaurant and dishes search).
We want to use modern search tool like Lucene, but we are not sure if it is what we really need.
Due to knowledge information, this is more for text search with different kinds of indexes and so on, while our app will make search in database. BUT, if we want to add new features in future, I guess we need good search engine background today.
So, let me know if Lucene is able to do "select" operations or something like it, or this technology is just for text searches?
Sedond question, what can you advise in realisation of this feature? Where to start with?
Thank you in advance.
It all depends. You usually don't start with Lucene and Solr, you use it to attain a goal or implement a specific behavior you need. Usually Solr is your secondary storage, built from your primary database - i.e. you're inserting data into Solr to solve a specific need, for example proper full text search with relevancy scoring.
If you're just starting up, go with the technology you know - i.e. usually a regular RDBMS. You can then attach Solr if you need those features that they're really good at, and wait with introducing new technology until it's necessary. The need first, then the technology. Maybe Lucene/Solr isn't the right technology for what you end up needing when you get to that point.
One of the main tenants of modern development is "YAGNI" - You Ain't Gonna Need It. You implement features when you need them, not for some imagined behavior that may or may not show up down the road.
I feel like i should almost give a friggin synopsis to this/these lengthy question(s)..
I apologize if all of these questions have been answered specifically in a previous question/answer post, but I have been unable to locate any that specifically addresses all of the following queries.
This question involves data extraction from the web (ie web scraping, data mining etc). I have spent almost a year doing research into these fields and how it can be applied to a certain industry. I have also familiarized myself with php and mysql/myphpmyadmin.
In a nutshell I am looking for a way to extract information from a site (probably several gigs worth) as fast and efficiently as possible. I have tried web scraping programs like scrapy and webharvey. I have also experimented with programs like HTTrack. All have their strengths and weaknesses. I have found that webharvey works pretty good yet it has its limitations when scraping images that are stored in gallery widgets. Also I find that many of the sites I am extracting from use other methods to make mining data a pain. It would take months to extract the data using webharvey. Which I can't complain given that I'd be extracting millions of rows worth of data exported in csv format into excel. But again, images and certain ajax widgets throw the program off when trying to extract image files.
So my questions are as follows:
Are there any quicker ways to extract said data?
Is there any way to get around the webharvey image limitations (ie only being able to extract one image within a gallery widget / not being able to follow sub-page links on sites that embed their crap funny and try to get cute with coding)?
Are their any ways to bypass site search form parameters that limit the number of search results (ie obtaining all business listings within an entire state instead of being limited to a county per search form restrictions)**
Also, this is public information so therefore it cannot be copyrighted; anybody can take it :) (case in point: Feist Publications v. Rural Telephone Service). Extracting information is extracting information. Its legal to extract as long as we are talking facts/public information.
So with that said, wouldn't the most efficient method (grey area here) of extracting this "public" information (assuming vulnerabilities existed), be through the use of sql injection?... If one was so inclined? :)
As a side question just how effective is Tor at obscuring ones IP address? Lol
Any help, feedback, suggestions or criticism would be greatly appreciated. I am by no means an expert in any of the above mentioned fields. I am just a motivated individual with a growing interest in programming and automation who has a lot of crazy ideas. Thank you.
You may be better off writing your own Linux command-line scraping program using either a headless browser library like PhantomJS (JavaScript), or a test framework like Selenium WebDriver (Java).
Once you have your scrape program completed, you can then scale it up by installing it on a cloud server (e.g. Amazon EC2, Linode, Google Compute Engine or Microsoft Azure) and duplicating the server image to as many are required.
I'm going to create a fairly large (from my point of view anyway) web project with a friend. We will create a site with roads and other road related info.
Our calculations is that we will have around 100k items in our database. Each item will contain some information like location, name etc. (about 30 thing each). We are counting on having a few hundred thousand unique visitors per month.
The 100k items and their locations (that will be searchable) will be the main part of the page but we will also have some articles, comments, news and later on some more social functions (accounts, forums, picture uploads etc.).
We were going to use Google AppEngine to develop our project since it is really scalable and free (at least for a while). But I'm actually starting to doubt that AppEngine is right for us. It seems to be for webbapps and not sites like ours.
Which system (language/framework etc.) would you guys recommend us to use? It doesn't really mater if we know the language since before (we like learning new stuff) but it would be good if it's something that is future proof.
I think that GAE can do the job. Google claims that Google App Engine is able to handle 5 million visitors for free and you will have to start paying only if you exceed their free quota.
It's also pretty easy to get started. If you don't have experience on administrating websites and choose a regular hosting service, you will have to worry about several things that you don't even imagine now.
My only concern would be with respect of the kind of data and queries you will have to do, since it does not have a relational database. Anyway, there is an open source project for GAE, called GeoModel that gives GAE the ability to do complex geo spacial queries, like proximity fetch. Have a look at their tutorial and the demo app.
About your impression that GAE was intended only for small web apps, there are a couple of CMS that run on it.
Good luck!
If once of your concerns is scalability, and you don't want to depend on expensive or commercial tools, I would recommend that you take a look at this tech stack:
Erlang - A programming language designed for concurrency and distribution.
Nitrogen - An Erlang web framework with a lot of cool stuff, like transparent AJAX.
NoSQL scalable databases, such as CouchDB or Riak - Save the the hassle of SQL code and are more scalable than plain MySQL. Both has direct native Erlang API.
To be honest, I don't know if this tool set is your cup of tea; These are not mainstream solutions. I just suggest these to everyone who ask about size-sensitive web applications.
All serious web frameworks will provide you with what you need. The real issues (for example scalability) might be tackled in a different way depending on what you use, but you wont be limited if you choose a well-known one. The choice of database system might be more important for that (sql vs nosql), even if both of those will do fine too.
It's all about
knowing how to use
enjoying to use
the tool(s) you've chosen.
In either case, name-dropping some suggestions:
Rails (Ruby)
Django (Python)
Nitrogen (Erlang)
ASP.NET MVC (C#)
And please note, if you really want to learn everything from the bottom, you'd be fine with any of these (or one of the other gazillion out there). But if you want to perform your best, choose one that supports a language you know well or uses techniques/tools you have experience of etc. Think twice about how you value this is fun and we learn a lot against we want to be productive and do a really good job.
It is known that google has best searching & indexing algorithm.
The also have good relevancy.
They are also quicker in getting down the latest results.
All that's fine.
What programming language (c, c++, java, etc...) & database (oracle, MySQL, etc...) have they used in achieving this (since they have to manipulate with volume of data quickly and effectively)?.
Though I'm not looking for their in-depth architecture (if in case violates their company policies) an overview of all such things could be useful.
Anybody please add you valuable suggestions and insight on this?
Google internally use C++, Java and Python. See Rhino on Rails:
One of the (hundreds of) cool things
about working for Google is that they
let teams experiment, as long as it's
done within certain broad and
well-defined boundaries. One of the
fences in this big playground is your
choice of programming language. You
have to play inside the fence defined
by C++, Java, Python, and JavaScript.
Google's search algorithm is essentially MapReduce, which stems from functional programming techniques, implemented in C++.
Google has its own storage mechanism for this called the Google File System.
Mainly pigeons:
PigeonRank's success relies primarily on the superior trainability of the domestic pigeon (Columba livia) and its unique capacity to recognize objects regardless of spatial orientation. The common gray pigeon can easily distinguish among items displaying only the minutest differences, an ability that enables it to select relevant web sites from among thousands of similar pages.
Relevance of search results is governed by quality of information retrieval algorithms they use, not the programming language.
But C++ is what most of their backend code is written in (for most services).
They don't use any off-the-shelf RDBMS products for data storage. All of that is written in-house.
Check it out, the Bigtable.
Screen scraping seems like a useful tool - you can go onto someone else's site and steal their data - how wonderful!
But I'm having a hard time with how useful this could be.
Most application data is pretty specific to that application even on the web. For example, let's say I scrape all of the questions and answers off of StackOverflow or all of the results off of Google (assuming this were possible) - I'm left with data that is not very useful unless I either have a competing question and answer site (in which case the stolen data will be immediately obvious) or a competing search engine (in which case, unless I have an algorithm of my own, my data is going to be stale pretty quickly).
So my question is, under what circumstances could the data from one app be useful to some external app? I'm looking for a practical example to illustrate the point.
It's useful when a site publicly provides data that is (still) not available as an XML service. I had a client who used scraping to pull flight tracking data into one of his company's intranet applications.
The technique is also used for research. I had a client who wanted to compare the contents of several online dictionaries by part of speech, and all of these sites had to be scraped.
It is not a technique for "stealing" data. All ordinary usage restrictions apply. Many sites implement CAPTCHA mechanisms to prevent scraping, and it is inappropriate to work around these.
A good example is StackOverflow - no need to scrape data as they've released it under a CC license. Already the community is crunching statistics and creating interesting graphs.
There's a whole bunch of popular mashup examples on ProgrammableWeb. You can even meet up with fellow mashupers (O_o) at events like BarCamps and Hack Days (take a sleeping bag). Have a look at the wealth of information available from Yahoo APIs (particularly Pipes) and see what developers are doing with it.
Don't steal and republish, build something even better with the data - new ways of understanding, searching or exploring it. Always cite your data sources and thank those who helped you. Use it to learn a new language or understand data or help promote the semantic web. Remember it's for fun not profit!
Hope that helps :)
If the site has data that would benefit from being accessible through an API (and it would be free and legal to do so), but they just haven't implemented one yet, screen scraping is a way of essentially creating that functionality for yourself.
Practical example -- screen scraping would allow you to create some sort of mashup that combines information from the entire SO family of sites, since there's currently no API.
Well, to collect data from a mainframe. That's one reason why some people use screen scraping. Mainframes are still in use in the financial world and often it's running software that has been written in the previous century. The people who wrote it might already be retired and since this software is very critical for these organizations, they really hate it when some new code needs to be added. So, screenscraping offers an easy interface to communicate with the mainframe to collect information from the mainframe and then send it onwards to any process that needs this information.
Rewrite the mainframe application, you say? Well, software on mainframes can be very old. I've seen software on mainframes that was over 30 years old, written in COBOL. Often, those applications work just fine and companies don't want to risk rewriting parts because it might break some code that had been working for over 30 years! Don't fix things if they're not broken, please. Of course, additional code could be written but it takes a long time for mainframe code to be used in a production environment. And experienced mainframe developers are hard to find.
I myself had to use screen scraping too in a software project. This was a scheduling application which had to capture the output to the console of every child process it started. It's the simplest form of screen scraping, actually, and many people don't even realize that if you redirect the output of one application to the input of another, that it's still a kind of screen scraping. :)
Basically, screen scraping allows you to connect one (web) application with another one. It's often a quick solution, used when other solutions would cost too much time. Everyone hates it, but the amount of time it saves still makes it very efficient.
Let's say you wanted to get scores from a popular sports site that did not offer the information available with an XML feed or API.
For one project we found a (cheap) commercial vendor that offered translation services for a specific file format. The vendor didn't offer an API (it was, after all, a cheap vendor) and instead had a web form to upload and download from.
With hundreds of files a day the only way to do this was to use WWW::Mechanize in Perl, screen scrape the way through the login and upload boxes, submit the file, and save the returned file. It's ugly and definitely fragile (if the vendor changes the site in the least it could break the app) but it works. It's been working now for over a year.
One example from my experience.
I needed a list of major cities throughout the world with their latitude and longitude for an iPhone app I was building. The app would use that data along with the geolocation feature on the iPhone to show which major city each user of the app was closest to (so as not to show exact location), and plot them on a 3D globe of the earth.
I couldn't find an appropriate list in XML/Excel/CSV type format anywhere easily, but I did find this wikipedia page with (roughly) the info I needed. So I wrote up a quick script to scrape that page and load the data into a database.
Any time you need a computer to read the data on a website. Screen scraping is useful in exactly the same instances that any website API is useful. Some websites, however, don't have the resources to create an API themselves; screen scraping is the developer's way around that.
For instance, in the earlier days of Stack Overflow, someone built a tool to track changes to your reputation over time, before Stack Overflow itself provided that feature. The only way to do that, since Stack Overflow has no API, was to screen scrape.
The obvious case is when a webservice doesn't offer reverse search. You can implement that reverse search over the same data set, but it requires scraping the entire dataset.
This may be fair use if the reverse search also requires significant pre-processing, e.g. because you need to support partial matching. The data source may not have the technical skills or computing resources to provide the reverse search option.
I use screen scraping daily, I run some eCommerce sites and have screen-scraping scripts running daily to gather product lists automatically from my suppliers wholesale sites. This allows me to have upto date information on all the products available to me from several suppliers and allows me to flag non-economical margins due to price changes.