Users submit queries & requests . Wolfram Alpha then computes answers from
a knowledge base of curated, structured data that come from other sites.
It generates output by doing computations from its own internal knowledge
base instead of searching the web and returning link.
WolframAlpha thus differs from semantic search engines, which index a large
number of answers and then try to match the question to one.
Using the Mathematica toolkit, Wolfram Alpha can respond to natural
language questions and generate a human-readable answer.
Goal : “Wolfram|Alpha's long-term goal is to make all
systematic knowledge immediately computable and accessible
Some of the explored areas:
WolframAlpha was officially launched on May 18, 2009.
It is the culmination of 5 years of work, and 26 more years of previous development.
December 3, 2009, an iPhone app was introduced.
October 6, 2010 an Android version of the app was released.
It is now available for Kindle Fire and Nook.
Also 71 apps are available which use the Wolfram Alpha engine for specialized tasks.
Technology– the “Four Pillars”
Formalization NLP Visualization
Pillar1 - Curation
Field Experts help the team find the best content sources and
validate the data.
Community input is also accepted, but all the data has to go
through a rigorous validation process before being used.
Almost none of their data comes from the Internet now.
It turned out that curation and data gathering was only 5% of
Pillar 2 - Formalization
Organizing the curated data so that it can be computable.
All these are encoded algorithmically in Wolfram Alpha so that they’re
available when needed.
All the algorithms, models and equations are encoded into functions in
Mathematica, the programming language behind Wolfram Alpha.
Mathematica’s language is able to represent data of all kinds using
arbitrarily structured symbolic expressions
Mathematica already includes a very big set of algorithms and
functions, making it easier to implement new (usually more complex)
Pillar 3 – Natural Language Processing
How could users interact with the system and use
its computing powers? Through human language is
the most natural response.
The problem is not the one we are used to –
instead of trying to make sense of a big set of words,
the system has to map small pieces of human input (queries)
into its large set of symbolic representations.
The implemented solutions generally achieve good results
Pillar 4 – Visualization
Wolfram Alpha’s ability to present results in formats other than text is one of
its most visually appealing features.
Mathematica includes some functionality to deal with this challenge, through
what they call “computational aesthetics”.
This automates, for a specific symbolic representation, what to present and
how to present it
Technology Interesting Facts
Wolfram Alpha is written in 15 million lines of Mathematica code.
Wolfram Alpha runs on more than 10,000 CPUs.
The database currently includes hundreds of datasets, such as "All Current and
The curated datasets are checked for quality either by a scientist or other expert in a
Its server technology is based on Apache Web servers accessing clusters
of webMathematica servers.
On the Server side ,it’s using JSP.
It is all a matter of representing data and mapping queries to the set of
things they can compute about.
Uses an internal and pre-structured database to find the answers to the
Computation brings a lot of value when comparing it to search engines like
Little to no information available about how the system works internally.