BrainBoost
is a natural language search engine. Ask BrainBoost questions in plain
English and you’ll get answers in plain English. BrainBoost is
automated and uses no human editorial invention. The legend goes that
BrainBoost was created by 24-year-old software programmer Assaf
Rozenblatt. It took him a year to build it and he built it so that his
fiancé could better do her college research.
For more information and analysis, check out the review of BrainBoost I did for the Search Lounge.
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Hi Assaf, thank you so much for joining me here at the Search
Lounge. I know you started BrainBoost, but what exactly is your role
these days? And can you provide some more background about the size and
structure of the company?
We are a very small team at the moment, with only a handful of developers.
We are still primarily focused on development, but we will be switching
gears soon to the sales and marketing of our licensable AnswerRank
technology.
I am still very hands-on with the software development and continually help improve the technology on an ongoing basis.
A
big issue in Internet search is evaluating the trustworthiness of
sources. This issue is amplified in BrainBoost because the answers are
shown right on the search results page and do not require users to
click through to investigate the trustworthiness of the source. For
example, for the search what is the population of Scotland?,
the first three answers are slightly different (5.2 million, 5.1
million, just over 5 million. Like I said, just a slight difference.)
Maybe if you included a published/crawled date, would that help? Or
some kind of page rank metric? Do you have any suggestions for how
BrainBoost users should address this issue?
We are currently working on a PageRank like system to help identify trustworthy sources.
How
do you evaluate the relevancy and quality of the results that are
returned on BrainBoost? Do you have a formal process in place for doing
this? And, what subjects or types of queries do you think BrainBoost is
particularly good at? How about subjects or types of queries that need
some improvement?
For QA, we compiled a database of common
questions and manually researched the answers for each of them. We then
run the questions through the BrainBoost engine, which in turn
automatically goes out to find answers. Precision is then easily
determined by comparing what percent of the automatically generated
BrainBoost answers match our manually found answers
There really isn’t a question type that is problematic for us at this time.
BrainBoost
is 100% automated, but would you consider blending BrainBoost’s
technology with some editorial content or mapping of results for
certain types of queries?
Extracting answers from
unstructured documents is what really sets us apart from existing
‘Answer Engines’ like Ask Jeeves and the new MSN search. It’s a much
trickier problem to solve, and we are going to continue focusing on it
for the time being.
Can you provide any insight into how
BrainBoost reformulates a query when it sends it to another engine? Any
chance you might be willing to provide an example of how this works?
Query
reformulation helps ensure search engines return web pages that most
likely contain answers somewhere within them. A simple example: “what
does NASA stand for” gets reformulated into “NASA stands for”. This
simple reordering of words (and the conjugation of the verb) greatly
boosts the likelihood that relevant documents are returned by the
engines. With larger and especially multipart questions this can get
very complicated.
There’s something I don’t quite
understand about BrainBoost. I enter a search on BB; BB reformulates my
query and sends the new query against other engines; the other engines
provide results; BB gathers those results and ranks them. OK, so here’s
the question: how does BB take a result from another engine and then
show a different description (and title?) than what I would see on the
other engine? Or am I missing a piece of the puzzle?
BrainBoost
does not just display the results it gathers from other engines. It
merely uses those results as it’s starting point. The core technology
of BrainBoost is a system we call AnswerRank. The AnswerRank system is
given a question and a collection of documents. AnswerRank then
analyzes the documents line by line and automatically extracts the very
best answers from those documents. The top few hundred search results
from the popular engines are what we feed into AnswerRank. BrainBoost
begins where the search engines leave off.
Does BrainBoost give a higher weight to certain sources? How about results from certain engines?
No, not at this time. All sources begin processing with an equal weight.
I’ve noticed that it matters if I don’t format my search like a question. Compare these two queries: population of Scotland vs. what is the population of Scotland?. Is that done on purpose?
BrainBoost
pays close attention to all words in the question. The type of words
you use and the order in which you use them determines what
classification, or algorithm, BrainBoost will use to answer your
question. Whereas most search engines ignore words like ‘what’,
‘where’, ‘when’ and ‘how’, BrainBoost very much relies on them. In this
case, the wording of the two questions resulted in two distinct
classifications.
Sometimes I see repeat phrases being displayed, such as for the query What is BrainBoost, the following phrase is repeated several times:
-BrainBoost is a Question Answering search engine.-
This probably is not too big a deal, and in fact it may even be a good
thing because it shows agreement, but what is your opinion about it?
We
chose not to filter out answers that provide the same information in
slightly different ways. Like you said, it really does help with
identifying agreement towards a specific answer.
I read some helpful information you posted about BrainBoost in a thread on Search Guild.
You wrote: “BrainBoost classifies incoming questions into distinct
categories. Classification enables BrainBoost to predict what lexical
properties the answer will most likely contain.” Can you expound on
this? Do you classify searches based on the subject or topic of the
search? Or do you parse the query to look for clues in the phrasing of
the search? Or…?
Its best to give an example: When asked
“how long do cats live?” BrainBoost recognizes that the user is looking
for sentences that quantify the answer in terms of years/months/weeks
etc. Responding with an answer that talks about inches/feet/centimeters
would not be very intelligent at all. BrainBoost has many dozens of
these types of classifications, all of which help ensure suitable
answers are returned.
It seems like I hear very little
about BrainBoost. Are you purposefully trying to keep a low profile? Or
might that change in the future? I like BrainBoost and since it is so
easy to use I think a lot of other people would like it too.
Yes, we have been trying to keep a low profile. It’s given us the luxury of time we needed to perfect our AnswerRank system.
A
considerable amount of time was also spent on packaging AnswerRank
technology into a licensable software component that can be ‘plugged
into’ any existing keyword-based search system, allowing for companies
to add Question Answering to their existing in-house search.
What
do you see as the current state of natural search engines on the web?
Would you care to predict for us what the world of natural search will
look like a couple years from now?
I think Natural Language question answering mixed with sophisticated personalization is the future of search.
Lastly, what is your favorite drink?
Triple Grande Latte
Assaf, thank you for your time. Is there anything else you would like to add?
Thanks for your time Chris.
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