Courtesy of Computer World
Q and A with SYL
Semantics chief scientist Peter de Vocht
The ability of organisations to search vast volumes of data has
led to an increasing focus on semantic search technology.
Wellington company SYL Semantics is at the forefront of this field
and has so far signed three government customers (which it can't
disclose). SYL chief scientist Peter de Vocht, who developed the
software, speaks four languages and has a master's degree in
computational linguistics. His parents relocated from Europe to New
Zealand when he was in his teens, and he attended Auckland
University. He talks to Randal Jackson.
Why did you develop this software?
Ever since I was in my teens, I was inspired by popular
culture to look at Artificial Intelligence. By the time I was 16
years old I started writing larger computer programs and tried my
hand at writing a natural language system. Very soon I realised I
lacked the necessary knowledge to get anywhere. So when it was time
to go to university, I enrolled in computer science and began
learning all I could.
I realised that the academics of the time didn't really have
any ideas on how to build the system I kept seeing in my mind's
eye. I finished a computer science master's degree in computational
linguistics. Computational linguistics is basically combining
computer programming with human languages (aka. natural
I started training as a commercial programmer, writing a
variety of systems and learning how to write better code all the
time. I had a brief stint in the gaming industry, where I was asked
to choose between my job and my family. After choosing
family-friendly environments, I ended up in Wellington. I still
dabbled with systems to the point where I became a mentor and
Suddenly I had time to continue my thoughts and work on this
system I kept seeing in my mind. I decided to implement one of the
popular artificial intelligence computer languages of the time
called Prolog. I thought that if I could write such a logic system,
it would help me understand the final pieces of the puzzle. It took
me a few times to write it and get it to go. Once I wrote it, I
based the first prototype of SYL on this home-grown Prolog
I decided to look for funding and approached a few people
with my ideas of combining logic programming with human languages.
Search seemed to be the most natural and first application. If
nothing else, computer science had taught me that the understanding
of artificial intelligence was based primarily on
I met Sean Wilson, who evaluated the idea of creating a
platform for language analysis mixed with search. Sean also had a
few innovative ideas about how to construct and run the business
and already had a lot of contacts all over town.
SYL's roadmap is much larger than just search, it was
initially meant to be much like Apple's Siri when I thought of it.
The business world, however, has an appetite for data that is
unmatched with anything else I had ever seen up to that point, and
I decided that these challenges too would keep me busy for a long
What practical problem were you trying to
I've always been interested in helping people with knowledge
assistants. The long-term, far away problem I was trying to solve
was that I wanted people to have intelligent assistants - perhaps
like a virtual personal assistant.
Why is the semantic approach important and
This assistant would need to be able to communicate with a
human being. Our most natural 'interface' is face to face speech.
It's what people have done for thousands of years. There is also
the case for knowledge building when it comes to semantics. For a
computer to be able to help a person, it would need to be able to
'understand' what that person wanted. We do this with knowledge
representation. Our modern computers are good at calculating sums
and performing logical steps (usually of the form 'if this is the
case, then do that').
Human language is complex and hugely ambiguous. When
two people communicate, there is a lot of knowledge they share. Two
people who grew up in the same culture at roughly the same time
have still a lot more in common than, for instance, people from
different cultures or times. So when we communicate, all that
background knowledge is 'assumed' and doesn't have to be repeated
in each sentence.
Computers don't deal well with background knowledge (since
they have no culture, and need to be taught such structures from
the ground up). To teach a machine to interact with us more
naturally, one would have to introduce a mechanism for it to
'reason' at a logical level. Semantics is that last step before
building that knowledge representation for a computer.
Why is the enterprise space an ideal space for
SYL in its current state is a basic language system with
semantics, and without this knowledge representation, though this
is on the roadmap. The future for SYL is a fascinating one where it
would become more sophisticated and able to do more and more with
the knowledge put into it. The enterprise space, too, is a smaller
problem than 'everything' (for example Google's approach). Dealing
with the knowledge in a specific industry enables us to approach
the problem of understanding in a more manageable
We can find out from our customers what they want the system
to do first and work towards a solution that doesn't boil the
Where is semantic technology going in the next five
As you might have already deduced, the goal of semantic
technologies is to bridge that gap between what a human being means
and what a computer needs to construct to use that information to
'learn', adapt and help that person in a meaningful way. I think
that such technologies will proliferate and become common place.
You will see a lot more products trying to manage large amounts of
data more effectively too.
The only way this can be done is through 'understanding'
what is in this information. Once a computer understands or
processes the information it can easily detect duplicates, find
relevant information (as opposed to looking for combinations of
letters - keywords), and linkages between pieces of
Systems like SYL will be able to start answering rudimentary
questions: Who did what, when, and where. The 'why' and 'how' are a
lot harder, but they will follow.
How is this reflected in the way you created
My initial idea for SYL was to be a logical inference
engine. That is - give the computer a clever logic engine first
that can deal with the knowledge problem - the answering of who,
However, once I started writing this system, I soon realised
that the language aspect of the system is much more important and
has the potential to shape this logic. I came to the realisation
that logic is a poor companion for human thought and
I think SYL can be much better than that by not relying
heavily on logic, as other systems seem to automatically do. Human
beings aren't logic engines.
The other challenge of SYL is scalability. There is little
use in having a system that can't process real-time information, or
store more than a few thousand concepts. I've always felt that
working to the limitations of a technology should never be the main
concern when designing a system.
There is of course a practical aspect to this; you do have
to know what limitations are. Since the 1950s computer hardware has
been increasing exponentially in capacity every 18
Taking that into account and relying on it enables one to
make predictions a few years ahead (not too many) on what is
possible. My personal advice is "think big".
PETER DE VOCHT - SNAPSHOT
Favourite mobile device: currently has
an iPhone but moving to Android because of its more open
Car: Toyota Corolla
Most important technology
Who do you most admire? Aram