4 ms·

Ask HN: How do you implement a Watson?

4 points by sown 16y ago

I want to implement my own version, which will probably be very crappy. But I'd like to try anyways with the goal that I'll learn something.

emef 16y ago
Probably start with a good knowledge representation. Facts need to be categorized and linked to one another. I assume a solid ontology would be key. A way to process your training data to fit your knowledge graph needs to be unambiguous. No sense it teaching it anything if it becomes full of contradictions. Finding a dataset for it to learn would be tough as well. Given the breadth of possible questions, you'll need to parse huge encyclopedias and/or wikipedia. Finally, a way to efficiently query this massive amount of data. If it has to come up with an answer faster than its competitors, it better be able to lookup information pretty damn fast.
th0ma5 16y ago
There are some architectural diagrams and other info in the Quora thread here: http://www.quora.com/IBM-Watson/Whats-the-system-architecture-of-the-IBM-Watson http://www.quora.com/IBM-Watson/Whats-the-system-architectur...
- sown 16y ago
  I saw those but I am wondering if those aren't at the level needed for an implementation, especially for an imbecile such as myself. :) I'm going to start with information retreival book I found at stanford but something tells me I need much more than just that!
  - th0ma5 16y ago
    No fair enough. I guess it is all in the field of search or natural language processing. For NLP I recommend the NLTK tutorial for Python as an intro, and for search I would recommend checking out Apache Lucene. Perhaps to implement you need to tear concepts from those fields into your own ideas tailored for the task at hand. If you choose Hadoop as job management and distribution, it would scale wide, to a threshold of overhead. If you just want highly specialized on a smaller dataset, and traceable, and I might get shunned for this, heh, but I recommend looking at RDFS and OWL as a somewhat approachable something in the realm of description logic, but that's too formal, maybe. Creating ngrams and other NLP techniques may be pretty neat to play around with, but at smaller scales, it is hard for me to envision something simple that is really any better than just free text search.
gojomo 16y ago
I think you could get pretty far (even reproducing some of Watson's errors) probing a full-text index of a local copy of Wikipedia. My reasoning with examples is here: http://memesteading.com/2011/02/16/ibm-watson-overprovisioned-big-iron/ http://memesteading.com/2011/02/16/ibm-watson-overprovisione... Apparently the open-source Apache UIMA and Hadoop projects are key parts of Watson's preprocessing and live operation: https://blogs.apache.org/foundation/entry/apache_innovation_bolsters_ibm_s https://blogs.apache.org/foundation/entry/apache_innovation_...

4 points by sown 16y ago

I want to implement my own version, which will probably be very crappy. But I'd like to try anyways with the goal that I'll learn something.

emef 16y ago

Probably start with a good knowledge representation. Facts need to be categorized and linked to one another. I assume a solid ontology would be key. A way to process your training data to fit your knowledge graph needs to be unambiguous. No sense it teaching it anything if it becomes full of contradictions. Finding a dataset for it to learn would be tough as well. Given the breadth of possible questions, you'll need to parse huge encyclopedias and/or wikipedia. Finally, a way to efficiently query this massive amount of data. If it has to come up with an answer faster than its competitors, it better be able to lookup information pretty damn fast.

th0ma5 16y ago

There are some architectural diagrams and other info in the Quora thread here: http://www.quora.com/IBM-Watson/Whats-the-system-architecture-of-the-IBM-Watson http://www.quora.com/IBM-Watson/Whats-the-system-architectur...

sown 16y ago
I saw those but I am wondering if those aren't at the level needed for an implementation, especially for an imbecile such as myself. :) I'm going to start with information retreival book I found at stanford but something tells me I need much more than just that!
- th0ma5 16y ago
  No fair enough. I guess it is all in the field of search or natural language processing. For NLP I recommend the NLTK tutorial for Python as an intro, and for search I would recommend checking out Apache Lucene. Perhaps to implement you need to tear concepts from those fields into your own ideas tailored for the task at hand. If you choose Hadoop as job management and distribution, it would scale wide, to a threshold of overhead. If you just want highly specialized on a smaller dataset, and traceable, and I might get shunned for this, heh, but I recommend looking at RDFS and OWL as a somewhat approachable something in the realm of description logic, but that's too formal, maybe. Creating ngrams and other NLP techniques may be pretty neat to play around with, but at smaller scales, it is hard for me to envision something simple that is really any better than just free text search.

gojomo 16y ago

I think you could get pretty far (even reproducing some of Watson's errors) probing a full-text index of a local copy of Wikipedia. My reasoning with examples is here: http://memesteading.com/2011/02/16/ibm-watson-overprovisioned-big-iron/ http://memesteading.com/2011/02/16/ibm-watson-overprovisione... Apparently the open-source Apache UIMA and Hadoop projects are key parts of Watson's preprocessing and live operation: https://blogs.apache.org/foundation/entry/apache_innovation_bolsters_ibm_s https://blogs.apache.org/foundation/entry/apache_innovation_...