I tried Bach and it appears that it just returned Wikipedia articles that mentioned Bach. My initial expectations were more along the lines of Google Sets. What are the shapes on the homepage for? It was kind of intriguing that they seemed to do something, but I didn't know what.
You have to click on the bold links to "drill-down". So, if you click on "johann sebastian bach", it will give start to give you results such as: "Vivaldi's Cello" and "History of Germany" Currently, we're limited to running on one server. Therefore, the algorithm is restricted to running on the english Wikipedia corpus. It appears that the "skip slope" metaphor with the shapes was a bad idea. Each shape is a ski-slo…
A new experimental "similarity search" algorithm. Thoughts?
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Re: A new experimental "similarity search" algorithm. Thoughts?
#12Earlier quoted context omitted.
You have to click on the bold links to "drill-down". So, if you click on "johann sebastian bach", it will give start to give you results such as: "Vivaldi's Cello" and "History of Germany" Currently, we're limited to running on one server. Therefore, the algorithm is restricted to running on the english Wikipedia corpus. It appears that the "skip slope" metaphor with the shapes was a bad idea. Each shape is a ski-slo…
The difficult rating thing is really really clever! I wish Google had that. Only problem is that I usually ski near home (in Europe) and the convention over there is colors (green, blue, red and black) instead of shapes. Still, kudos for that feature!
I'm trying to think of a way to make the functionality of the shapes more obvious.
Re: A new experimental "similarity search" algorithm. Thoughts?
#13Besides the fact that it thinks too long, what is new about it, what are you trying to achieve? I search for "Test" and results were not really relevant.
Thank you for trying it out. Re: performance - It's mining through ~40gb of data on server with 8gb of ram. - Also, we're not using caching of search results -- it computes on-the-fly for each query. - If we can get a hold of more servers, we should be able to bring down the query time below 1 second. Re: query "Test" - You have to search for something you're interested in.
Think about it in order for you guys to have a meaningful search engine you will need data in the TB range how many servers will you need then? Spend more time fine tuning your search algorithm and processing you should get better performance out of what you have now. Then your repository will grow proportionally to your resources and you should be fine.
When I said the search for 'test' did not return good result I meant you should do more work on relevancy.
Re: A new experimental "similarity search" algorithm. Thoughts?
#14Earlier quoted context omitted.
Thank you for trying it out. Re: performance - It's mining through ~40gb of data on server with 8gb of ram. - Also, we're not using caching of search results -- it computes on-the-fly for each query. - If we can get a hold of more servers, we should be able to bring down the query time below 1 second. Re: query "Test" - You have to search for something you're interested in.
From my own experience 40GB and 8GB RAM is very good it should be enough for better performance. I don’t think you need more servers at this time. Think about it in order for you guys to have a meaningful search engine you will need data in the TB range how many servers will you need then? Spend more time fine tuning your search algorithm and processing you should get better performance out of what you have now. Then…
PLSI tends to perform very well on more specific queries, such as "Paul Graham", "silicon graphics", etc...
The problem with PLSI is that it is extremely computationally expensive -- which is why most internet-scale search engines don't use it.
Our innovation was figuring out some tricks that have allowed us to improve performance dramatically. However, there is obviously still room for improvement.
Our goal is to satisfy 80% of the queries with decent results -- and to leave the other 20% (square, etc...) to someone else.
The interesting thing about PLSI is that it's able to rank documents from the text alone -- ignoring the link structure and other metadata.
Therefore, we're thinking our algorithm will make the most sense in situations where there is lots of textual data without web-like link metadata.
The two scenarios that come to mind where people need to text-mine documents outside the metadata-rich web are: (1) windows file shares on corporate intranets (2) large volumes of legal documents inside law firms
Text-mining wikipedia is a proof-of-concept at this point