Here's my situation (or see TLDR at bottom): I'm trying to make a system that will search for user entered words through several documents and return the documents that contain those words. The user(s) will be searching through thousands of documents, each of which will be 10 - 100+ pages long, and stored on a webserver.
The solution I have right now is to store each unique word in a table with an ID (only maybe 120 000 relevant words in the English language), and then in a separate table store the word id, the document it is in, and the number of times it appears in that document.
E.g: Document foo's text is
abc abc def
and document bar's text is
abc def ghi
Documents table will have
id | name
1 'foo'
2 'bar'
Words table:
id | word
1 'abc'
2 'def'
3 'ghi'
Word Document table:
word id | doc id | occurrences
1 1 2
1 2 1
2 1 1
2 2 1
3 2 1
As you can see when you have thousands of documents and each has thousands of unique words, the Word Document tables blows up very quickly and takes way too long to search through.
TL;DR My question is this:
How can I store searchable data from large documents in an SQL database, while retaining the ability to use my own search algorithm (I am aware SQL has one built in for .docs and pdfs) based on custom factors (like occurrence, as well as others) without having an outright massive table for all the entries linking each word to a document and its properties in that document?
Sorry for the long read and thanks for any help!
Rather than building your own search engine using SQL Server, have you considered using a C# .net implementation of the lucene search api's? Have a look at https://github.com/apache/lucene.net
Good question. I would piggy back on the existing solution of SQL Server (full text indexing). They have integrated a nice indexing engine which optimises considerably better than your own code probably could do (or the developers at Microsoft are lazy or they just got a dime to build it :-)
Please see SQL server text indexing background. You could query views such as sys.fulltext_index_fragments or use stored procedures.
Ofcourse, piggy backing on an existing solution has some draw backs:
You need to have a license for the solution.
When your needs can no longer be served, you will have to program it all yourself.
But if you allow SQL Server to do the indexing, you could more easily and with less time build your own solution.
Your question strikes me as being naive. In the first place... you are begging the question. You are giving a flawed solution to your own problem... and then explaining why it can't work. Your question would be much better if you simply described what your objective is... and then got out of the way so that people smarter than you could tell you HOW to accomplish that objective.
Just off hand... the database sounds like a really dumb idea to me. People have been grepping text with command line tools in UNIX-like environments for a long time. Either something already exists that will solve your problem or else a decent perl script will "fake" it for you-- depending on your real world constraints, of course.
Depending on what your problem actually is, I suspect that this could get into some really interesting computer science questions-- indexing, Bayesian filtering, and who knows what else. I suspect, however, that you're making a very basic task more complicated than it needs to be.
TL;DR My answer is this:
** Why wouldn't you just write a script to go through a directory... and then use regexes to count the occurences of the word in each file that is found there?
How is it possible to filter the results of the Google Geocode API and the use of the components parameter for two (or more) countries, without issuing another request.
Example:
http://maps.googleapis.com/maps/api/geocode/xml?sensor=false&components=country:DE,AT&address=wien
gives zero result, as there is no Wien in Germany and the AT after the comma is ignored
http://maps.googleapis.com/maps/api/geocode/xml?sensor=false&components=country:AT,DE&address=wien
is ok, because there is an Wien in Austria
http://maps.googleapis.com/maps/api/geocode/xml?sensor=false&components=country:AT|country:DE&address=wien
gives zero results, as the two component filters are outruling each other
Is there something in the documentation that I overread that makes it possible, or can't I make it at all?
Edit
I tried a workaround with setting the merged bounds of Germany and Austria, but that just weighs the results and not filters them, so I got a result which I didn't want with http://maps.googleapis.com/maps/api/geocode/xml?sensor=false&components=bounds:55.058,17.161|46.372,5.866&address=new%20york. In this case I want a ZERO_RESULTS status.
This is not currently possible. Please vote up issue #4233 if you want to see this happen.
For time being I can suggest three possible workarounds, none of them perfect:
Make multiple requests and merge the result sets manually. This will give the best accuracy, but is obviously the slowest.
Use the manually merged bounds trick in HerrSerker's question, then manually filter out results where the short_name of the address_components element with a types of country is not in your list of countries.
Drop the component filter entirely and include "Austria OR Germany" in your search string. Again, you will want to manually filter the results.
I'm writing a search engine with Lucene.net for a database of ~ 2 million products. I'm using the Snowball Analyzer and so far I've been really impressed with the performance and result sets.
The one issue I can't seem to overcome is detecting missing spaces in search inputs.
For Example:
A User is looking for 'Black Diamond' brand products but they search for 'blackdiamond'.
Since the snowball analyzer creates two separate Tokens for Black Diamond I get 0 results.
What approach can I take to correct this issue? I've looked a bit into the Shingle Analyzer(n-gram) but not sure if that would help.
Is it possible to combine a Shingle Analyzer with the SpellChecker (and would that be an effect solution)? It would be idea if I could just prompt people with a Did You Mean: 'Black Diamond'? link when this occurs.
How about initially running the user query as is, if there are no results (or score is below a certain threshold), run N additional searches (where N is the number of possibilities to break the word in two) showing the user results for the possibility that received the highest score.
I'm running into a conceptual problem that I cannot seem to conquer in my mind.
Let's say I want a user to enter what they're currently wearing into a database via a form. Throwing 'T-Shirt' and 'Blue' in a new row is incredibly easy. However, let's say I want to compare one users against others, and rank in order from most similar to least.
This becomes a huge nightmare when you consider the amount of options available.
Undershirt
Overshirt
Jacket
Scarf/Necklaces
Headwear
Pants
Underwear
Leggings
Socks
Footwear
Accessories
As I see it, I could hard-code in the 11 categories above and let a user make selections from drop-drop boxes tailored to each category. Now, let's use an example of 'Undershirt' and 'Overshirt'. Depending on the person, a long-sleeved shirt could be used as either; they're still wearing one. If I make users put values in categories, User A might put it in one and User B might but it in another category. And they wouldn't get compared because of that, separate categories.
Now, instead of hard-coding in categories (and thus making a limit of how much a user can enter), I could put each item into its own row and search by User ID. But let's say a person enters in shorts one day, and next throws in jeans and a shirt. How can I make sure that they're compared separately (e.g., dress compared to shorts, dress compared to jeans+shirt) and not (dress compared to shorts+jeans+shirt).
As to actually comparing, each item vs. each other could be performed via a 2d lookup table. (Row Dress vs. Column Jeans would net a zero, Row Dress vs. Column Dress would net a one)
The appropriate design for this would depend on the acceptable margin of error. If there is zero acceptable error, then you must present the users with the categories and they specify true/false yes/no for each one or select from a limited set of possible answers.
HANDS:
gloves
mittens
brass knuckles
[Caveat: user could be wearing brass knuckles inside the mittens. You have to take into account
whether values are mutually exclusive or not. Barefoot <> no socks.
Someone who is barefoot is not wearing socks but someone not wearings socks may be wearing docksiders]
FEET1:
anklet socks
sheer stockings
fishnet stockings
ragg wool hiking socks
kneesocks
gym socks
no socks
FEET2:
mocassins
running shoes
sandals
wing-tips
uggs
spike heels
...
HEAD:
sombrero
beret
baseball hat
pirate's hat
beanie
knitted cap
NECK:
scarf
mock turtleneck aka dickie
Et cetera et cetera ad nauseam.
Or if margin of error is very generous, you could allow simple freeform text-entry and match/partial-match on words. Slightly less error : you could set up a synonyms table and match on the synonyms of the supplied words.
As a general rule, get the database design right and worry about reporting later. If this is not just a thought exercise, you may like to say what you are actually comparing, because with the above, a person is quite likely to say "tuxedo" or "evening dress", and let the details be inferred, whereas in some other area, this may not be possible. Even so, it seems that you would need a minimum of three columns (fields) for each item:
Timestamp
Major category (jeans, trousers, skirt)
Item (Levi's, tweeds, mini)
If accuracy is particularly important, you will need a trained interviewer :)
I have just noticed underwear in that list, which is even more complicated, because what would qualify as full underwear for a lady of a certain age is by no means the same as that for a gentleman of ten years.
I have Persons table in SQL Server 2008.
My goal is to find Persons who have almost similar addresses.
The address is described with columns state, town, street, house, apartment, postcode and phone.
Due to some specific differences in some states (not US) and human factor (mistakes in addresses etc.), address is not filled in the same pattern.
Most common mistakes in addresses
Case sensitivity
Someone wrote "apt.", another one "apartment" or "ap." (although addresses aren't written in English)
Spaces, dots, commas
Differences in writing street names, like 'Dr. Jones str." or "Doctor Jones street" or "D. Jon. st." or "Dr Jones st" etc.
The main problem is that data isn't in the same pattern, so it's really difficult to find similar addresses.
Is there any algorithm for this kind of issue?
Thanks in advance.
UPDATE
As I mentioned address is separated into different columns. Should I generate a string concatenating columns or do your steps for each column?
I assume I shouldn't concatenate columns, but if I'll compare columns separately how should I organize it? Should I find similarities for each column an union them or intersect or anything else?
Should I have some statistics collecting or some kind of educating algorithm?
Suggest approaching it thus:
Create word-level n-grams (a trigram/4-gram might do it) from the various entries
Do a many x many comparison for string comparison and cluster them by string distance. Someone suggested Levenshtein; there are better ones for this kind of task, Jaro-Winkler Distance and Smith-Waterman work better. A libraryt such as SimMetrics would make life a lot easier
Once you have clusters of n-grams, you can resolve the whole string using the constituent subgrams i.e. D.Jones St => Davy Jones St. => DJones St.
Should not be too hard, this is an all-too-common problem.
Update: Based on your update above, here are the suggested steps
Catenate your columns into a single string, perhaps create a db "view" . For example,
create view vwAddress
as
select top 10000
state town, street, house, apartment, postcode,
state+ town+ street+ house+ apartment+ postcode as Address
from ...
Write a separate application (say in Java or C#/VB.NET) and Use an algorithm like JaroWinkler to estimate the string distance for the combined address, to create a many x many comparison. and write into a separate table
address1 | address n | similarity
You can use Simmetrics to get the similarity thus:
JaroWinnkler objJw = new JaroWinkler()
double sim = objJw.GetSimilarity (address1, addres n);
You could also trigram it so that an address such as "1 Jones Street, Sometown, SomeCountry" becomes "1 Jones Street", "Jones Street Sometown", and so on....
and compare the trigrams. (or even 4-grams) for higher accuracy.
Finally you can order by similarity to get a cluster of most similar addresses and decide an approprite threshold. Not sure why you are stuck
I would try to do the following:
split up the address in multiple words, get rid of punctuation at the same time
check all the words for patterns that are typically written differently and replace them with a common name (e.g. replace apartment, ap., ... by apt, replace Doctor by Dr., ...)
put all the words back in one string alphabetically sorted
compare all the addresses using a fuzzy string comparison algorithm, e.g. Levenshtein
tweak the parameters of the Levenshtein algorithm (e.g. you want to allow more differences on longer strings)
finally do a manual check of the strings
Of course, the solution to keep your data 'in shape' is to have explicit fields for each of your characteristics in your database. Otherwise, you will end up doing this exercise every few months.
The main problem I see here is to exactly define equality.
Even if someone writes Jon. and another Jone. - you will never be able to say if they are the same. (Jon-Jonethan,Joneson,Jonedoe whatever ;)
I work in a firm where we have to handle exact this problem - I'm afraid I have to tell you this kind of checking the adress lists for navigation systems is done "by hand" most of the time. Abbrevations are sometimes context dependend, and there are other things that make this difficult. Ofc replacing string etc is done with python - but telling you the MEANING of such an abbr. can only done by script in a few cases. ("St." -> Can be "Saint" and "Street". How to decide? impossible...this is human work.).
Another big problem is as you said "Is there a street "DJones" or a person? Or both? Which one is ment here? Is this DJones the same as Dr Jones or the same as Don Jones? Its impossible to decide!
You can do some work with lists as presented by another answer here - but it will give you enough "false positives" or so.
You have a postcode field!!!
So, why don't you just buy a postcode table for your country
and use that to clean up your street/town/region/province information?
I did a project like this in the last centuary. Basicly it was a consolidation of two customer files after a merger, and, involved names and addresses from three different sources.
Firstly as many posters have suggested, convert all the common words and abbreveations and spelling mistakes to a common form "Apt." "Apatment" etc. to "Apt".
Then look through the name and identifiy the first letter of the first name, plus the first surname. (Not that easy consider "Dr. Med. Sir Henry de Baskerville Smythe") but dont worry where there are amiguities just take both! So if you lucky you get HBASKERVILLE and HSMYTHE. Now get rid of all the vowels as thats where most spelling variations occur so now you have HBSKRVLL HSMTH.
You would also get these strings from "H. Baskerville","Sir Henry Baskerville Smith" and unfortunately "Harold Smith" but we are talking fuzzy matching here!
Perform a similar exercise on the street, and apartment and postcode fields. But do not throw away the original data!
You now come to the interesting bit first you compare each of the original strings and give say 50 points for each string that matches exactly. Then go through you "normalised" strings and give say 20 points for each one that matches exactly. Then go through all the strings and give say 5 points for each four character or more substring they have in common. For each pair compared you will end up with some with scores > 150 which you can consider as a certain match, some with scores less than 50 which you can consider not matched and some inbetween which have some probability of matching.
You need some more tweaking to improve this by adding various rules like "subtract 20 points for a surname of 'smith'". You really have to keep running and tweaking until you get happy with the resulting matches, but, once you look at the results you get a pretty good feel which score to consider a "match" and which are the false positives you need to get rid of.
I think the amount of data could affect what approach works best for you.
I had a similar problem when indexing music from compilation albums with various artists. Sometimes the artist came first, sometimes the song name, with various separator styles.
What I did was to count the number of occurrences on other entries with the same value to make an educated guess wether it was the song name or an artist.
Perhaps you can use soundex or similar algorithm to find stuff that are similar.
EDIT: (maybe I should clarify that I assumed that artist names were more likely to be more frequently reoccurring than song names.)
One important thing that you mention in the comments is that you are going to do this interactively.
This allows to parse user input and also at the same time validate guesses on any abbreviations and to correct a lot of mistakes (the way for example phone number entry works some contact management systems - the system does the best effort to parse and correct the country code, area code and the number, but ultimately the user is presented with the guess and has the chance to correct the input)
If you want to do it really good then keeping database/dictionaries of postcodes, towns, streets, abbreviations and their variations can improve data validation and pre-processing.
So, at least you would have fully qualified address. If you can do this for all the input you will have all the data categorized and matches can then be strict on certain field and less strict on others, with matching score calculated according weights you assign.
After you have consistently pre-processed the input then n-grams should be able to find similar addresses.
Have you looked at SQL Server Integration Services for this? The Fuzzy Lookup component allows you to find 'Near matches': http://msdn.microsoft.com/en-us/library/ms137786.aspx
For new input, you could call the package from .Net code, passing the value row to be checked as a set of parameters, you'd probably need to persist the token index for this to be fast enough for user interaction though.
There's an example of address matching here: http://msdn.microsoft.com/en-us/magazine/cc163731.aspx
I'm assuming that response time is not critical and that the problem is finding an existing address in a database, not merging duplicates. I'm also assuming the database contains a large number of addresses (say 3 million), rather than a number that could be cleaned up economically by hand or by Amazon's Mechanical Turk.
Pre-computation - Identify address fragments with high information content.
Identify all the unique words used in each database field and count their occurrences.
Eliminate very common words and abbreviations. (Street, st., appt, apt, etc.)
When presented with an input address,
Identify the most unique word and search (Street LIKE '%Jones%') for existing addresses containing those words.
Use the pre-computed statistics to estimate how many addresses will be in the results set
If the estimated results set is too large, select the second-most unique word and combine it in the search (Street LIKE '%Jones%' AND Town LIKE '%Anytown%')
If the estimated results set is too small, select the second-most unique word and combine it in the search (Street LIKE '%Aardvark%' OR Town LIKE '%Anytown')
if the actual results set is too large/small, repeat the query adding further terms as before.
The idea is to find enough fragments with high information content in the address which can be searched for to give a reasonable number of alternatives, rather than to find the most optimal match. For more tolerance to misspelling, trigrams, tetra-grams or soundex codes could be used instead of words.
Obviously if you have lists of actual states / towns / streets then some data clean-up could take place both in the database and in the search address. (I'm very surprised the Armenian postal service does not make such a list available, but I know that some postal services charge excessive amounts for this information. )
As a practical matter, most systems I see in use try to look up people's accounts by their phone number if possible: obviously whether that is a practical solution depends upon the nature of the data and its accuracy.
(Also consider the lateral-thinking approach: could you find a mail-order mail-list broker company which will clean up your database for you? They might even be willing to pay you for use of the addresses.)
I've found a great article.
Adding some dlls as sql user-defined functions we can use string comparison algorithms using SimMetrics library.
Check it
http://anastasiosyal.com/archive/2009/01/11/18.aspx
the possibilities of such variations are countless and even if such an algorithm exists, it can never be fool-proof. u can't have a spell checker for nouns after all.
what you can do is provide a drop-down list of previously entered field values, so that they can select one, if a particular name already exists.
its better to have separate fields for each value like apartments and so on.
You could throw all addresses at a web service like Google Maps (I don't know whether this one is suitable, though) and see whether they come up with identical GPS coordinates.
One method could be to apply the Levenshtein distance algorithm to the address fields. This will allow you to compare the strings for similarity.
Edit
After looking at the kinds of address differences you are dealing with, this may not be helpful after all.
Another idea is to use learning. For example you could learn, for each abbreviation and its place in the sentence, what the abbreviation means.
3 Jane Dr. -> Dr (in 3rd position (or last)) means Drive
Dr. Jones St -> Dr (in 1st position) means Doctor
You could, for example, use decision trees and have a user train the system. Probably few examples of each use would be enough. You wouldn't classify single-letter abbreviations like D.Jones that could be David Jones, or Dr. Jones as likely. But after a first level of translation you could look up a street index of the town and see if you can expand the D. into a street name.
Again, you would run each address through the decision tree before storing it.
It feels like there should be some commercial products doing this out there.
A possibility is to have a dictionary table in the database that maps all the variants to the 'proper' version of the word:
*Value* | *Meaning*
Apt. | Apartment
Ap. | Apartment
St. | Street
Then you run each word through the dictionary before you compare.
Edit: this alone is too naive to be practical (see comment).