I was thinking about text driven search by user input.
often you are searching in a database of addresses, where you can find customers and so on.
has anybody any idea how to find out which of the typed words is the name, which is the street name, which is the company name?
and secondly if the name is a double name like "Lee Harvey", how can I find out that the two words Lee and Harvey belong together?
Same problem with company names like "frank the baker inc."...
Is there any algorithm or best practice strategy?
thanks for links, tutorials, scripts and all other help ;-)
What you basically want is a search engine :) Here are the basic steps you need to follow -
You need to create an 'Inverted Index' of the content you want to be searched on.
The index is 'name'=>'value' pair. You can have this pair in whichever way you want (tuned according to your data & needs.
Eg. for your problem of double names, you could split all your names into single words & index it like so -
'lee'=>'lee harvey'
'harvey'=>'lee harvey'
...
this way when anyone searches for 'lee' they get 'lee harvey'. There are other better approaches to this called "n-gram" indexing. Check it out...
You could possibly build indexes of names, addresses, emails etc & when the user types a query check it against all your indexes with the approach suggested above. After you get the results then merge them. Maybe you could introduce the notion of rank so that you can sort your results & show the most latest or most relevant ones at the top. For this you need to figure out a way to score your terms...
Don't care, just perform full-text search. Then you should check the result items for which field contains the search terms. Also, you may display items in separate lists (terms found int name, term found in address). The only difficulty is if John Smith is living in the John Smiht street, you must decide, which list/lists the result item belongs to.
Related
We have a long list of organizations that we are trying to match to another long list of organizations. Normally, I would just use an index/match formula to match the 2 columns together. However, they both utilize organization names and the spelling can vary mildly.
For example, one column might refer to Organization A as "Tanner Health" and another might refer to it as "Tanner Healthcare". Another example, one column might refer to Organization B as "The Tanner Health Institute" and another might refer to it as "Tanner Health Institute". There are countless examples, but the idea is that we want to try and get the closest possible match between the 2 columns.
It is totally possible that a Organization in column 1 might not exist in column 2, in which case, we don't want it to return anything.
If it helps at all, we also have information about each organizations state and zip code. So, I considered an array formula that matches Zip code, State, and then does the best fuzzy match to the available options.
I am struggling to find a formula that can do a fuzzy match of the 2 columns, and am looking for suggestions on the best way to approach this problem.
I am putting my collection of some 13000 books in a mySQL database. Most of the copies I possess
can be identified uniquely by ISBN. I need to use this distinguishing code as a foreign key into
another database table.
However, quite a few of my books date from pre-ISBN ages. So for these, I am trying to devise a
scheme to uniquely assign a code, sort of like an SKU.
The code would be strictly for private use. It should have the important property that, when I
obtain a pre-ISBN publication, I could build the code from inspecting the work, and based on the
result search the database to see if I already have other copies in my possession.
Many years ago I think I saw a search scheme for some university(?) catalogue, where you could
perform a search of a title based on a concatenated string' (or code) that was made up of let's
say 8 letters from the title, and 4 from the author, and maybe some other data. For example,
to search 'The Nature of Space and Time' by Stephen Hawking and Roger Penrose you might perform
a search on the string 'Nature SHawk', being comprised of 8 characters from the title (omitting
non-filing words and stopwords) and 4 from the author(s).
I haven't been able to find any information on such scheme's, or whether or not such an approach
was standardized in any way.
Something along these lines could be made up of course, but I was wondering if people here have
heard of such schemes, of have ideas on how to come to a solution to this.
So keep in mind the important property of 'replicability': using the scheme, inspection of a pre-
ISBN dated work should --omitting very special or exclusive cases-- in general lead to a code
that can singly be used to subsequently determine if such a copy is already in the database.
Thank you for your time.
Just use the Title (add Author and Publisher as options) and a series id to produce a fake isbn. Take a look at fake_isbn.
NOTE: use the first digit as a series id but don't use 9!
I want to do something in neo4j that I hope will work ok: I want to make "fuzzy" path matches; the links will sometimes count as a relationship, and sometimes not, depending on the query.
Here's an example: let's say I have a (p:Person)-[:HAS]->(n:Name). A search has found a Person (say, by phone number). I want to go from this Person to other Persons with similar names, to get their phone numbers. Also, I want the similarity to be adjustable, so the user might ask to match very similar names, or not very similar names.
I could get the first person's name, and then do a search against other names with some lucene patterns - this is easy enough, but it means doing a full lucene search on the Name values, which in my use case is not ideal as I think it might be a bit slow (there are very many names - let's say a billion, remembering this is just an example). I hope there is a better way.
One approach I can imagine is having a "similarity" relationship between Names. Whenever a new Name node is added, we check for similar names and link them (creating these relationships would be slow, but we could push it onto a batch process, and it's ok if it takes some minutes). We would only link names that were fairly similar (so the number of links would hopefully not get too large). I suppose we could then craft a query on this, matching similarities greater than my threshold. Something like this:
MATCH (p1:Person {phone:"555-234234"})-->(n1:Name)-[s:SIMILAR]->(n2:Name)-->(p2:Person)
WHERE s.matchLevel >=2
RETURN p2.phone;
Is this approach better or worse than just doing the lucene search? Has anyone else wanted to do something like this?
Also, based on the suggestion at http://graphaware.com/neo4j/2013/10/24/neo4j-qualifying-relationships.html, I believe I'll be better off having many relationships (SIMILAR_1, SIMILAR_2 ..) instead of using a "match level" attribute on my relationship.
BTW, I know there are many similar questions to this (eg. Neo4j 2 Cypher fuzzy search), but afaik this exact question isn't on stackoverflow (and I have looked).
I was reading through rails tutorial (http://ruby.railstutorial.org/book/ruby-on-rails-tutorial#sidebar-database_indices) but confused about the explanation of database indicies, basically the author proposes that rather then searching O(n) time through the a list of emails (for login) its much faster to create an index, giving the following example:
To understand a database index, it’s helpful to consider the analogy
of a book index. In a book, to find all the occurrences of a given
string, say “foobar”, you would have to scan each page for “foobar”.
With a book index, on the other hand, you can just look up “foobar” in
the index to see all the pages containing “foobar”.
source:
http://ruby.railstutorial.org/chapters/modeling-users#sidebar:database_indices**
So what I understand from that example is that words can be repeated in text, so the "index page" consists of unique entries. However, in the railstutorial site, the login is set such that each email address is unique to an account, so how does having an index make it faster when we can have at most one occurrence of each email?
Thanks
Indexing isn't (much) about duplicates. It's about order.
When you do a search, you want to have some kind of order that lets you (for example) do a binary search to find the data in logarithmic time instead of searching through every record to find the one(s) you care about (that's not the only type of index, but it's probably the most common).
Unfortunately, you can only arrange the records themselves in a single order.
An index contains just the data (or a subset of it) that you're going to use to search on, and pointers (or some sort) to the records containing the actual data. This allows you to (for example) do searches based on as many different fields as you care about, and still be able to do binary searching on all of them, because each index is arranged in order by that field.
Because the index in the DB and in the given example is sorted alphabetically. The raw table / book is not. Then think: How do you search an index knowing it is sorted? I guess you don't start reading at "A" up to the point of your interest. Instead you skip roughly to the POI and start searching from there. Basically a DB can to the same with an index.
It is faster because the index contains only values from the column in question, so it is spread across a smaller number of pages than the full table. Also, indexes usually include additional optimizations such as hash tables to limit the number of reads required.
I'm building an application that takes inputs from SMS text thru Twilio. I'd like to build a table the matches the incoming SMS body with the appropriate response.
For example, imagine I'm building an NFL text message thing.
Someone texts in 'Redskins' and we text back, "The Redskins play at FedEx field"
Someone texts in 'Colts' and we text back, "The Colts are the pride of Indiana."
Here's the tricky part:
Of course, our Rails app is going to need to interpret the incoming team names through Regular Expressions, as many people will text in: Redskins or REDSKINS or REDSKIN or Redskin or REDskin.....
With one or two teams, one could just hardcode the RegExp and response into the controller...but with 30 teams, that seems wrong. (And with 120 entries -- say all pro sports-- even worse).
Does any one have any tips on getting the team names from the input stage, thru the DB table stage with a 'RegExp' conversion in the middle?
Thanks in advance.
for a modest number of keywords, I recommend a two table approach with Keywords and Aliases, always stores in lower case. Convert input to lower case. For each Keyword (say, redskins) you manually add 5-10 variations (including the correct one) in Aliases all of which have Alias.keyword_id = the id of the keyword. So you simply search Alias for the user input, and if you find a match you have the keyword_id of the keyword.
It has two advantages: fast and easy to extend... i fyou log the "no matches" you'll get a list of new aliases to add once to the dbase. MUCH easier and more reliable than trying to do via regex.
I don't think you want regexps here. What about spelling errors? For helpfulness (esp coming from a txt msg) I think you want to allow shortenings too.
Maybe a Soundex-based library or spelling correction thing would be best. You want a nearest match algorithm not a patterned match one.
If the text message is not too long, you should first chop that into words, and then take an intersection with the list of team names.
array_of_team_names = %w(Redskins Colts ... ) # keep it all capitalized
'cOLts blah blah'.scan(/\w+/).map{|word| word.capitalize} & array_of_team_names
# => ['Colts']
If you want to handle mistypes as suggested by drysdam, or if you want to handle larger text with more accuracy, you should use some library specific to that.
I think what you are asking is "how do I avoid hardcoding a regexp into my code, since I might have a lot of them, and they are really a data element"?
If you want to do the matching with regexp, you should note that you can create a regexp from a string, so you could easily have a table that contains column of regexp in string form. You can then dynamically create the array of regexp objects that you'd be using to search the incoming string with. The trick is what to do when you have a match. You'll need to develop a set of rules (yet another table) that basically says which response to pick based on incoming text. For example, if your rule is simply "match based on the team name and say where they play", that's pretty easy. Each regexp that you are searching for maps to exactly one action ("The Bears play in Chicago"). If your rules are more complicated (look for the Bears, and then look to see if the word "schedule" is in there too as well as "first game(s)", then you'd need another table that maps a collection of matches to a response.