I need to match an exact phrase in open search server.
which means "Master of business administration" should not match the keyword "business" or "Master" or "administration" or "of".
I need exactly matched results only.
Is it possible using open search server.??
OpenSearchServer uses Lucene as back-end. It supports the same syntax. The double quote applies a proximity query. It means that it look for close words. You can also add a distance tolerance between words (phrase slop), using this syntax:
"master business administration"~2
That way "master OF business administration" will be find. The default phrase slop is 10.
About exact spelling, it depends on which field you apply the search. On a standard OpenSearchServer template, you have two fields: content and contentExact.
"content" stores a "lemmmatized + lowercase" version of the words: "mast of busing administr"
It means you could find: "masterING of business administrATIVE".
"contentExact" stored a lowercase version of the words, keeping the original spelling.
To force using one field use the semicolon syntax:
contentExact:"master of business administration"
You can also mix both parameters:
contentExact:"master of business administration"~2
You can change the default query of OpenSearchServer as well as the semantical filters applied to fields using the web interface.
I hope this help.
Related
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!
Just starting to learn Solr for a project at work and was wondering on how to go about this issue. Our application allows a user to search based on a business name. The business name is comprised of 3 different categories ( English, French and Combined Name ). Based on a single query entered by the user, how would one go about using Solr to provide the most relevant search results? I have looked into fuzzy and proximity searches which seem reasonable enough. Although fuzzy search only applies to a single term, which makes me believe that I would need to split the query into single terms and apply fuzzy search to each and merge the results if I were to use it ? My question is how to best approach the problem ? Thanks!
To provide relevancy to your documents , you need to have a combination of proper boosting queries and your priorities as what relevance means to your use case . If Regex based search is included in use case you may go for NGrams , if exact search is what you seeking for , boosting is important . You can use parameters like phrase slope , mm, and other edismax parameters to your advantage . You may use a combination of title and text content search, with a good combination of boosts . Also , Solr allows you to pass your query in parenthesis, that functions like an SQL IN query , that further boosts relevancy in your documents by sticking to keywords only mentioned in the query . And , at last , if all these doesn't suffice, you may use custom function queries to meet your needs . While doing all this, just keep in mind the Analyzers in schema.xml file are just right and serve the purpose to execute above mentioned queries .
You can go as far down this rabbit-hole as you have time for wrt Business Name search. (Fuzzy, sound-alike, language-specific analysis, weird compounded-terms used as a domain name (eg: getting "EZBake" to match "easy bake", or "1-to-1" to match "one to one" is non-trivial)
Since this sounds like a pre-existing application, I typically look to query logs (when available) to sample the frequency of different types of mismatches (dig out the zero-result search terms and start manually categorizing the high-level issues behind the more common mismatches).
That will provide you with a backlog of "matching use cases to research how to implement" (in the order of maximal benefit, as determined by your sample).
Then you're ready to start burning them down, and asking much more specific questions about how to get Solr to jump through your domain-specific hoops.
Default a submitter (uploader) of a document can add self chosen keywords to that document.
It is also possible to configure DSpace in a way that the submitter has to choose from one or more predefined keywords (controlled vocabulary).
The DSpace manual seems to suggest that you - when configuring - have to choose between free and predefined keywords.
I would like to give the submitter the possibility to choose between one or more predefined keywords. But also that he or she can add one or more self chosen keywords.
Is that possible?
The hierarchical taxonomy feature gives you exactly this:
https://wiki.duraspace.org/display/DSDOC5x/Authority+Control+of+Metadata+Values#AuthorityControlofMetadataValues-HierarchicalTaxonomiesandControlledVocabularies
You can see it in the demo installation on the "subject" field: you have a lookup feature that allows lookup in a tree of subjects, but manually entered values are possible as well.
screencast:
http://screencast.com/t/0Cth3mORwxd
I personally would set this up to use two different metadata fields.
Something like dc.subject.whateverdescribesyourlistoffixedterms -- or even localschema.subject.whateverdescribesyourlistoffixedterms -- for the list of terms the user should select from. Note, for "whateverdescribesyourlistoffixedterms" I would choose something related to the name of the list of terms if at all possible (see example below).
dc.subject for "standard" user-supplied keywords
Then just add both to your input forms, perhaps going with Bram's suggestion of a hierarchical taxonomy for the first.
To give you better advice on what's most appropriate, it would be great if you could give some more details about what you're trying to achieve. For example
Is your list of fixed keywords something that's used beyond your own organisation? If yes, this strongly points to having its own metadata field to me, with the qualifier something that's related to the name of the classification system -- eg, dc.subject.anzsrc for the Australia/New Zealand fields of research codes.
Do you want to mix the two types of keywords in browse/facet options? You can do this even when they're in two separate fields. Have a look at the Discovery search filters & sidebar facets documentation and see how that puts dc.contributor.author and dc.creator into the author facet. The documentation for browse indexes has a similar example in the author browse.
Are both types of subject keywords required for submission? Both optional? One type required, the other type optional? You say in a comment (if I read you correctly) that you want the fixed keywords to be mandatory during submission, while the free-text keywords should be optional. That means they must be in separate metadata fields because otherwise you wouldn't know, if the submitter gives keywords, whether they are from the fixed list of terms or not. If you use separate fields, you can make eg dc.subject.anzsrc a required field in the submission form and dc.subject an optional one.
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.
Consider the following site:
http://maps.google.com
It has a main text input, where the user can type business, countries, provinces, cities, addresses and zip codes. I wonder which is the best way to implement a search like this. I realize that probably Google Maps uses a full text search with all kinds of data in the same table, and it has a chance of having a parser which classifies the input (i.e. between numeric, like zip codes and coordinates, and textual, like business and addresses).
With the data spread in many tables and systems, a parser is essential. The parser could be built from regular expressions, or could be built with IA tools like Artificial Neural Networks and Genetic Algorithms.
Which approach would you recommend?
It might be best to aggregate the data from all of your tables into a search index. Lucene is a free search engine, similar to how Google's search engine works (inverted index), and it should allow you to search by any of those values or any combination of them with relative ease.
http://lucene.apache.org/java/docs/
Lucene comes with its own query language (again, very similar to Google's or any other Internet search sites syntax). The only drawback of using something like Lucene is you would need to build its index. You wouldn't be querying your database directly (which could get very complicated...inverted index are pretty much designed for what your trying to do), so you need to periodically gather up new information from your database and add it to your index. It might also be necessary to rebuild your index to remove unneeded data.
With Lucene, you get a pretty flexible query syntax that most people are familiar with (because pretty much everyone searches the internet), it performs very well, and is not terribly complicated. By using Lucene, you avoid the hit of using regular expressions (which are not the most performant text searching mechanism), and you don't have to write your own parser. Should be a win-win, aside from a little learning curve to build a Lucene index generator and figure out how to query that index.
I'd have the data in one database. If the data got to big or I knew it would be huge, I'd assign an id to each business, address etc, then have other tables which reference this data.
Regular Expressions would only be necessary if the user could define what they want to search for:
business: Argos
But then what happens if they want an Argos in Manchester (Sorry, I'm English), maybe then get the location of the user based on their IP but what happens if they say:
business: Argos Scotland
Now you don't know if the company has two words, or if there is a location next to it. All of this has to be taken into consideration.
P.s Sorry if that made no sense.
You will need to pre process the query before doing a full text search on it. If you are using a GIS database, then you will already have columns like city, areacode, country etc. Convert your query into tokens seperated on space or commas, or both. Then hit individual columns to see match. This way you will know what part of the query is the city, the areacode etc.
You could also try some naive approximation approaches,example - 6 consecutive numbers will probably be an area code. Look for common words like "road" , "restaurant" , "street" etc which will be part of many queries and then use some approximation to figure out what they are looking for. Hope this helps.