I want to find articles when searched on following keyword:
"maruti sx4 maintenance costs against honda city"
I want a query or php regular expression which can find a article which having below text
"SX4 maintenance cost is lesser because of Maruti. Honda City maintenance is also okay."
i.e i want a function/code which can find article by matching "maintenance cost" ( which is common text )
Please guide me how to do it
Thanks
Satish Kalepu
Straightforward solution
The not-so-efficient solution is to match each of your search term one by one to your complete set of articles. For each new query you would repeat this process.
Use explode to split your query string into an array of individual search terms, stripos too test if the term occurs within the text of an article.
Document Retrieval System
If you want to create a full document retrieval system from scratch, you probably should start by creating an inverted index mapping search terms to documents (articles).
Then for each individual search term you can retrieve matching document.
The document which has most matches would be the output of the search system, or you can rank the found documents by the number of search terms matched.
This simple idea can become more advanced if you take into account word stemming and document/term frequency (i.e. the word "the" is less interesting as a search query than "honda" as almost all documents contain "the" but few contain "honda").
Related
Good evening,
I am facing a small problem whilst trying to build a little search algorithm.
I have a database table containing video game names and software names. Now I would like to add new offers by fetching and parsing xml files on other servers. The issue is:
How can I compare the strings for the product name so it works even if the offer name doesn't match the product name stored in my database up to a 100%?
As an example I am currently using this PHP + SQL code to compare the strings:
$query_GID = "select ID,game from gkn_catalog where game like '%$batch_name%' or meta like '%$batch_name%' ";
I am currently using the like operator in conjunction with two wild-cards to compare the offer name (batch_name) with the name in the database (game).
I would like to know how I can improve on this as this method isn't very failsafe or whatever you want to call it, what happens is:
If the database says the game title is:
Deus Ex Human Revolution Missing Link
and the batch_name says:
Deus Ex Human Revolution Missing Link DLC
the result will be empty/wrong/false ... well it won't find the game in my database at all.
Same goes for something like this:
Database = Lego Star Wars The Complete Saga batch_name = Lego
Star Wars : The Complete Saga
Result: False
Is there a better way to do the SQL query? Or how can I try to get that query working so it can deal with strings that come with special characters (like -minus- & [brackets]) and or characters which aren't included in the names within the database (like DLC, CE...)?
You're looking for fuzzy search algorithms and fuzzy search results. This is a whole field of study. However, there are also some straightforward tutorials to get you started if you take a quick google around.
You might be tempted to try something like PHP's wonderful levenshtein method, which calculates the "closeness" of two strings. However, this would require matching it against every record. If there will be thousands of records, that's out of the question.
MySQL has some matching tools which may help. I see that as I'm writing this, somebody has already mentioned FULLTEXT and MATCH() in the comments. Those are a great way to go.
There are a few other good solutions to look into as well. Storing an index of keywords (with all the articles and helpers like of/the/an/am/is/are/was/of/from removed) and then searching on each word in the search is a simple solution. However, it doesn't produce great results in that the returned values are not weighted well, and it doesn't localize at all.
There are lots of cheap and wonderful third party search tools (Lucene comes to mind) as well that will do most of this work for you. You just call an API and they manage the caching, keywords, indexing, fuzzying, et al for searches.
Here are some SO questions that are related to fuzzy searches, which will help you find more terminology and ideas:
Lightweight fuzzy search library
Fuzzy queries to database
Fuzzy matching on string
fuzzy searching an array in php
MySQL queries, as you found out can use the percent character as a joker (%) in conjunction with the LIKE operator.
You have multiple solutions depending on what you want exactly.
you can make a fulltext search
you can search using language algorithm like soundex
you can search by keywords
Remember that you can make a search in multiple passes (search for exact match, then percent on every side, explode in words then insert % between every word, search by keyword, etc.) depending if exact match has priority over close search, etc.
I am trying to optimize my search engine. Right now, I am running a strcmp between the search words the user entered and keywords stored in the database. I am trying to come up with a way so that the more matches the users search words has with the keywords the sooner it will show up in the search results.
For example, if the user search for "red apple painting" and I have two entries for that item with the following keywords 1. "old apple painting green" 2. "apple painting red new york" I would like the second entry to come up first in the search result because all of the users search words were found in the keywords stored in the db.
Any help on how I can achieve this?
Take a look at full text search.
You may also want to consider an external text search engine such as Lucene or Sphinx.
you need to create an index of words. The index would contain word id, doc id, number of hits, position of hits. Then the searcher will be able to give results like you want. There are free indexing tools available in market. But if you want to develop your own then follow the original paper bt google founders-
http://infolab.stanford.edu/~backrub/google.html
Find applicable keywords with accurate seek site visitors capability.
Create and optimize pages for engines like google and customers alike.
Make sure your internet site is offered to each bots and human beings.
Build applicable links from different notable web sites.
What I am trying to implement is a rather trivial "take search results (as in title & short description), cluster them into meaningful named groups" program in PHP.
After hours of googling and countless searches on SO (yielding interesting results as always, albeit nothing really useful) I'm still unable to find any PHP library that would help me handle clustering.
Is there such a PHP library out there that I might have missed?
If not, is there any FOSS that handles clustering and has a decent API?
Like this:
Use a list of stopwords, get all words or phrases not in the stopwords, count occurances of each, sort in descending order.
The stopwords needs to be a list of all common English terms. It should also include punctuation, and you will need to preg_replace all the punctuation to be a separate word first, e.g. "Something, like this." -> "Something , like this ." OR, you can just remove all punctuation.
$content=preg_replace('/[^a-z\s]/', '', $content); // remove punctuation
$stopwords='the|and|is|your|me|for|where|etc...';
$stopwords=explode('|',$stopwords);
$stopwords=array_flip($stopwords);
$result=array(); $temp=array();
foreach ($content as $s)
if (isset($stopwords[$s]) OR strlen($s)<3)
{
if (sizeof($temp)>0)
{
$result[]=implode(' ',$temp);
$temp=array();
}
} else $temp[]=$s;
if (sizeof($temp)>0) $result[]=implode(' ',$temp);
$phrases=array_count_values($result);
arsort($phrases);
Now you have an associative array in order of the frequency of terms that occur in your input data.
How you want to do the matches depends upon you, and it depends largely on the length of the strings in the input data.
I would see if any of the top 3 array keys match any of the top 3 from any other in the data. These are then your groups.
Let me know if you have any trouble with this.
"... cluster them into meaningful groups" is a bit to vague, you'll need to be more specific.
For starters you could look into K-Means clustering.
Have a look at this page and website:
PHP/irInformation Retrieval and other interesting topics
EDIT: You could try some data mining yourself by cross referencing search results with something like the open directory dmoz RDF data dump and then enumerate the matching categories.
EDIT2: And here is a dmoz/category question that also mentions "Faceted Search"!
Dmoz/Monster algorithme to calculate count of each category and sub category?
If you're doing this for English only, you could use WordNet: http://wordnet.princeton.edu/. It's a lexicon widely used in research which provides, among other things, sets of synonyms for English words. The shortest distance between two words could then serve as a similarity metric to do clustering yourself as zaf proposed.
Apparently there is a PHP interface to WordNet here: http://www.foxsurfer.com/wordnet/. It came up in this question: How to use word Net with php, but I have not tried it. However, interfacing with a command line tool from PHP yourself is feasible as well.
You could also have a look at Programming Collective Intelligence (Chapter 3 : Discovering Groups) by Toby Segaran which goes through just this use case using Python. However, you should be able to implement things in PHP once you understand how it works.
Even though it is not PHP, the Carrot2 project offers several clustering engines and can be integrated with Solr.
This may be way off but check out OpenCalais. They have a web service which allows you to pass a block of text in and it will pass you back a parseable response of things that it found in the text, such as places, people, facts etc. You could use these categories to build your "clouds" and too choose which results to display.
I've used this library a few times in php and it's always been quite easy to work with.
Again, might not be relevant to what your trying to do. Maybe you could post an example of what your trying to accomplish?
If you can pre-define the filters for your faceted search (the named groups) then it will be much easier.
Rather than relying on an algorithm that uses the current searcher's input and their particular results to generate the filter list, you would use an aggregate of the most commonly performed searches by all users and then tag results with them if they match.
You would end up with a table (or something) of URLs in a many-to-many join to a table of tags, so each result url could have several appropriate tags.
When the user searches, you simply match their search against the full index. But for the filters, you take the top results from among the current resultset.
I'll work on query examples if you want.
I want to search for threads in my mysql database with Solr.
But i want it to not just search the thread words, but for similar words.
Eg. if a thread title is "dog for sale" and if the user searches for dogs the title will be in the result.
and also if a user searches for "mac os x" the word "snow leopard" will appear.
and the ability to link words the application thinks is related eg. house and apartment.
how is this kind of logic done?
i know that you can with solr look up words in a dictionary file you create/add, so solr will look for dogs and see what related words there are (eg. dog).
but where do you find such a dictionary?
i have no idea about this kind of implementation.
please point me into right direction.
thanks
I think you'll have to build such a dictionary yourself, since it's very application-specific. "House" and "Apartment" might be similar terms for your application but very distant in another application.
Once you have this dictionary you can use it through the SynonymFilterFactory.
Matching "dog" when the user searches for "dogs" is managed by the stemmer and doesn't require any dictionary.
You could use the synonym.txt file and create your own dictionary.
Another option for you could be fuzzy search.
As the title says, I need a search engine... for mysql searching.
My website is PHP based.
I was going with sphinx but my hosting company doesn't support full-text indexes!
So a search engine to be used without full-text!
It should be pretty powerful, and must include atleast these functions below:
When searching for 'bmw 520' only matches where these two words come in exactly this order is returned. not matches for only 'bmw' or only '520'.
When searching for 'bmw 330ci' results as the above will be returned, but, WITH AND WITHOUT the ci extension. There are a nr of extensions in cars as you all know (i, ci, si, fi etc).
I want the 'minus sign' to 'exclude' all returns containing the word after the sign, ex: 'bmw -330' will return all 'bmw' results without the '330' ones. (a NOT instead of minus sign is also ok)
all special character accents like 'é' are converted to their simple values, in this case 'e'.
list of words to ignore completely in the search
Thanks guys!
The Zend_Lucene search competent works fairly well. I am not sure how it would cope with your second requirement, however if you customized the tokenized you should be able to do it by treating a change from letters to numbers as a new word.
The one I am really not sure about is the top requirement. Given how it is indexed, order becomes irreverent in the search, so you may not be able to do it without heavy editing of Lucene, writing a filter (using lucene to pull the matches, then checking the order), or writing your own solution. All of these will slow the search down, and add load to your server.
There is also solr, but I have never used it and don't know anything about it. Sphinx was another one, but I see you have already ruled that out.
Xapian is very good (very comprehensive) if you have the time for the initial setup.
It functions as you would expect a search engine to work, tell the indexer what bits of information to index under what namespace/table/object (Page, Profile, Products etc), then issue a query for your users based on keywords, it also supports google style tags e.g. "profile:Mark icecream" would search my profile for the word icecream, i seem to remember it supporting ranges too for data you specify as numeric.
Can be used in local mode which can offer spelling modifications (Did you mean?), or remote mode that many sites can index to and query from.
What really saved me one time was the ability to attach transient non searchable data to an indexed item, e.g. attaching the DB id to all data indexed for that record, very good for then going and getting the whole record from the DB when your matches come back from xapian.
I have used a couple of Search Engines on my site during it's time, but in the next rebuild I'm planning to move to Google Site Search.
There are several reasons for this:
Users are very familiar with the Google style of search result listings which improves usability and hence click-through rates
The Google engine is very good at guessing when to use the page description and when to use a fragment of the page (it also very good at getting relevant fragments compared to some other engines)
It's used by thousands of very popular websites
Google is the most popular search engine around so you know their technology is both reliable and accurate
Google Site Search begins at $100 per annum for 1000 pages or less (and a limit on queries)
or you can use the free Google Custom Search Engine (but this has much less customizability)