This is a very important programming question (and a popular interview question), yet I wasn't able to find any direct answer on the internet.
All I know is that they are:
Based on real searches
Different for different languages and regions
Censored
Based on your search history
Duplicates removed
Sorted by most relevant and popular
So we're looking at a data structures that are:
Easily sortable
Elements are unique
The word you type in is somehow connected to the suggestions
My first guess would be: it's a graph.
Another option is an adjacency list. Each element contains a link list of suggestions etc.
Does anyone know it's really done?
Related
as the question says: "Is there a way to get all complete sentences that a search engine (e.g. Google) has indexed that contain two search terms?"
I would like to use the (e.g. Google) search syntax: BMW AND Toyota. (<-- this is just an example)
And I would then like to have returned all sentences that mention BMW and Toyota. They must be in a single (ideally: short) sentence though.
Is that possible?
Many thanks!
PS.: Sorry - I have difficulties finding the right tags for my question... Please feel free to suggest more appropriate ones and I will update the question.
PPS.: Let me rephrase my question: If it is not readily possible with an existing search engine, are there any programmatical ways to do that? Would one have to write a crawler for that purpose?
No this may not be possible, as google stores this info based on keywords and other algorithms.
For any given keyword or set of keywords, google must be maintaining a reference to one or many matching (some accurate, some not so accurate) titles.
I do not work for google, but that could one way they are maintaining their search results.
I'm trying to build a local version of the freebase search api using their quad dumps. I'm wondering what algorithm they use to match names? As an example, if you go to freebase.com and type in "Hiking" you get
"Apo Hiking Society"
"Hiking"
"Hiking Georgia"
"Hiking Virginia's national forests"
"Hiking trail"
Wow, a lot of guesses! I hope I don't muddy the waters too much by not guessing too.
The auto-complete box is basically powered by Freebase Suggest which is powered, in turn, by the Freebase Search service. Strings which are indexed by the search service for matching include: 1) the name, 2) all aliases in the given language, 3) link anchor text from the associated Wikipedia articles and 4) identifiers (called keys by Freebase), which includes things like Wikipedia article titles (and redirects).
How the various things are weighted/boosted hasn't been disclosed, but you can get a feel for things by playing with it for while. As you can see from the API, there's also the ability to do filtering/weighting by types and other criteria and this can come into play depending on the context. For example, if you're adding a record label to an album, topics which are typed as record labels will get a boost relative to things which aren't (but you can still get to things of other types to allow for the use case where your target topic doesn't hasn't had the appropriate type applied yet).
So that gives you a little insight into how their service works, but why not build a search service that does what you need since you're starting from scratch anyway?
BTW, pre-Google the Metaweb search implementation was based on top of Lucene, so you could definitely do worse than using that as your starting point. You can read some of the details in the mailing list archive
Probably they use an inverted Index over selected fields, such as the English name, aliases and the Wikipedia snippet displayed. In your application you can achieve that using something like Lucene.
For the algorithm side, I find the following paper a good overview
Zobel and Moffat (2006): "Inverted Files for Text Search Engines".
Most likely it's a trie with lexicographical order.
There are a number of algorithms available: Boyer-Moore, Smith-Waterman-Gotoh, Knuth Morriss-Pratt etc. You might also want to check up on Edit distance algorithms such as Levenshtein. You will need to play around to see which best suits your purpose.
An implementation of such algorithms is the Simmetrics library by the University of Sheffield.
We have a client who is looking for a means to import and categorize a large amount of textual data. This data has to be categorized and it's been suggested that the easiest way to to do this would be to look at the description field and try to match the words held there to see if a category can be derived for that particular record.
It was thought the best way to do this would be matching the words to key words held against each category and if that was unsuccessful then to use some kind of synonym look up to see if this could be used instead. So for example, if a particular record had the word "automobile" in it then a synonym look up could match that word to the word "car" which would be held against the category "vehicle".
Does anyone know of a web service or other means of looking up a dictionary to find synonyms for a particular word? The project manager has suggested buying a Google Enterprise Search license for this but from what I can make out that doesn't offer what these guys are looking for.
Any suggestions of other getting the client what they are looking for would be gratefully accepted.
Thanks! I'll look into Wordnet.
Do you know of any other types of textual classification software products out there. I see there's some discussion of using Bayasian algorithms for this but I can't see any real world examples of it.
The first thing that comes to mind is Wordnet. Wordnet is a human-generated database of words and related words, including synonyms. The Wikipedia Wordnet entry lists several interfaces to Wordnet. I believe some of them are web services.
You can also roll your own. Manning and Schutze's chapter 5 (free PDF) shows ways to do this.
Having said that, are you solving the right problem? How do you build the category list?
Is it a hierarchy? a tag cloud? See Clay Shirky's Ontology is Overrated for a critique of hierarchical categories. I believe that synonyms are less important if you base your classification on sets of words (Naive Bayes, for example) rather than on single words.
You should look at using WordNet. You can visit their website http://wordnet.princeton.edu/ to get more information, but there are libraries available for integrating against them in lots of languages.
Go to their online tool to see the use of it in action here: http://wordnetweb.princeton.edu/perl/webwn. If you look up a word, then click on "S" next to each definition, you'll get a list of semantically related words to that definition.
I also think you should check out software that will allow you to perform "document clustering." Here is an example: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview. That should help you bootstrap the category creation process.
I think this will help get you a long way toward what you want!
For text classification you can take a look at Apache Mahout.
I am currently working on project where I have to match up a large quantity of user-generated names with a separate list of the same names in a canonical format. The problem is that the user-generated names contains numerous misspellings, abbreviations, as well as simply invalid data, making it hard to do a cross-reference with the canonical data. Any suggestions on methods to do this?
This does not have to be done in real-time and in this case accuracy is more important than speed.
Current ideas for this are:
Do a fuzzy search for the user entered name in the canonical database using an existing search implementation like Lucene or Sphinx, which I presume use something like the Levenshtein distance for this.
Cross-reference on the SOUNDEX hash (which is supposedly computed on the sound of the name rather than spelling) instead of using the actual name.
Some combination of the above
Anyone have any feedback on any of these or ideas of their own?
One of my concerns is that none of the above methods will handle abbreviations very well. Can anyone point me in a direction for some machine learning methods to actually search on expanded abbreviations (or tell me I'm crazy)? Thanks in advance.
First, I'd add to your list the techniques discussed at Peter Norvig's post on spelling correction.
Second, I'd ask what kind of "user-generated names" you're talking about. Having dealt with both, I believe that the heuristics you'd use for street names are somewhat different from the heuristics for person names. (As a simple example, does "Dr" expand to "Drive" or "Doctor"?)
Third, I'd look at a combination using testing to establish the set of coefficients for combining the results of the various techniques.
I've seen a few sites that list related searches when you perform a search, namely they suggest other search queries you may be interested in.
I'm wondering the best way to model this in a medium-sized site (not enough traffic to rely on visitor stats to infer relationships). My initial thought is to store the top 10 results for each unique query, then when a new search is performed to find all the historical searches that match some amount of the top 10 results but ideally not matching all of them (matching all of them might suggest an equivalent search and hence not that useful as a suggestion).
I imagine that some people have done this functionality before and may be able to provide some ideas of different ways to do this. I'm not necessarily looking for one winning idea since the solution will no doubt vary substantially depending on the size and nature of the site.
have you considered a matrix of with keywords on 1 axis vs. documents on another axis. once you find the set of vetors representing the keywords, find sets of keyword(s) found in your initial result set and then find a way to rank the other keywords by how many documents they reference or how many times they interset the intial result set.
I've tried a number of different approaches to this, with various degrees of success. In the end, I think the best approach is highly dependent on the domain/topics being searched, and how the users form queries.
Your thought about storing previous searches seems reasonable to me. I'd be curious to see how it works in practice (I mean that in the most sincere way -- there are many nuances that can cause these techniques to fail in the "real world", particularly when data is sparse).
Here are some techniques I've used in the past, and seen in the literature:
Thesaurus based approaches: Index into a thesaurus for each term that the user has used, and then use some heuristic to filter the synonyms to show the user as possible search terms.
Stem and search on that: Stem the search terms (eg: with the Porter Stemming Algorithm and then use the stemmed terms instead of the initially provided queries, and given the user the option of searching for exactly the terms they specified (or do the opposite, search the exact terms first, and use stemming to find the terms that stem to the same root. This second approach obviously takes some pre-processing of a known dictionary, or you can collect terms as your indexing term finds them.)
Chaining: Parse the results found by the user's query and extract key terms from the top N results (KEA is one library/algorithm that you can look at for keyword extraction techniques.)