What i am doing and what i did so far:
i'm developing an question and answering system using Solr,i took product reviews as my data-set(contains product id and its reviews from different users) which is in json format.i have performed indexing on my data-set and successfully got the response of indexed data.
Requirements:
In my Q/A system i will provide query in Natural language for example, "why should i buy X(product name)" and my Q/A should be capable of recognizing the words in reviews like "its ease to use, flexible product" and it should frame its answer depending on those words.
I would like to know the following
How can i process natural language query into solr executable query,
How can i prepare my answer to the query,
What kind of nlp models should i use,
How should i train my Q/A system
and any other information which can help me to achieve the requirements.
You are nowhere near Solr yet. You have to go back and look for the actual NLP (Natural Language Processing) system. If it uses Solr (or OpenNLP that integrates with Solr) - great. If not, you have to invent this bridge, it does not just come with Solr, as this is still a cutting edge of research.
Related
Imagine that you feed a system a bunch of pdfs that you, and only you, know "how" these are related (e.g. they are all dissertations, or news, or invoices). The system know that the batch is connected, but does not know how they relate.
The system then scan these pdf's, and suggest indexes and their respective value for each document.
Here's an example: You feed a the system all the invoices your company gets. The system process these docs and suggests for indexes "Supplier", "Invoice Cost" and "Due Date". Foreach pdf the system also extracts the value of the entry.
So my question is: what kind of artificial intelligence system is most adequate for this scenario? A Neural Network? A combination?
You are looking for unsupervised learning algorithms. More specifically, yours is a clustering problem, since your system does not know anything about the data it is going to analyze and it has to come up with a correct classification of the documents (or their properties).
In your example, by using clustering algorithms, your system can learn to distinguish the documents you provide and to extract the field "Invoice", "Supplier" ...
The wiki page I linked should be enough to have a general idea of the class of algorithms you need. On Google you will find a plethora of lecture slides on the topic.
You could do this by just a keyword search, if you know what keyword the machine should be looking for, and the documents all follow the same format.
If the formats are non-uniform within each category, however, then you would need to consider some kind of language processing in order for the machine to be able to understand what's going on.
Try do some research into natural language processing, this is probably along the lines of what you're looking for:
NLP Wiki
I am looking into Neo4j as a stripped-down document store. A key aspect of document storage is search, and I know Neo4j includes full text search via legacy indices provided by Lucene.
I would be very interested in hearing the limitations of Neo4j search capabilities in a distributed environment. Does it provide a distributed index? In what ways is it inferior to Solr or ElasticSearch? How far can I take it before I must install Solr?
-- EDIT --
We are trying to integrate two distinct search efforts. The first is standard text content search. For instance, using the Enron emails, we want to search for every email that matches "bananas" or "going to the store" and get those document bodies in response. This is where people often turn to Solr.
The second case is more complicated, we have attached a great deal of meta-data to each document. We may have decided that "these" emails were the result of late-night drunk-dialing. Now I want to search for all emails that may have been the result of late-night drunk-dialing. For this kind of meta-data, we believe a graph database is in order.
In a perfect world, I can use one platform to perform both queries. I appreciate that Neo4j (nor OrientDB, Arango, etc) are designed as full text search databases, but I'm trying to understand the limitations thereof.
In terms of volume, we are dealing at a very large scale with batch-style nightly updates. The data is content heavy, with some documents running into hundreds of pages of text, but mostly on the order of a page or two.
I once worked on a health social network where we needed some sort of search and connection search functionalities we first went on neo4j we were very impressed by the cypher query language we could get and express any request however when you throw there billion of nodes you start to pay the price and we started considering another graph db, this time we've made a lot of research, tests and OrientDB was clearly the winner, OrientDB is highly scalable but the thing is that you have to code by yourself, your "search algorithm" if you want to do some advanced things (what is the common point between this two nodes) otherwise you have the SQL like query language (i don't know/remember if he has a name) but you can do some interesting stuff with it
So in conclusion i would definitely go on OrientDB
Neo4j can provide a "distributed index" in the sense that the high availability cluster can make your index available on more than one machine, but I'm pretty sure that's not what you're after. Related to this issue is a different answer I wrote about graph partitioning, and what it takes to distribute a really large number of nodes/relationships across multiple machines. (It's not terribly simple)
Solr and Lucene do two different things (although Solr is built on top of Lucene). I think solr and neo4j are not comparable because they're trying to do completely different things. This site isn't about software recommendations so I can't tell you what you should use other than to say you should read up on solr and neo4j, and figure out which set of functionality you want. As far as I know, this is an exclusive decision as I'm not aware of people integrating solr with neo4j.
Your question is very difficult to answer, I'd recommend expanding on what you are trying to do and what you have tried, you'll probably get better responses.
I have very huge solr index. I want to tag all documents with terms which better represent that document like this. Does this type of clustering results is also come under document tagging?
Which approach is better, Index time Document tagging or Query time document tagging like carrot2 ?
Query time has the obvious drawback that this makes the query more expensive.
However, the clustering results at query time are supposedly better, because at that time, more information has been seen and user feedback can be incorporated.
Note that technically, this is probably more frequent pattern mining than cluster analysis.
Maybe you should just try this variant of frequent pattern mining on your whole data set. You might not even need to store which documents were tagged which way - the solr engine should already be optimized to retrieve them again when needed.
I understood from your question that you want to know how to implement something similar to carrot2 faceting using solr.
IMO you can add a multivalued field tag to your documents (see this Stack Overflow Question for an example) with the cluster names for that doc, and then build facets using that field as explained in Solr wiki here and here.
I'm lost in: Hadoop, Hbase, Lucene, Carrot2, Cloudera, Tika, ZooKeeper, Solr, Katta, Cascading, POI...
When you read about the one you can be often sure that each of the others tools is going to be mentioned.
I don't expect you to explain every tool to me - sure not. If you could help me to narrow this set for my particular scenario it would be great. So far I'm not sure which of the above will fit and it looks like (as always) there are more then one way of doing what's to be done.
The scenario is: 500GB - ~20 TB of documents stored in Hadoop. Text documents in multiple formats: email, doc, pdf, odt. Metadata about those documents stored in SQL db (sender, recipients, date, department etc.) Main source of documents will be ExchangeServer (emails and attachments), but not only. Now to the search: User needs to be able to do complex full-text searches over those documents. Basicaly he'll be presented with some search-config panel (java desktop application, not webapp) - he'll set date range, document types, senders/recipients, keywords etc. - fire the search and get the resulting list of the documents (and for each document info why its included in search results i.e. which keywords are found in document).
Which tools I should take into consideration and which not? The point is to develop such solution with only minimal required "glue"-code. I'm proficient in SQLdbs but quite uncomfortable with Apache-and-related technologies.
Basic workflow looks like this: ExchangeServer/other source -> conversion from doc/pdf/... -> deduplication -> Hadopp + SQL (metadata) -> build/update an index <- search through the docs (and do it fast) -> present search results
Thank you!
Going with solr is a good option. I have used it for similar scenario you described above. You can use solr for real huge data as its a distributed index server.
But to get the meta data about all of these documents formats you should be using some other tool. Basically your workflow will be this.
1) Use hadoop cluster to store data.
2) Extract data in hadoop cluster using map/redcue
3) Do document identification( identify document type)
4) Extract meta data from these document.
5) Index metadata in solr server, store other ingestion information in database
6) Solr server is distributed index server, so for each ingestion you could create a new shard or index.
7) When search is required search on all the indexs.
8) Solr supports all the complex searches , so you don't have to make your own search engine.
9) It also does paging for you as well.
We've done exactly this for some of our clients by using Solr as a "secondary indexer" to HBase. Updates to HBase are sent to Solr, and you can query against it. Typically folks start with HBase, and then graft search on. Sounds like you know from the get go that search is what you want, so you can probably embed the secondary indexing in from your pipeline that feeds HBase.
You may find though that just using Solr does everything you need.
Another project to look at is Lily, http://www.lilyproject.org/lily/index.html, which has already done the work of integrating Solr with a distributed database.
Also, I do not see why you would not want to use a browser for this application. You are describing exactly what faceted search is. While you certainly could set up a desktop app that communicates with the server (parses JSON) and displays the results in a thick client GUI, all of this work is already done for you in the browser. And, Solr comes with a free faceted search system out of the box: just follow along the tutorial.
Going with Solr (http://lucene.apache.org/solr) is a good solution, but be ready to have to deal with some non-obvious things. First is planning your indexes properly. Multiple terabytes of data will almost definitely need multiple shards on Solr for any level of reasonable performance and you'll be in charge of managing those yourself. It does provide distributed search (doing the queries off multiple shards), but that is only half the battle.
ElasticSearch (http://www.elasticsearch.org/) is another popular alternative, but i don't have much experience with it regarding scale. It uses the same Lucene engine so i'd expect the search feature-set to be similar.
Another type of solution is something like SenseiDB - open sourced from LinkedIn - which gives the full-text search functionality (also Lucene-based) as well as proven scale for large amounts of data:
http://senseidb.com
They've definitely done a lot of work on search over there and my casual use of it is pretty promising.
Assuming all your data is already in Hadoop, you could write some custom MR jobs that pull the data in a consistent schema-friendly format into SenseiDB. SenseiDB already provides a Hadoop MR indexer which you can look at.
The only caveat is it is a little more complex to setup, but will save you with the scaling issues many times over - especially around indexing performance and faceting functionality. It also provides clustering support if HA is important to you - which is still in Alpha for Solr (Solr 4.x is alpha atm).
Hope that helps and good luck!
Update:
I asked a friend who is more versed in ElasticSearch than me and it does have the advantage of clustering and rebalancing based on the # of machines and shards you have. This is a definite win over Solr - especially if you're dealing with TBs of data. The only downside is the current state of documentation on ElasticSearch leaves a lot to be desired.
As a side note, you can't say the documents are stored in Hadoop, they are stored in a distributed file system (most probably HDFS since you mentioned Hadoop).
Regarding searching/indexing: Lucene is the tool to use for your scenario. You can use it for both indexing and searching. It's a java library. There is also an associated project (called Solr) which allows you to access the indexing/searching system through WebServices. So you should also take a look at Solr as it allows the handling of different types of documents (Lucene puts the responsability of interpreting the document (PDF, Word, etc) on your shoulders but you, probably, can already do that)
I just wanted to know know what all databases do Google, Yahoo or Bing use for natural language query processing. I mean they can take in so complex queries in natural language. Do they programmatically do the processing of our query and break down in some kind of hash and then map to result.
Please don't mind if the question is silly. I am just a newbie. I just wanted to know what all kind of databases are used for such purposes.
Type Lucene OR
Type Solr OR
Type Sphinx
on Google or Bing.
You would then also come to know apart from indexing data structures something about stemming, thesaurus, synonyms, query expansion in search engines, metaphone etc. This all stuff would help you in getting answer to your question.
After you are through with above then later you can read more about establishing semantic relationship between keywords, collective intelligence, wisdom of crowd stuff that would help you in establishing similarity between say for example keywords like java and jee and jsp and servlets.