That functionality is not supported through the iKnow Architect. It is our intent to focus on the table and query data location options, as those are just a bit of COS development away from other sources of (meta)data.

Hi Benjamin,

the default algorithm indeed won't return scores for each record, but will only make the calculation for all records that contain at least a decent number of entities that are relevant in the source document. You can indeed simply approximate the other documents' score by taking 0.

For your specific use case, you may want to take a look at the text categorization infrastructure. I've posted a tutorial on the topic here.

regards,
benjamin

Hi Jack,

there's no need to normalize your search strings, as it's take care of automatically as part of executing your search when appropriate.

When you use DELETE FROM in SQL, or ##class(Your.Table).%DeleteExtent() in COS, the associated iFind indices' data will be erased as well. To drop just the indices data, use ##class(Your.Table).%PurgeIndices() (cf class ref for refinements). Note that, unless you are using index-local storage (new feature in 2016.1), the words and entities tables will not be wiped as they are shared between all iFind indices in your namespace (somewhat conserving space and indexing efficiency).

iFind can calculate a score representing how well a record satisfies a search string, largely based on TFIDF (although it'll leverage the more refined dominance scores for entities when it can). This is also new in 2016.1. See https://community.intersystems.com/code/ifind-search-portal for an example.

 

regards,
benjamin

Hi Benjamin (sounds like a conversation amongst just Benjamins now!),

 

The knowledge portal demonstration interface you find in the %iKnow.UI package (which gets a significant visual overhaul in 2016.3) is written using InterSystems' Zen technology, a web development framework that helps you combine client-side JavaScript and server-side Caché ObjectScript to build web applications. If you're good with PHP and/or JavaScript, there's no strict need to dig into Zen to build an iKnow-powered application. You can either use ODBC to connect to Caché and use SQL as in the above examples to query an iKnow domain, or you can build a simple REST service on top of iKnow (in Caché ObjectScript) and query that from your PHP/JavaScript code. We'll be releasing an out-of-the-box REST interface with 2016.3, but it's no rocket science to build one that fits your needs on earlier versions. If you already have an ISC sales engineering contact (none of them called Benjamin, unfortunately ;o) ), we can work together to get you up and running.

 

FYI, this github repo contains a simple iKnow demo application written with AngularJS and a REST interface. It's technically speaking a CSP page (yet another ISC web technology at a lower level than Zen), but could have been a straight HTML page.

 

 

Regards,

Benjamin

Hi Jack,

you can enable stemming by setting the INDEXOPTION index parameter to 1 (or by leveraging the more flexible TRANSFORMATIONSPEC index parameter if you are on 2016.1).

Class ThePackage.MyClass Extends %Persistent
{
	Property MyStringProperty As %String;
	
	Index MyBasicIndex On (MyStringProperty) As %iFind.Index.Basic(INDEXOPTION=1);
}

The class reference for %iFind.Index.Basic also explains how you can toggle between stemmed and normal search by using the search mode argument:

SELECT * FROM ThePackage.MyClass WHERE %ID %FIND search_index(MyBasicIndex, 'interesting')

for normal search vs using search option 1 for stemmed search:

SELECT * FROM ThePackage.MyClass WHERE %ID %FIND search_index(MyBasicIndex, 'interesting', 1)

 

We do not discard stop words in iFind, in order to ensure you can query for any literal word sequence afterwards. If you start looking at the projections for entities (cf %iKnow.Index.Analytic class ref), you'll see how iKnow offers you a more insightful view of what a sentence is about through the "entity" level, where classic search tech may only offer you the words of a sentence minus the stop words.

 

regards,

benjamin