InterSystems iKnow is unique technology for text exploration that enables you to gain insight from unstructured data and use it to enrich your solutions. iKnow is different because it uses a “bottom up” approach to text exploration, discovering concepts and relations within the text itself. With iKnow technology, there is no need for the tedious – and limiting – process of predefining dictionaries or ontologies.
I want to connect IRIS system as it has all the database tables. on top of that I am creating a REST API in python. How can I connect to IRIS DB. here is my example code for connection
after this connection is created but how can I get tables data. Please let me know more about how we can integrate IRIS database into a python REST API.
Hello there! I am hoping to get some help with "SQL Search index" and JSON objects. I am storing a JSON object in a column of type %Stream.GlobalCharacter Property JSON As %Stream.GlobalCharacter; I also have a index that looks like below. Index AnalyticIdx On (JSON) As %iFind.Index.Analytic(INDEXOPTION = 0, LANGUAGE = "en", LOWER = 1);
I'm working in an application that uses %SIMILARITY to find matches among a set of documents that vary greatly in length. It's generally good but I've noticed issues with ranking short partially-matching documents over longer documents that match the search string entirely.
I have a SQL Query using %iFind.Highlight which returns text highlighting certain words and phrases. %iFind.Highlight seems to remove cr/lf from the returned text.
Here's my query
ClassMethod Search(pSessionId As %String, pSearchString As %String) As %String { set tTags="<span style='background-color:yellow;'>" &sql( SELECT %iFind.Highlight(Text , :pSearchString , , :tTags) into :results FROM SSA_OCR.TempSearchable where sessionId = :pSessionId) quit results }
I've been working with SQL using an iFind index to search text. Using the %iFind.Highlight function in my SELECT statement I can get text back that highlights the found words using <b> and </b>
I am aware that using ##class(%iFind.Utils).Highlight, I can pass a parameter to override the <b> tag and use instead a <span> tag with style to change the background color of the found words.
Is there a way to override the <b> tag from a SQL statement?
I have iKnow domain of forum posts, their full text is an iKnow data, and each post also has a number of views as a metadata field.
I want to get a sum of views by concept. Let's say I have a concept called "TESTEST" and there are 10 sources that have this concept. Each source has some views. I want to get views total - impact of this concept so to say.
What's the best iKnow architecture for this use case?
I am trying to create an iKnow domain programmatically like:
Set dom = ##class(%iKnow.Domain).%New("TestDom")
Do dom.SetParameter("DefaultConfig", "MyConfiguration")
Set sc = dom.%Save()
...
Although "MyConfiguration" sets the language to "ja", i.e. Japanese, it doesn't seem to be respected, and what I see on the top right pane in Knowledge Portal is related concepts, instead of proximity profiles, which I expect to see in Japanese language mode.
Also resulting segmentation of sentences looks to be it is in English mode.
Has anyone ever estimated the amount of disk space consumed by the iKnow indexing process ? I know this will be a rough estimate, but, I imagine that for sizing purposes, that would be enough.
The language the unstructured text is in is English.
In my cache studio i couldn't find the a namespace of iknow so how can i check is my studio version is compatible to to the one i am using now. If i don't have one then can be able to create a new namespace in studio?
We use iKnow's GetSimilar for decision making. Right now we have a domain with both good and bad documents and using GetSimilar we want to see if a document is more similar to the good ones or the bad ones. To do this we simply compare the weighted average of the score from the good ones and the bad ones that GetSimilar returns.
The problem is that GetSimilar doesn't always return the score to all other documents. Assuming we have 50 documents I would expect the following result:
My group and I are currently doing a research project on natural language processing and iKnow plays a big role in this project. I am aware that the algorithms iKnow use aren't public, and I respect that.
My question is, are there any public documents/research that explains, at least part of, the algorthims iKnow uses and the motivations for using them?
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