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'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.
If you've worked with iKnow domain definitions, you know they allow you to easily define multiple data locations iKnow needs to fetch its data from when building a domain. If you've worked with DeepSee cube definitions, you'll know how they tie your cube to a source table and allow you to not just build your cube, but also synchronize it, only updating the facts that actually changed since the last time you built or synced the cube. As iKnow also supports loading from non-table data sources like files, globals and RSS feeds, the same tight synchronization link doesn't come out of the box. In this article, we'll explore two approaches for modelling DeepSee-like synchronization from table data locations using callbacks and other features of the iKnow domain definition infrastructure.
Can we use iknow in the Cache tool means if i don't have deepsee tool to work on like for doing some sample programs. Because i don't have deepsee tool with me?
And is there any open source for downloading deepsee?
I created an iKnow domain, where I supplied dictionaries, blacklist, metadata and stemming. The datasource is a table.
I would like to use iFind semantic search feature. It is said in the documentation that iFind use iKnow semantic analysis. But I want iFind to use the iKnow domain configuration I created earlier earlier. How can I do that ?
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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I have a class which, in the previous instance, was able to extract metadata field names and data from a text file, and load this information into a domain. I am trying to run this in the field test instance, but it is not loading the metadata - only the field names. I am not getting an error, but the data is not loaded.
The few changes I made to the original class:
Previously, this class also ran iTables. I commented all that code out.
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.
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:
Presenter: Danny Wijnschenk Task: Help people make better decisions by letting application deal with all the data. Approach: As an example, we’ll extend a demo asset management application for portfolio and trade compliance, using iKnow technology to translate agreements into rules that ensure portfolio compliance prior to trade execution.
In this session, we’ll discuss how easy it is to extend a classic application that deals with straightforward transactions, to also offer insights and actions based on more complex, unstructured data. We’ll present a use case on portfolio compliance from the financial services industry.
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Benjamin De Boe Task: Extract specialized information from your unstructured data Approach: Combine InterSystems iKnow technology with third-party and custom text-processing tools
This session explains how you can easily combine ISC, third-party and custom text processing tools to get the broadest insights in your unstructured data.
Content related to this session, including slides, video and additional learning content can be found here.
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In iknow they are mentioning that Japanese words cannot be used for iknow in CRC and in CC, If one needs to use iknow for Japanese character how does concepts and relation works for them?
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.
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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 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 }
In Cache'/Ensemble, by specifying the objectsPackage parameter, dictionaries (and other objects) get projected to tables that can be accessed by SQL queries.
But in IRIS (IRIS for UNIX (Ubuntu Server LTS for x86-64 Containers) 2019.1 (Build 507U) Mon Feb 25 2019 13:47:16 EST), when I created a dictionary with ##class(%iKnow.Matching.DictionaryAPI).CreateDictionary(), it does not get projected to a table.
The class APIs correctly retrieve information about this dictionary.
Am I missing something with IRIS, or is there any issues about this?
Presenter: Misha Bouzinier Task: Gain an understanding of natural language processing and the current state of the art Approach: Discuss how InterSystems iKnow technology fits into the NLP ecosystem and complements the output of other components such as Lucene and Stanford NLP tools
A 101 session on Natural Language Processing that positions Intersystems tools in the broader ecosystem Problem: we’ve been touting “unstructured data” for five years, but many people both internally and externally still don’t know what it means to “process natural language” in general and how iKnow and our upcoming UIMA capabilities fit in this NLP ecosystem. This session will describe what a number of common technologies offer and how bare-bone NLP output typically needs to be complemented with more classic analytics or inference tooling to get the value out.
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Dirk Van Hyfte Task: Leverage unstructured data to improve how clinicians deliver care Approach: Give real-world examples of organizations that are benefiting from using their unstructured data
This session will feature real-world examples of how healthcare organizations can benefit from exposing unstructured data to clinicians at point-of-care as well as to clinical informatics building predictive models. Presenters are Wesley Williams, PhD, Vice President and Chief Information Officer, Mental Health Center of Denver; Augie Turano PhD. IT Director Veterans Informatics and Computer Infrastructure (VINCI); and Dirk Van Hyfte, MD, PhD, Senior Research Consultant.
Content related to this session, including slides, video and additional learning content can be found here.
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