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.
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In a conference call earlier this week, a customer described how they built an iKnow domain with clinical notes and now wanted to filter the contents of that domain based on the patient's diagnosis codes.
Experts estimate that 85% of all data exists in unstructured formats – held in e-mails, documents (contracts, memos, clinical notes, legal briefs), social media feeds, etc. Where structured data typically accounts for quantitative facts, the more interesting and potentially more valuable expert opinions and conclusions are often hidden in these unstructured formats. And with massive volumes of text being generated at unprecedented speed, there’s very little chance this information can be made useful without some process of synthesis or automation.
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This is the fourth article in a series on iKnow demo applications, showcasing how the concepts and context provided through iKnow's unique bottom-up approach can be used to implement relevant use cases and help users be more productive in their daily tasks. Previous articles discussed the Knowledge Portal, the Set Analysis Demo and the Dictionary Builder Demo, each of which gradually implemented slightly more advanced interactions with what iKnow gleans from unstructured data.
This week, we'll look into one more demo application, the Rules Builder Demo, in which we'll build on previous work but again climb a step on the level ladder, implementing a more high-level use case than in the previous ones. The idea came from an opportunity where we were asked to help the customer in the finance sector make sense of vast volumes of contract data. They wanted to semi-automate the extraction of logical rules from that text (in fluent legalese!), so they could be fed into other systems. While this was an exciting use case to work on (and more on it in this GS2016 presentation), we've also used it in other cases, for example to extract mentions of ejection fraction from Electronic Health Records.
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 ?
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 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?
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:
I'm in a process of acquiring a corpus of documents on educational courses.
For example there is an educational course called "OOP" and it can have documents from 2008, 2009, ... 2016 etc. And there are a lot of these courses, each one with programs from different years (hopefully)
So 1 document is 1 programm of one course for one year.
I want to calculate how much does a course changes per year.
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.
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?
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.
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?
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.
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: 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.
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?
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