implementing knowledge-as-a-service

2019 DoD and Federal Knowledge Management Symposium

Implementing Knowledge-as-a-Service

Copyright © 2019 A.J. Rhem & Associates, Inc.

About Your Presenter

Dr. Anthony J. Rhem, PhD.: serves as the President and Principle Consultant of A.J. Rhem & Associates, Inc., a privately held Knowledge Management & System Integration Consulting, Training and Research firm located in Chicago, Illinois.

Dr. Rhem has over thirty (30) years of experience in information technology and twenty years (20) in Knowledge Management. A published author, educator, and researcher; Dr. Rhem has presented the application and theory of Software Engineering Methodologies, Knowledge Management, Artificial Intelligence, Information Architecture, Big Data and IoT at universities and conferences in the US, Europe and Australia.

Copyright © 2019 A.J. Rhem & Associates, Inc. 2

Knowledge-as-a-Service (KaaS)

• Knowledge as a Service (KaaS) blends Knowledge Management (KM) and Artificial Intelligence (AI) to deliver the right knowledge to the right person in the right context at the right time across various platforms (desktop, laptop or any mobile device) in order to facilitate fast, efficient and accurate decision making.


Why Knowledge-as-a-Service (KaaS)?

• There is too much information and knowledge within an organization and its imperative to make sense of it and focus on what you need in a timely manner.

• KaaS is needed to:• Improve access to the collective knowledge of an organization; • Provide personalized knowledge that responds to the needs of the worker; • Enable workers to be productive by executing tasks and learning more

efficiently and effectively

• KaaS provides the mechanisms to support and encourage a knowledge sharing culture and a Knowledge-Driven Organization


Tenets of Knowledge-as-a-Service


Tenets of Knowledge-as-a-Service

• Tenets of KaaS• Uses a distributed computing model.

• Connects Tacit and Explicit Knowledge through ontology management and knowledge mapping

• Tacit and Explicit Knowledge is constantly updated adhering to your organization’s content (information and knowledge) lifecycle management processes.

• Provides a Dynamic, Accurate and Personal delivery of Knowledge

• Makes use of AI through predictive analytics and Knowledge Flow Optimization


KaaS Distributed Computing Model

• Allows resource sharing, including software by systems connected to the network.

• Incorporates the use of Web services. The next generation application interaction allowing applications to communicate based on standard methodologies for information exchange.

• The main advantages of distributed data computing include the lower cost of processing data, having multiple control centers that reduce the risk of a system breakdown, and improved efficiency.


KaaS Distributed Computing Model

Copyright © 2019 A.J. Rhem & Associates, Inc.8

Knowledgebase Front End – User Interface

Web Services

Search & Metadata ServicesAPI’s

…

KnowledgebaseRepository

Machine Learning (Data Analytics)

Expertise Knowledgebase

Metadata and Ontology Management

Microservices API’s

User Access Layer

Infrastructure Layer

Distributed Knowledge Services

Layer

AI/ML Layer





ML Analytics ML Analytics ML Analytics …

KaaS Ontology Management & Knowledge Mapping• Providing a common ontology provides consistent classification and

establishes relationships between the tacit and explicit knowledge within the repository.

• The ontology is the backbone of the interactive knowledge map depicting a visual relationship between the tacit and explicit knowledge within the repository.

• The ontology also provides the machine learning algorithm with the necessary labels and relationships between data to perform accurate predictive analytics.


KaaS Ontology Management & Knowledge Mapping


Ontology Management Knowledge Mapping

KaaS Knowledge Flow Optimization

• An understanding of how knowledge flows throughout your organization is essential for delivering KaaS

• Connecting explicit knowledge to the tacit knowledge holders creates a holistic view of knowledge.

• Tacit knowledge holders • workers who are experienced with

executing certain tasks, developing a solution, working in a specific industry, practice area or company while leveraging the stored knowledge.


Connect

Collect

Store

Use/ReuseLearn

Create New

Store New

KaaS Delivery of Knowledge


Dynamic

Accurate

Personal

Constantly updated adhering to your organization’s content (information and knowledge) lifecycle management processes; including the experts who can provide insights about the knowledge. The Dynamic component of knowledge reflects your organization’s brand, tone and evolves over time.

Identified as the authoritative source and authoritative voice for that subject matter. This knowledge is accepted by your organization as the “source of truth”.

Answers the questions that the users of your knowledge are seeking; tailored to what the individual need to make a decision; facilitated by how knowledge flows throughout your organization.

Incorporating Artificial Intelligence (AI)Using AI to Deliver Knowledge


AI in KaaS Knowledge Delivery

• AI plays an important part to KaaS by elevating how the delivery of knowledge occurs to the people who need it. AI is used to scale the volume and effectiveness of knowledge distribution.


AI KM

AI will enable KaaS in your organization by providing the ability to :

• Predict trending knowledge areas/topics that your knowledge workers needs

• Identify which targeted knowledge will resonate with your knowledge workers based on real-time engagement and content consumption

• Personalize knowledge based on individual preferences

• Improve content decisions by leveraging machine learning around what content will be best suited to address the situation

• AI will make search and its search products more relevant, precise and efficient.

• AI through intents will be able to better know what content your knowledge workers need. Intents will provide a better understanding of what a person is looking for by better understanding the intended use of the content.

• Chatbots w/ Natural Language Processing (NLP): will provide cognitive capabilities to understand, interpret and manipulate human language that will enable the bots to anticipate the needs, attitudes and aspirations of users to aid in decision making and improve outcomes, all geared to achieve substantial business value.


AI in KaaS Predicting Trending Knowledge

• Predict trending knowledge areas/topics that your knowledge workers needs

• Leverages supervised learning algorithms that will learn over time.• Supervised learning allows the algorithm to make inferences from labeled data.


AI in KaaS: AI Delivering Smart Search

• AI will make search and its search products more relevant, precise and efficient.

• AI through intents will be able to better know what content your knowledge workers need. Intents will provide a better understanding of what a person is looking for by better understanding the intended use of the content.


AI in KaaS: Chatbots w/ Natural Language Processing (NLP)

• Personalize knowledge –through specialized knowledge assistants (bots) working together with users, knowledge can be made available in a personalized way depending on the needs of the consumer.

• Chat Bots will provide value for all knowledge workers in the various business areas along critical decision-making points with personalization of the delivery of knowledge.


Knowledge-as-a-ServiceArchitectural Framework


KaaS Architectural Framework



Search/Chatbot/AI Layer

Data Layer

Taxonomy/MDM Layer

Presentation layer

Capabilities Layer

IT Foundational

Canonical Data Model (Structured/Unstructured

Data)Data Warehouse Security

Managed Metadata/Controlled Vocabulary services

Ontology/Taxonomy ManagementData Governance

Search Chatbots TaggingContent

Management

API Services Bus

Agile DevContinuous

Testing/ReleaseMonitoring/Operations

Cloud Infrastructure

Security

Knowledge MappingBusiness Services

App Layer Tacit Knowledge

Explicit Knowledge

AI

Smart Search

Expert finder

Doc Vis ChatbotsTaxonomy

Mgmt

Knowledge BI

QueryActivity Metrics

Knowledge Capture

K. CaptureUsage

MetricsCurate & Harvest

Data Lake

Request Management

Data and AnalyticsTaxonomyDocument

Visualization

Core Search & Statistical Analysis

Natural Language Processing & Semantic Extractors

Search AssistanceCustom ML Algorithms

Interactive Knowledge Maps

Tacit & Explicit Knowledge

Delivery CoPs Chatbots

Knowledge Capture & Alignment

Ontology MgmtQuery

Activity Metrics

Usage Metrics

Smart Search

Expert finder

Doc VisK. CaptureCurate & Harvest


Usage Metrics

Knowledge-as-a-ServiceBenefits


Benefits of KaaS

• The main advantage of deploying a KaaS framework will not only be better management of the knowledge, but also better access to knowledge.

• Here are some key benefits:

• Personalized Knowledge Access• Predict trending knowledge areas/topics that your knowledge workers needs• Identify targeted knowledge for real-time engagement and content consumption• Personalize knowledge based on individual preferences

• Increase productivity by executing tasks and learning more efficiently and effectively

• Cognitive capabilities to enable bots to anticipate the needs, attitudes and aspirations of users to aid in decision making and improve outcomes


Benefits of KaaS

If you know where the knowledge comes from, where it goes, and how it is used your organization is well on its way to properly delivering a KaaS solution. This solution will enable the organization to deliver its services more effectively and efficiently by tapping into the collective knowledge of your organization.


KaaS Case Study:

Professional Services Firm (PSF)


Executive Summary

Executive leadership as part of a 3-year digital transformation effort has mandated that the organization must align the capabilities/strategy (mission & vision) of knowledge management with the strategic priorities of the business and to re-invent how Knowledge is distributed and access at PSF. In order to be successful in delivering a state-of-the-art Knowledge Management Solution it has been determined that Artificial Intelligence (specifically Machine Learning) be utilized.


Key Challenges

• The following have been identified as key challenges in delivering the Knowledge Management Solution:

• An initial high-level architecture must be developed to identify the product components and how they will interact with the overall and specific architectural components

• A flexible and extensible information architecture must be developed to facilitate content organization, metadata and taxonomy alignment to enable search, navigation and Machine Learning capabilities

• Understanding of the Artificial Intelligence elements that must be considered and how the new KM system will support/facilitate and align with the overall digital transformation effort.

• Tacit knowledge and explicit knowledge alignment and access must occur

• Language translation and localization challenges in the implementation of KM system


Addressing The Key Challenges




Data Layer

Taxonomy/MDM Layer

Presentation layer

Capabilities Layer

IT Foundational

Canonical Data Model (Structured/Unstructured

Data)Data Warehouse Security




Management

API Services Bus

Agile DevContinuous



Security



Explicit Knowledge

AI

Smart Search

Expert finder


Mgmt

Knowledge BI


Knowledge Capture

K. CaptureUsage


Data Lake

Request Management


Visualization








Ontology MgmtQuery

Activity Metrics

Usage Metrics

Smart Search

Expert finder



Usage Metrics


Results:

Provide Knowledge Base Migration

Capture and Catalog Tacit and Explicit Knowledge from across several business units and produce content that is solution base, fast and easily searchable and retrievable.

Delivered Chatbots leveraging ML to provide accurate delivery of knowledge both tacit and explicit

Establish AI Powered Enterprise Search utilizing Oracle Search® Producing Solution Based Results

Delivered a robust architecture that aligned with the overall digital transformation strategy

KaaS Case Study:

Cancer Machine Learning/ Data Analytics Tool


Executive Summary

New lung cancer cases effect over 218,000 people each year in which ~90% are Non-Small Cell Lung Cancer (NSCLC). Treatment problems have occurred because currently researchers have been unable to analyze a variety of NSCLC data from many sources quickly and effectively.

This problem can be addressed by applying a semi-supervised machine learning algorithm performing multivariate analysis against the variety of clinical NSCLC data


Key Challenges

• The following have been identified as key challenges in delivering the NSCLC Cancer Data Analytics Solution:

• The alignment of the types od data (structured, semi-structured and unstructured data) under one common ontology

• A flexible and extensible architecture focused on facilitating content organization, ontology management, and visualization of the results of applied Machine Learning

• Achieving an 85 to 90% accuracy rate of the machine learning algorithm to mine a variety of NSCLC data

• Tacit knowledge and explicit knowledge alignment and access


The Sherlock™ Big Data Knowledge Discovery &

Analysis tool is an application that will use AI

Machine Learning Algorithms to “mine” Big Data

Cancer repositories to extract knowledge. This

knowledge will be used to assist oncologists and

cancer researchers in enhancing diagnostic

decision making for patients with Non-Small Cell

Lung Cancer (NSCLC) and in discovering new

treatment strategies.

Tacit & Explicit

Knowledge

Alignment thru

Common Ontology

Self-learning

Neural Network

Data Curation

(Structured,

Semi-structured,

Unstructured)

Content/Data

Visualization

Data Analysis and Visualization

• Data analysis: Examined data concepts, relationships, business rules and metadata.

• Data Visualization: visual representation of the results of the machine learning algorithm including cluster analysis, dimensions in the data (features, predictors, or variables) and decision prediction analysis.

Data Classification/StandardizationNSCLC Ontology Structure

• Our product ontology is based on the NSCLC disease patterns as determined by the data from the National Center for Biomedical Ontology.

It is a combination of the following ontology resources:

NSLC Biomedical Ontology - Scientific Projects

NSCLC Physicians Data Query

Human Disease Ontology

Interlinking Ontology for Biological Concepts

http://bioportal.bioontology.org/ontologies/CRISP?p=classes&conceptid=http://purl.bioontology.org/ontology/CSP/4005-0042#visualization

http://bioportal.bioontology.org/ontologies/PDQ?p=classes&conceptid=http://purl.bioontology.org/ontology/PDQ/CDR0000040862#details

http://bioportal.bioontology.org/ontologies/DOID?p=classes&conceptid=http://purl.obolibrary.org/obo/DOID_3908#details

http://bioportal.bioontology.org/ontologies/IOBC?p=classes&conceptid=http://purl.jp/bio/4/id/200906023656598154

NSCLC ML Algorithm

The deep learning semi-supervised algorithm was used to…

• This algorithm will facilitate predictions on given set of samples.

• Searches for patterns within the value labels assigned to data points.

• This algorithm used labeled training set that contains both normal and anomalous samples for constructing the predictive model.

Copyright © 2019 A.J. Rhem & Associates, Inc.

Addressing The Key Challenges




Data Layer

Taxonomy/MDM Layer

Presentation layer

Capabilities Layer

IT Foundational

Canonical Data Model (Structured/Semi-

Structured/Unstructured Data)Data Warehouse Security




Management

API Services Bus

Agile DevContinuous



Security



Explicit Knowledge

AI

Smart Search

Expert finder


Mgmt

Knowledge BI


Knowledge Capture

K. CaptureUsage


Data Lake

Request Management


Visualization








Ontology MgmtQuery

Activity Metrics

Usage Metrics

Smart Search

Expert finder



Usage Metrics


Results:Provided Data Curation and alignment of Structured, Semi-structured, and Unstructured data.

Development of a common ontology to align all data types.

Delivered a flexible and extensible architecture focused on facilitating content organization, ontology management, and visualization of the results of applied Machine Learning

Delivered a semi-supervised learning algorithm to perform data analytics

Knowledge-as-a-ServiceConclusion


Conclusion

Knowledge-as-a-Service (KaaS) as described in this presentation evolves a concept first introduced in 2005 by S. Xu (Dept. of Comput. Sci., Texas Univ., San Antonio, TX, USA) and W. Zhang (Dept. of Comput. Sci., Texas Univ., San Antonio, TX, USA) in their IEEE article: Knowledge as a service and knowledge breachingIn this evolution of KaaS I have integrated AI & KM to bring about a higher level of knowledge access and application on a personalized and action oriented focus.The case study presented is only one such example of how AI is evolving. As AI continues to evolve KM will be essential to its evolution. Another emerging evolution of AI is Cognitive Digital Twins. KaaS will provide a valuable framework for the realization of Cognitive Digital Twins technology at organizations.


https://ieeexplore.ieee.org/document/1531242

https://cognitiveworld.com/term/tags/cognitive-digital-twins

Dr. Anthony J. Rhem, PhD.

CEO/Principal Consultant

500 North Michigan Ave., Suite 600Chicago, Illinois 60611Phone: 312-396-4024

email: [email protected]

Website: www.ajrhem.com

Blog: http://knowledgemanagementdepot.com/

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mailto:[email protected]

http://www.ajrhem.com/

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