Cognitive Systems Redefining the Library
User Interface
Authors:
Kshama Parikh, Institute of Law Library, Nirma
University. ([email protected])
Saurin Parikh , Florida Atlantic University, USA
and Institute of Technology, Nirma University,
India. ([email protected])
Dr. Hari Kalva, Florida Atlantic University, USA.
([email protected])
Dr. David Jaramillo, IBM, USA.
([email protected])
Problem – An Overview
• Library members may feel overwhelmed with
information available on websites or library portals
and often requires human assistance to find
pointers to needed information.
• In a eLibrary scenario, library staff cannot respond
to members’ queries, 24/7 in real time.
• Ability to address to members concerns in real time
may lead to better service.
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Proposed Solution
• A cognitive system with an ability to interact,
understand, reason, and learn just like a Human
Brain.
• Interacts in Natural language, which may provide a feel of
having conversation with a library staff.
• Understand the context from the content just like a human
brain.
• Does reasoning to provide confidence weighted responses
with supporting evidence.
• Learns and trains from new discovery and inputs, which
may improve the responses.
• Cognitive system may reduce the effort and time
needed to access resources and information.
Understand
Reason
Interact
and
Learn
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Watson – The Cognitive System Platform
Watson a cognitive technology platform launched by IBM has an ability to
understand, reason , learn and interact just like a Human. [1],[2]
Characteristics :
• Interact :- Watson, can engage in interaction with human users in natural
language by using chatbots.
• Understand :– can analyse unstructured and structured multimedia data
such as text, images, audio, and video in order to predict context from
content.
• Reason :- has human like reasoning ability. It can identify user’s
personality, tone and emotional traits in order to provide personalized
recommendations.
• Learn :- new discovered information. uses machine learning techniques
to train itself by learning from
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Why not just use a search engine /tool ?
Search Engine Scenario :
• In Google, if you type the query:
'anything that's not an elephant.'
• What do we get?
Many images related to elephants.
Watson Scenario :
• It understands the context from the content
and does not retrieve information only on
basis of ngram matches from the query.
• It understands that difference between
following phrases, as both have totally
different context [2]:
"when feet run" and "when noses run",
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How Does Watson Work ? (A comparison with conventional
computing approaches)
Conventional Computing approaches
Handles structured data
Example: Data stored in the databases.
Cognitive Systems (Watson’s Approach)
Along with Structured Data, it also understands,
unstructured data that occupies huge amount of data
space [1], [2]
Example : reports, blogs, posts and tweets etc.
In order to respond to a Query it relies on well In order to respond to queries, it relies on natural
specified information of well defined fields of language, which is governed by rules of grammar,
structured data stored in Database.
context, and culture [1], [2]
(Unstructured data is normally in natural language)
Query based Information Search is governed by Sentence is interpreted grammatically, relationally and
keyword matching (ngram matching).
structurally. It understands context and predicts the
real intent of the users’ query [1][2].
Can not gain insight into domain specific knowledge Watson works in a particular field or Domain, it
over time.
learns the language, the jargon and gains domain
insight and knowledge from experiences [1],[2],[3]
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How Does Cognitive System (Watson) collect
Domain Knowledge – An Overview
Loads the
relevant body
of literature
Training by
human
experts and
learning by
ongoing user
interaction
Learns by
using
machine
learning
approach
Watson
Cognitive
System
Content is
curated
(Selected &
organized)
Undergoes
data
ingestion
(importing
Data into
Database)
Cognitive Systems learns,
adapts and keeps relearning
Domain Knowledge.
Create a
knowledge
graph
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IBM Watson – Available as set of Services
• Watson is available as set of services and service can be
integrated [1],[5].
Watson Services [4][5] :
Higher Reasoning Skills
• Conversation
• Language Translation
Knowledge Organization
Skills
• Document Conversion
• Retrieve & Rank
• AlchemyLanguage
• AlchemyData News
Foundational Cognitive
Skills
• Speech to Text / Text to Speech
• Personality Insights
• AlchemyVision / Visual Insights
• Visual Recognition 8
Architecture of a Cognitive System
Interfaces
Messenger
IOT Apps
Mobile Apps
Library User
Other
Services
Watson
Cognitive
System
(Library
Assistant)
Conversation
Service
Non SQL Data
sources
(Unstructured Data)
Robots
Library
System
SQL
Database
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Watson Characteristics – Interacting like a Human
– Introduction to Conversation Service
What is Conversation Service and its uses?
• Conversation service allows to create virtual agents and bots that combine
machine learning, natural language understanding, and integrated dialog tools
to provide automated customer engagements.
• Natural language interface can be added to applications in order to Automate
interactions with end users. (Human like conversation) and allows to build
natural conversation flows between apps and users.
• Can integrate chat bots into web / mobile application which can communicate
on any channel or device.
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Basic Conversation Service Pattern (How it works)?
• Heterogeneous Input Platforms:
Watson conversation service adds
conversational capability to apps
such that it can interact with endusers on their platforms of choice,
such as :Mobile apps , messaging,
IOT and robots.
• Natural Language Classifier and
the conversation service-building
Blocks : allows to input our library
domain expertise in form of
1) User Input Examples:
Context : eLibrary access
Source : The Era of Cognitive Computing (by Rob High, Jr, IBM)
Natural Language Classifier
Intent Example :
Search a resource
Intents
Dialogs
Intents, Entities and Dialogs
• Watson Dialog Implementation :
The service outputs a trained
model,
which
will
enable
conversation in natural language
with end users.
Entity Example :
Sodhaganga
Entities
Trained Model
a) cant find Sodhaganga
b) Where is link of
Sodhaganga?
c) I need to refer to
Sodhaganga
2) NLC Classifier
(Prediction)
Intent predicted :
Search a resource.
Confidence Weightage :
0.89123
Entity predicted :
online resource
Confidence Weightage :
0.95321
3) Provide Response :
invokes the database system
or retrieves related
web
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links for providing to user
What are Intents ?
• An intent is the goal or purpose of the user's input [3].
• Intent Examples : Possible interaction scenarios in a library
•
•
•
•
•
•
•
•
Member is searching for specific resource,
Member inquires regarding accessibility of resources from off campus,
Need to know transaction rules for issue, renew ,reserve or return a resource,
Searching for the location of the resource,
Inquire about resource availability,
requires alternate recommendation on non availability of a resource,
Specific questions about membership rules,
Needs resource recommendation, pertaining to a concept.
• User conversation examples are added to intents in order to help our chat-bot
understand different ways in which people would interact with it (All possible
examples are not needed to be added as it uses machine learning techniques to
predict the intent)
• Examples on next slide
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Sample Library Intents, designed in IBM Watson [1]-[3].
User Interaction
examples
Intents
User Interaction examples
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What are Entities ?
• Entity is a portion of the user's input that can be used to provide a different response
to a particular intent [2][3].
• Values and synonyms of entities can be added and it helps bot learn and understand
important details about users' intent [2][3].
• Following are few samples of library entities with their synonyms created in IBM
Watson [1]-[3].
Entity :Resource Lending with its sample
values and synonyms
Entity :Digital
Resources with its
sample values
Entity : Transaction-types with its
sample values
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What are Dialogs?
Watson Dialog – Library user dialog, a sample example
• It can define Conversation flows
[2][3].
• Dialog uses intents, entities, and
context from application to
define a response to each user's
input [2][3].
• Dialog defines how your bot
will respond to user queries
Dialog (conversation flow) is designed
for “searching a resource” intent
Intent
Entity
• An example of Watson Dialog is
shown on the right side on this
slide prepared using IBM
Watson [1],[2][3][4].
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Screen shot showing Natural Language Interaction
Scenario – A sample example
One Conversation
scenario for
searching and
lending a resource
User Query : “do
you have a book
on Watson”
Predicted Intent :
#search-forresource
Dialog design:
conversation flow
for searching a
resource (intent :
search resourcelending
Predicted Entity:
@Resourcelending:Book
Click
for
Demo
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Cognitive System supporting various forms of
expression (inputs)
Cognitive systems support various forms of expressions that are more natural
for human interaction [5].
• Human expression (inputs) comes in many forms such as:
 Written,
 Verbal and
 Visual
How to learn various representation of Data in order to predict context, user
intent and entities from user input ?
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Watson Uses Machine Learning Techniques with
Deep Learning to learn representations of Data.
User Query : Show me books explaining this concept
Watson uses Machine Learning Techniques with Deep Learning [5]
• Learns representations of data by modelling high-level abstractions and uses
model architectures with multiple layers of non-linear transforms.
• Overcomes challenges of designing hand-crafted features for tasks [5]
“Pi Notation”
(Product notation)
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Summary
• Watson redefines the means of user interaction with library systems
by providing a more human like conversation in natural language.
• Watson simplifies the way of inputting domain expertise by
designing intents, entities and dialogs.
• Library Cognitive system will learn the user behaviour and adapt to
their changing needs with help of machine learning techniques and
formal & informal training.
• In few years. Cognitive systems will be replacing the role of
transaction processing systems[5].
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References
1) IBM Bluemix documentation, http://www.ibm.com/bluemix.
2) Learn how IBM Watson works and has similar thought processes to a human,
http//www.ibm.com/Watson.
3) ZeroToCognitive, https://www.youtube.com/watch?v=Jj7IFjd3FyI&index=1&list=PLnJzIOiv6cVTaS8k90R3T9AlS_kf5XWmX
4) https://github.com/rddill-IBM/ZeroToCognitive
5) Era of cognitive computing-Technical Strategy, Rob High Jr, IBM
Thank you
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