This tutorial seeks to showcase AI strategies that provide medical context to patient data with the help of a knowledge graph. This supports personalization through a personalized knowledge graph that captures the patient’s personalized health management objectives within the context of the clinical guidelines and care plan. The continuous capture of this information through the analysis of patient-VHA interactions, and the strategy of creating engaging interactions (conversations) can further augment the personalized knowledge graph. These operations are required to support self-appraisal and self-management, and when necessary perform fail-safe tasks such as connecting the patient to a crisis help-line or professional help. The core innovation is the use of a novel knowledge-infused reinforcement learning method. The by-product of this approach leads to transparency in decision-making with the ability to offer a user understandable explanation. https://www.knowledgegraph.tech/kgc-2022-tutorial-knowledge-infused-reinforcement-learning/ More Information: https://aiisc.ai/kirl/

1. Knowledge Infused
Reinforcement Learning
Use-case: Conversational Systems
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Tweet, post on LinkedIn your insights, screenshots, and questions
#KGC2022 #ProcessKnowledge #KnowledgeInfusedLearning #safeAI
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http://aiisc.ai/kirl

2. About the Tutors
2
Qi Zhang,
Professor, CS@UofSC
Planning and Reinforcement
Learning in Multi-Agent Systems,
Knowledge-infused
Reinforcement Learning
Amit Sheth,
Professor, CS@UofSC
Director, AI Institute @ UofSC
Knowledge-infused Learning,
Knowledge Graphs, Semantic
Web, Natural Language
Understanding,
Conversational Systems
Manas Gaur,
Ph.D. in CS @ UofSC
Researcher, AI Institute @ UofSC
Knowledge-infused Learning,
Natural Language
Understanding, Knowledge
Graphs,
Kaushik Roy,
Ph.D. in CS @ UofSC
Researcher, AI Institute @ UofSC
Knowledge-infused
Reinforcement Learning,
Conversational Systems

3. D
O
M
A
I
N
S
3
Conversational AI in [Healthcare]
Sheth et al. 2018, Roy et al. 2021, Gaur et al. 2022
[Pandemic/Crisis] Matching Support Seekers with
Support Providers (Gaur et al. 2021)
[Education] ALLURE Collaborative Rubik’s Cube Solver
Agostinelli et al. 2021 (https://tinyurl.com/AIISC-Rubiks)
[Food] Allergy-Aware Recipe Understanding
(Khandelwal et al. 2022) (https://tinyurl.com/AIISC-Allergies)

4. How to Build a Conversational Agent
4
Attention Neural Network
(Vaswani et al. NIPS’17)
Large Amount of Textual Conversational
Data in General Domain
Trained Generative Model
Picture Credit: https://keyreply.com/conversational-ai-
for-healthcare/

5. Agent tasked to Converse with Patient with Mental
healthcare Condition
5
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
The model can
generate and ask
either of these
questions
They are either bad
questions or irrelevant
( A clinician won’t ask
either of these)
A model trained to asked questions
Risky

6. 6
Unsafe Question Generation
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
The model can
generate and ask
either of these
questions
A model trained to asked questions

7. Large Language Models Hallucinate
7
❏ Generate Factually Incorrect Response
❏ Blenderbot 1 [Roller et al. 2020]
❏ Blenderbot 2 [https://tinyurl.com/Meta-bbot2]
❏ Generate Questions that aren’t Safe
❏ OpenAI GPT-3 [Brown et al. 2020]
❏ DeepMind’s RETRO [Borgeaud et al. 2021]
❏ Google’s LaMDA [Thoppilan et al. 2022]
❏ Utilize Google’s long list of Safety Guidelines to place constraint on
Safety.

8. 8
Do you feel nervous?
More than half the days
Do you feel irritated or self
destructive?
Do you feel something
extreme might happen to
you?
Are you able to relax?
Do you feel nervous?
More than half the days
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?
Knowledge
Infusion using
Medical
Questionnaire
(MedQ)
These questions are
medically valid and safe.
Safety
Checks
Knowledge fix : Safety Checks
Roy and Gaur et al. ACL’22

9. 9
Do you feel nervous?
More than half the days
T5
(Raffel et.
al.
ACL’20)
KI
Attention
Model
(Ours)
30.6%
17.1%
10.6%
13.3%
KI: Knowledge Infusion
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?
(Ours) Roy and Gaur et al. ACL
(under review)
Effect of Knowledge fix : Safety Checks

10. Attention Model
I feel bothered by little interest
and have least pleasure in
doing anything
Did you check your dopamine
levels?
Do you feel your brain is
affected?
Did you intend to indulge in
risky behaviors
I feel bothered by little interest
and have least pleasure in
doing anything
What does “lack of pleasure”
mean to you?
Do you feel little pleasure doing
things you used to enjoy?
How long have you struggled with
lack of interest in things you used
to enjoy?
KI Attention Model
DSM-5
Lexicons for
Depression
SCID for
Depression
PHQ-9
Questionnair
e
Gaur et al. WWW’19; Yazdavar et al. ASONAM’17
0.83
0.78
0.71
0.33
0.21
0.19

11. Safety Checks
11
If is cause then symptom
If is symptom then medication
If is medication then treatment
Probability next question generation is
Process
Do you feel nervous?
More than half the days
Do you feel Irritated?
Are you bothered by
becoming easily annoyed
or irritable?
Are you bothered by any
relaxation troubles?

12. Uncertainty and Risk
12
Generation Task
Handling Uncertainty or Risk
Human
Annotation
Experience
(e.g. History)
Web Search
Corpus
Expert
Guidelines
Generative
Output
Classification
Output
Labeled Dataset

13. Safety Checks: A method of infuse Knowledge
13
Generation Task
Handling Uncertainty or Risk
Generative
Output
Classification
Output
Labeled Dataset
Information
graph (KG,
Lexicons,
etc.)
Kashyap and
Sheth CIKM’94

14. Viable forms of knowledge to infuse
14
Generative
Output
Classification
Output
Labeled Dataset
Interpretability
User-level Explainability
Information
graph (KG,
Lexicons,
etc.)
Kashyap and
Sheth CIKM’94
Generative
Output
Classification
Output
Labeled Dataset
Interpretability
User-level Explainability
ConceptNet
Healthcare
lexicons

15. 15
Example KG constructed either from manual effort (A, B, C), automatically (D, E), or semi-automatically (F)
(A) is empathi ontology designed to
identify concepts in disaster
scenarios (Gaur et al. 2019).
(B) Chem2Bio2RDF (Chen et al. 2010).
(C) ATOMIC (Sap et al. 2019).
(D) Education Knowledge Graph by
Embibe (Faldu et al. 2020).
(E) Event Cascade Graph in WildFire
(Jiang et al. 2019).
(F) Opioid Drug Knowledge Graph
(Kamdar et al. 2019)

16. Process Knowledge : Suicide Risk
16
Columbia Suicide Severity
Rating Scale (C-SSRS)
Process Knowledge
Structure of C-SSRS
Posner et al. 2011
Am. Journal of Psychiatry

17. Process Knowledge : Anxiety
17
Spitzer et al. 2006
Archives of Internal Medicine
Bartolo et al. 2017
SciELO Brasil

18. Knowledge Infused Learning (KiL)
18
Knowledge-infused Learning is a class of Neuro-Symbolic AI
techniques that incorporate broader forms of knowledge (lexical,
domain-specific, common-sense, and constraint-based) into
addressing limitations of either symbolic or statistical AI approaches,
such as model interpretations and user-level explanations.
Compared to powerful statistical AI that exploit data, KiL benefit
from data as well as knowledge.

19. 19
Shallow Infusion of Process Knowledge
Deep Language
Model for
Classification
Semantic
Embedding Loss
❏ External Knowledge is utilized in Annotating the
Dataset
❏ Patient Health Questionnaire-9 (PHQ-9): an
instrument to measure severity of Depression.
❏ Input is matched to Question
❏ To perform user-level classification
❏ Encodes knowledge as either word embeddings (text)
or graph embeddings (graphs,rules, trees)
❏ User-level Explainability and Safety:
❏ Model’s classification is checked by the questions
answered correctly.
❏ Correctly answered questions are matched to
concepts highlighted in the input.
Agarwal and Gupta et al. ACL RR
Under Review
embedding

20. Semi-Deep Infusion of Process Knowledge
20
Deep Language Model
For Representation
Bernoulli Loss
embedding
Decision
Tree
❏ Utilizes knowledge in its original form to modify the
parameters of the ML model
❏ We employ and adapt interpretable ML models
E.g. Decision Trees, Naive Bayes, K-Nearest Neighbors
❏ Knowledge is incorporated in the objective function to
achieve parameter tweaking.
❏ User-level Explainability, Safety, & Interpretability:
❏ Model’s outcome is a tree → Interpretability
❏ Predicted Tree is checked with PHQ-9 Questions
→ User Level Explainability
❏ Bernoulli Loss keep refining the tree and can be
tweaked by experts.
Roy and Gaur et al. Arxiv
(https://tinyurl.com/AIISC-PKiL)

21. Deep Infusion of Process Knowledge
21
❏ Every layer within the deep neural network is checked for
information divergence using external knowledge
❏ In case of Process Knowledge and in the context of
Depression, we use Structured Clinical Interview
information for Depression
❏ Knowledge Control:
❏ Checks if current learned representation can answer
questions in SCID
❏ If none questions are answered,
❏ Information from SCID replace learned
representation
❏ If some questions are answered incorrectly ( )
❏ Learned representation is altered as follows:
Structured Clinical
Interview for Depression
(SCID)
backprop
Kursuncu and Gaur et al. AAAI-MAKE’19

22. 22
Context Sensitive Capture:
Statistical AI is opinionated based on the text it sees
and input is partial representation of the world.
Uncertainty and Risk:
Statistical AI, fail to establish the connection between
input and output
User-level Explainable:
Statistical AI’s explanations are system-oriented
and not rich enough for user-level understanding.
Interpretable:
A Statistical AI model that you can understand and
control
Task Transferable:
Statistical AI learns the data and not the task
Knowledge can highlight the context in input.
Knowledge can assess risky prediction
Knowledge can influence attention of statistical AI.
Knowledge can enable User-level explanations
Knowledge can help in generalize across tasks
Benefits of Knowledge-infused Learning

23. Tutorial Overview
23
❏ Challenges in Conversational Agents in Healthcare
❏ Can a model capture context and handle uncertainty in input?
❏ Can user-level explanations be obtained from the success or failure of an AI model?
❏ Can we control an AI model by making it interpretable?
❏ How can we make an AI model self-explainable?
❏ Tutorial Highlights
❏ Reinforcement Learning for Co-operative Multi-Agent System Interactions
❏ Process Knowledge Integration in Reinforcement Learning
❏ Demonstration of Process Knowledge Infusion

24. Tutorial Outline
24
❏ Introduction
❏ Reinforcement Learning
❏ Knowledge-infused Reinforcement Learning
❏ Demo
❏ Summary and Future Work

25. 25
1. Reinforcement Learning

26. What’s Reinforcement Learning?
Learn to make good sequences of decisions
26

27. What’s Reinforcement Learning?
Learn to make good sequences of decisions
⬢ Repeated interactions with environment
27

28. What’s Reinforcement Learning?
Learn to make good sequences of decisions
⬢ Repeated interactions with environment
⬢ Reward to measure the quality of sequence of decisions
28

29. What’s Reinforcement Learning?
Learn to make good sequences of decisions
⬢ Repeated interactions with environment
⬢ Reward to measure the quality of sequence of decisions
⬢ Don’t know in advance how environment works
29

30. Animal Reinforcements
Negative reinforcements
⬢ Pain and hunger
Positive reinforcements
⬢ Pleasure and food
30
Reinforcements used to train animals
Let’s do the same with machines!
⬢ Here reinforcements are called rewards.
⬢ Machines are called (reinforcement learning) agents.

31. Rewards
A reward r_t is a scalar feedback signal
Indicates how well the agent is doing at step t
The agent’s job is to maximize cumulative reward
31
Reinforcement learning is based on the reward hypothesis:
All goals can be described by the maximization of expected cumulative reward

32. Examples of Rewards
Defeat the world champion at Chess
⬢ +/- reward for winning/losing a game
⬢ No reward (i.e. 0 reward) in the middle of a game
Manage an investment portfolio
⬢ + reward for each $ in bank
Control a power station
⬢ + reward for producing power
⬢ − reward for exceeding safety thresholds
Make a humanoid robot walk
⬢ + reward for forward motion
⬢ − reward for falling over
32

33. The RL Loop
⬢ Goal: select actions to maximize total future reward
⬢ May require sacrificing immediate reward to gain more long-term reward
33
Environment
Agent
Observation,
Reward
Action
Examples: game playing (Go, Atari), operations research (pricing,
vehicle routing), robotic control, conversational agents, autonomous
vehicles, computational finance, etc.

34. The RL Loop: Blood Pressure Control
34
Environment
Agent
Observation:
Patient’s information
Reward:
+1 if healthy pressure; -
0.05 for side effects
Action:
Exercise or medication
⬢ Goal: select actions to maximize total future reward
⬢ May require sacrificing immediate reward to gain more long-term reward

35. The RL Loop: Conversational Agent
35
Environment
Agent
Observation:
User utterance
Reward:
Task completion, user
satisfaction, etc.
Action:
Next utterance
⬢ Goal: select actions to maximize total future reward
⬢ May require sacrificing immediate reward to gain more long-term reward

36. Multi-Agent RL: A One-Slider
36
Environment
Observation,
Reward Action
Agent N
Agent 1
.
.
.
Action 1
Action N
Obs. & Reward 1
Obs. & Reward N

37. Multi Agent example: Manufacturing
37

38. 38
2. Knowledge-infused
Reinforcement Learning

39. Knowledge Infused Reinforcement Learning
(KiRL)
39
Knowledge-infused Reinforcement Learning is a class of Neuro-
Symbolic AI techniques that incorporates knowledge (lexical,
domain-specific, common-sense, and constraint-based) into the
policy function, resulting in better performance, interpretable models
and user-level explanations.

40. Data
Knowledge Infused RL
State representation
Knowledge
States, actions, rewards
Knowledge infused Policy
Explanation module
Knowledge Infused RL architecture (single agent)
40

41. Example: Adaptive Contagion Control
41
Roy et al. AAAI-MAKE’20

42. Example: Adaptive Contagion Control
42
Roy et al. AAAI-MAKE’20

43. 43
End to end Chatbot using KiRL

44. Overall Execution Flow: Mental Health
44
❏ User Profile Graphs:
❏ Historical Information about
patients stored as Graph
❏ User Profile Graph + Clinical
guidelines drives the high level
decision making of the RL algorithm.
❏ The knowledge modifies the policy to
tend towards the knowledge as a
strong prior in a Bayesian formulation
(We derive a MAP estimate by
formulating it as an optimization
problem)

45. Example: Information Gathering
45
❏ Once the high level task is chosen, it is
expanded to low level execution.
❏ For example what information to gather is
obtained from a dialogue generation
system that takes as input the patient
profile
❏ The responses are parsed for new
information to be added to the patient
knowledge graph.

46. 52
User Level Explainability

47. User level explanations
53
Definition from DSM 5
KG: SNOMEDCT

48. Process Knowledge-based User level explanation
54

49. Process Knowledge-based User level explanation
55
Process Knowledge Structure in C-SSRS
I wish I could give a shit about what would
make it to the front page. I have been there
and got nothing. Same as my life. I do have a
gun.’, ’I thought I was talking about it. I am
not on a ledge or something, but I do
have my gun in my lap.’, ’No. I made sure
she got an education and she knows how to
get a job. I also have recently bought her
clothes to make her more attractive. She
has told me she only loves me because I
buy her things.
1. Wish to be dead - Yes
2. Non-specific Active Suicidal
Thoughts - Yes
3. Active Suicidal Ideation with
Some Intent to Act - Yes
4. Label: Suicide Behavior or
Attempt
Agreement with Experts
47%
Process
Knowledge (Ours)
70%
XLNet
Yang et al.
NIPS’19
Gaur and Sheth et al. Internet Computing
Under Review

50. 57
Process Knowledge-based User level explanation

51. 58
End-to-End Conversational Information
Seeking using KiRL

52. Example: Conversational Information Seeking
What is Conversational Information Seeking?
Conversational Information Seeking (CIS) is an emerging research area within Conversation AI that
attempts to seek information from end-users in order to understand and satisfy user’s needs.
A CIS system can assist clinicians in pre-screening or triaging patients in healthcare.
Here, the agent is the propeller of a conversation with a user
What are Information Seeking Questions (ISQs) ?
ISQs differ from other question type (e.g Clarifying questions, Follow-up questions) by having a
● Structure : semantic relations between questions and logical coherence
● Cover Objective Details
● Expand on the breath of topic
Gaur et al. AAAI 2022
® Samsung Research America

53. Properties of Human's Information Seeking
Questions
60
❏ Task Oriented
❏Seeking information for preparing a delicious cuisine
❏Seeking information about a health condition
❏ Question Styles
❏Contextually, lexically, and syntactically diverse
❏Semantically related and have logical order
❏ Response Shaping
❏Require understanding procedural questions
❏Keep track of entities and actions
Gaur et al. AAAI 2022
® Samsung Research America

54. ISEEQ: Information SEEking Question generation using Dynamic
Meta-Information Retrieval and Knowledge Graphs
61
Gaur et al. AAAI 2022
® Samsung Research America

55. Baseline T5 &
ISEEQ
62
1. Deep Language Models
generate irrelevant questions
(blockchain and currencies) in
absence of external knowledge
2. Without external knowledge,
the model fails to
(a) prevent redundancy
(b) showcase diversity in question
generation
ISEEQ​
Baseline T5
Gaur et al. AAAI 2022
® Samsung Research America

56. Example
64
Sentence BERT Encoder
Sentence BERT Encoder
1. What is gross_domestic_product?
2. What is the measure of gross_domestic product?
3. What is the reason nation income relations gross_domestic_product?
4. What is the influence of inflation to gross_domestic_product?
5. What is the meaning of unemployment in inflation?
6. What is the influence of inflation on cost_of_living?
Title: Economy and Employment
Statistics
Description: Learn Information about
key economic concepts including gdp,
inflation, and the influence on
employment
Constituency Parsing
Information + { economy, employment
statistics, employment, influence
employment, inflation influence
employment, gdp, gdp influence
employment, key economic concepts}
economics
economy inflation employment
gdp
gross
domestic
product
unemployment
gnp
gross national
product national
income
cost of living
income
personal
income
income
tax
ConceptNet Graph for Semantic Query Expansion
ISQ by Generative Adversarial Reinforcement Learning
Knowledge-guided
Passage Retriever
Gaur et al. AAAI 2022
® Samsung Research America
Query

57. How ISEEQ show improvement?
(Context: Healthcare)
65
Bothered by feeling
hopeless and
depressed.
Need Advice.
Generator
Network
• Are you feeling bothered?
• Are you depressed?
• Do you feel depressed?
Generated questions are do not
(a) Capture User Context
(b) Share Semantic Relations
(c) Have Logical Order
Qcurr
Baseline Generator-only Approach (T5)
Bothered by feeling
hopeless and
depressed.
Need Advice.
Generator
Network
Neural
Passage
Retriever
Reward
(Qcurr , Qtrue)
• Are you feeling bothered?
• Are you depressed?
• Do you feel depressed?
• Do you feel like you are
depressed sometime?
• Do you know depression can
make you mentally slow?
Generated questions
(a) Captures the context
(b) Share semantic relations
(c) Follow Logical Order
Generated questions are unsafe
Baseline Generator-only Approach (T5) with Neural Passage Retriever
Gaur et al. AAAI 2022
® Samsung Research America

58. 66
How ISEEQ show improvement?
(Context: Healthcare)
Bothered by feeling
hopeless and
depressed.
Need Advice.
Generator
Network
Neural
Passage
Retriever
Context Reward
(Qcurr , Qtrue)
• Do you know what cause depression?
• Can you describe hopelessness?
• Do you think you feel hopeless and
depressed?
• What is the cause of sadness?
Commonsense
Knowledge Graph
ISEEQ Variant 1
Generated questions are
(a) Captures the context
(b) Share semantic relations
(c) In logical Order
Gaur et al. AAAI 2022
® Samsung Research America

59. 67
How ISEEQ show improvement?
(Context: Healthcare)
Bothered by feeling
hopeless and
depressed.
Need Advice.
Generator
Network
Evaluator
Network
ISEEQ Variant 2
Context Reward
(Qcurr , Qtrue)
Order
(Qcurr, Qprev)
Neural
Passage
Retriever
Qcurr: Current Generated Question
Qprev: Previous Generated Question
Qtrue: Ground truth Question
1. Do you think you feel down most of the
time?
2. How often do you feel depressed or
hopeless?
3. How long have you struggled with
depression?
4. Do you know what cause depression?
Generated questions are
(a) Captures the context
(b) Share semantic relations
(c) In logical Order
Gaur et al. AAAI 2022
® Samsung Research America

60. Summary and Future Directions

61. System 1 and System 2 Synchrony
69
Sheth and Thirunarayan, Duality of Data and Knowledge, IEEE Computer Society 2021
Low-level Data
Sensors, Text,
Image, and
Collection
Neural Network
and Deep
Learning
Knowledge (rules,
graphs, process
knowledge, ..)
Symbolic
Reasoning
Decisions/Actions
System 1
System 2
Neural Network
and Deep
Learning
Decisions/Actions
System 1
Low-level Data
Sensors, Text,
Image, and
Collection
Daniel Kahneman - 2011
Thinking Fast and Slow

62. Corpus of
Linguistic
Acceptability
Summarizing
Clinical
Interviews
DSM-5 and PHQ-9
Flesch Reading,
Divergence,
Theme Overlap
Matthew Correlation
Rich
Evaluation
metrics
Stanford
Sentiment
Treebank
Assessing Severity in
User-generated Content
Ordinal Error,
Perceived Risk
Measure, Ranked
Precision/Recall
DSM-5 and Drug
Abuse Ontology
Accuracy
Question NLI
ConceptNet and
WordNet
Concept Mover
Distance, BLEURT
F1-Score and
Accuracy
User-language
Paraphrase
Corpus
Microsoft
Paraphrase
Corpus
Recognizing
Textual
Entailment
Conversational
Information
Seeking
Process Knowledge
NLG
Gaur et al. JMIR’21 Gaur et al. Pone’21,
WWW’19, ACL’ 22
Roy & Gaur et al. ACL
Under Review
ConceptNet,
WikiNews, Wikipedia ,
MS-MARCO
Logical Coherence,
Semantic Relevance,
BLEURT
Accuracy
PHQ-9, GAD-7,
C-SSRS
Accuracy
Avg. # Unsafe
Matches, Avg.
#KG concept
Matches,
Avg. Sq. Rank
Error
Reagle & Gaur
FirstMonday’22
General Language Understanding Evaluation
(GLUE) (Wang et al. ICLR’19)
Knowledge-intensive Language Understanding (KILU)
(Sheth and Gaur IEEE IC’21)
Gaur et al. AAAI’22
Publicly Available Datasets with Knowledge Sources

63. 71
Multimodal Conversational System for Autism Assessment
Using ADOS
Autism
Diagnostic
Observation
Schedule

64. 72
Personalized Knowledge Graph in Autism Assessment

65. 73
User-level Explanations for Trust in AI System for Autism
Assessment

66. References
74
❏ Kaushik, R., Gaur, M., Zhang, Q., & Sheth, A. (2022). Process Knowledge-infused Learning for Suicidality
Assessment on Social Media. Scholar Commons.
❏ Sheth, Amit, Manas Gaur, Kaushik Roy, and Keyur Faldu. "Knowledge-intensive language understanding for
explainable ai." IEEE Internet Computing 25, 2021.
❏ Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, and Hongxia Jin. "ISEEQ: Information Seeking Question
Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs." arXiv preprint arXiv:2112.07622
(2021). In AAAI 2022
❏ Kaushik Roy, Qi Zhang, Manas Gaur, and Amit Sheth. "Knowledge Infused Policy Gradients for Adaptive
Pandemic Control." (2021). In AAAI 2021
❏ Kaushik Roy, Qi Zhang, Manas Gaur, and Amit Sheth. "Knowledge infused policy gradients with upper
confidence bound for relational bandits." In ECML-PKDD 2021
❏ Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (k-il): Towards deep incorporation of
knowledge in deep learning." In AAAI 2019
❏ Gaur, Manas, Keyur Faldu, and Amit Sheth. "Semantics of the black-box: Can knowledge graphs help make
deep learning systems more interpretable and explainable?." IEEE Internet Computing, 2021
❏ Sheth, Amit, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning
for enhancing deep learning." IEEE Internet Computing, 2019

67. Acknowledgement
75
Contribution to Demo on Virtual Assistant for Mental Health
We acknowledge partial support from the National Science Foundation (NSF)
award # 2133842 “EAGER: Advancing Neuro-symbolic AI with Deep Knowledge-
infused Learning,” with PI Dr. Amit Sheth. We also acknowledge partial support
from University of South Carolina ASPIRE Award
Any opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the views
of the National Science Foundation or University of South Carolina.
Vedant Khandelwal
Ph.D. Student, AIISC

68. Questions?
For further details, please send email to
mgaur@email.sc.edu
More Project on Knowledge Graphs and Knowledge-infused Learning:
http://wiki.aiisc.ai/
76