Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning

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After the era of Symbolic AI in the 20th century and Statistical AI in the first two decades of this century, there is a growing interest in the neuro-symbolic AI approach. It seeks to combine the respective powers and benefits of symbolic and statistical AI using knowledge graphs and deep learning. We have coined the term Knowledge-infused (deep) Learning (KiL) for a class of approaches that use a variety of knowledge at different levels of abstractions. This project will advance early and limited forms of enhancing deep learning with knowledge, called shallow and semi-deep KiL, with a more advanced form called deep-infusion. This project focuses on developing a deep learning architecture and associated algorithms that involve interleaving broader varieties of knowledge at different levels of abstractions or layers in a deep neural network.

Following activities are being pursued in this project.

  1. We are developing novel datasets that would exercise the development of novel algorithms that work in synchrony with human knowledge.
  2. Human knowledge is manifested in various forms such as rules, lexicons, relationships, relational databases, and knowledge graphs. We specifically focus on the knowledge graph's integration in deep learning algorithms (e.g., deep language models) to achieve explainability and interpretability.
  3. For explainability, specifically, user-level explainability is described as achievable through algorithms that connect its outcome with knowledge graphs and, when converged, reflect on the part of the knowledge graph that describes the predictions.
  4. For interpretability, at first, we are working towards leveraging simple and interpretable machine learning models that can help explain the internal mechanism of deep language models. Subsequently, we will work towards leveraging our understanding of the model's capability to define stratified knowledge structures like decision trees to allow the model to learn such trees at each layer with the help of a knowledge graph. With knowledge graph infusion, we provide semantic grounding to statistical models with unreasonable and non-deductive outcomes.
  5. AI systems have been stymied due to the lack of safety in data-driven AI. We have started to investigate how using domain (including process)knowledge as part of KiL methods can make AI systems safer.
  6. Building upon foundational research in this project, we have worked on several translational research opportunities that apply and advance KiL approaches. These include personalized health (specifically, mental health), personalized nutrition(specifically, management of carbohydrate intake in children with type 1 diabetes), and autonomous systems (including autonomous vehicles and smart manufacturing- see the third illustration in the attachment).

Current Research Questions in KiL

Based on our recent work under the purview of this grant, we have been asking the following questions concerning explainability, interpretability, safety, and reasonability.

  1. When do neural language models require non-parametric knowledge? We consider non-parametric knowledge as the source created/curated by humans. It includes lexicons, knowledge graphs, relational databases, etc.
  2. How do infuse non-parametric knowledge seamlessly into statistical AI models?
  3. It is known that deep neural models are learned by abstraction; how to leverage external knowledge’s inherent abstraction in enhancing the context of learned statistical representation?
  4. If the knowledge infusion is meant to happen at various layers in deep neural networks, how would the network regularize to prevent over-generalization or superfluous generations?
  5. We have established that the attention matrix in current transformer models makes the model reactive to global and local information in the input. It does by token-by-token square matrix. If we want to perform infusion, we must introduce two new matrices: (a) token and entity and (b) entity and entity. Simple matrix multiplication won’t work as these are out of the distribution matrix. Hence, we need to seek ways of creating a knowledge-aware attention matrix for the model from (a)token-by-token matrix, (b) token and entity, and (c) entity and entity matrix.
  6. Layered knowledge infusion might result in high-energy nodes contributing to the outcome. This is counter to the current softmax prediction. How to pick the most probable outcome? This would require us to explore marginalized loss functions using infused knowledge and input.
  7. How do you enable the generation of user-level explanations? (see the second illustration in the attachment).
  8. How to enforce safety constraints in model generations. This has been a pressing need since models tend to generate risky sentences (see the fourth illustration in the attachment).

Significant Results So Far

An overview of significant results reported in the publications and other dissemination is provided below.

  1. We explored various statistical bottlenecks in deep neural language models from the perspective of user-level explainability, interpretability, and safety. These pre-trained models are efficient, but the datasets they are trained on are not grounded in knowledge. Sheth et al. and Gupta et al. found that models hallucinate while generating responses leading to a factually incorrect or superfluous response. We investigated various methods of controlling this hallucination.
  2. We hypothesize that humans communicate through a contextual process of understanding and response. This process can either be an n-ary tree, a flat graph, or any structure of the conceptual flow. But, we face a challenge in terms of datasets. So, we created datasets cycling through a month-long annotation, evaluation, and quality check process. These datasets have been constructed under the purview of this grant and will be made available. The procedure we laid out in constructing these datasets contributes to the significance of our results. Every dataset was created automatically following deep learning and clinical knowledge. Subject Matter Experts were tasked to evaluate our labeling process. By this means, we not only checked that our knowledge-infused learning pipeline was accurate but also scaled the annotation-evaluation process by multiple folds and reduced time. Roy et al. could gather ten Subject Matter Experts' evaluations on five sets of outcomes from 5 different deep language models.
  3. We found that the simple neural language model can provide explainable results, and we also saw that experts achieved satisfactory agreement scores of >75% with simple language models.
  4. Further, we could achieve task transferability in models as they are trained on relatable clinical process knowledge-driven datasets. So far, we have found convincing results in Depression, Anxiety, and Suicide-related research. This also marks our first concrete step in realizing Knowledge-infused Learning.
  5. Some challenges arise when mental health conditions are comorbid with diseases like Cardiovascular, where we require contextual information on diseases and gender along with users' expressions.
  6. Next, we explored the domain of conversational AI as chatbots need to be safe when they communicate with the user with depression, anxiety, or suicidal tendencies (Gaur et al. CIKM). We have developed novel evaluation metrics and interpretable and explainable algorithms for process knowledge infusion in the Knowledge-infused Learning paradigm (Roy et al. ACL-IJCNLP).
  7. We studied mental health conversations related to Cardiovascular disease on social media, which requires domain knowledge. We developed knowledge-assisted masked language models in a task adaptive multi-task learning paradigm. We could differentiate the gender language and gender-specific symptoms based on user posts and comments. Lokala et al. proposed framework GeM fall under shallow knowledge-infused learning as we use external lexicons on Anxiety, Depression, and Gender for Knowledge-aware Entity Masking.
  8. The diverse forms of knowledge we infused into statistical AI are correlational. They are defined by word co-occurrence, synonymy linkage, and others but aren't causal. Representation of causality in AI systems using knowledge graphs can further improve explainability. Jamini and Sheth proposed a neat architecture on why causal knowledge graphs (CKGs) are needed, what modifications need to be made in existing knowledge graphs, and how infusion would occur.
  9. Within the scope of Knowledge-infused Learning, the causality aspect made us explore the autonomous driving domain. There are various scenarios in autonomous driving where the vehicle needs to decide based on what it has learned in other similar situations. Situations are scenes, and every scene has an interconnected set of entities that describe the scene. Wickramarachchi et al. developed a scene ontology for autonomous driving use cases and used it to extract entities from scene descriptions. An interconnection of scene entities is what is termed a scene graph. Such a graph improves machine perception in autonomous vehicles and can define sensible actions. Scene graphs and actions are absorbed by the architecture proposed by Jamini et al. to construct CausalKG.
  10. Along with mental health and healthcare in general, we are exploring the utility of knowledge-infused learning in autonomous driving. We find synchrony between these domains as the machine is tasked to provide action. Thus a correlation-alone knowledge graph falls short in expressing high-order semantic knowledge as expressed by humans.
  11. In a complementary direction, we studied the COVID-19 pandemic with a motive to help policymakers with explainable AI tools. Sivaraman et al. presented EXO-SIR, an epidemiological model supported by a component that considers external knowledge from textual reports to bootstrap SIR in estimating the likelihood of a rise in infections.

Illustrations: Key Concepts and Advances

Illustration 1

Illustration 01 - Progress in this project includes development of a deep knowledge infusion architecture, showing use of stratified knowledge (i.e., knowledge at different levels of abstractions) to modulate parameters at different layers in the deep neural network architecture. This figure shows adaptation of this advance in this project to the use case in autism at the center of the NSF AI Institute proposal submitted in May 2022 involving 11 institutions (Prof. Sheth is also the PI on that pending proposal)

Illustration 2

Illustration 02 - This figure shows the application of user-level explainability enabled by use of knowledge graph in KiL in the use case of autism to explain the reasons of referring a student to a school psychologist.

Illustration 3

Illustration 03 - Reddit is a rich source for bringing crowd perspective in training DLMs over conversational data. On the left is a sample post from r/depression help which sees inquisitive interaction from other Reddit users. At the top-right are the Follow-up Questions (FQs) asked by the Reddit users in the comments. These FQs are aimed at understanding the severity of the mental health situation of the user and are hence, diagnostically relevant. At the bottom-right are the questions generated by DLMs. It can be seen that these are not suitable FQs.

Illustration 4

Illustration 04 - The failure of large language models in supporting safety has been well publicized (left). The figure on the right shows the use of domain knowledge as part of Knowledge-infused Learning to ensure the conversation is medically valid and safe (as discussed in one of our publications).



The special issue on Knowledge-infused learning showcase interdisciplinary research that alleviate four critical limitations a) context capture, b) handling uncertainty and risk, c) model interpretability, and (d) model explainability. Further, the knowledge infusion has shown improvement in application performance. The article “Where Does Bias in Common Sense Knowledge Models Come From?” investigates methods to quantify the source of bias in COMET, a common sense transformer. The novel metrics presented can be considered as a proxy for measuring bias in large KGs. This would regulate knowledge infusion in pretrained language models. The article “Common sense Knowledge Infusion for Visual Understanding and Reasoning: Approaches, Challenges, and Applications” provides in-depth review on knowledge infusion in visual understanding. An explainable method for question answering is required when the language models are required to generate a paragraph-long answer. It is because the generated paragraph should preserve the context of the scenario. This article “SR3: Sentence Ranking, Reasoning, and Replication for Scenario-Based Essay Question Answering” shows how background knowledge bases can inform natural language questions and answer generations.

The next article “Physics-Informed Machine Learning for Uncertainty Reduction in Time Response Reconstruction of a Dynamic System” includes interpretable machine learning, which proposes a method to incorporate a physics-based model to quantify and remove uncertainty when the model is making predictions in dynamical processes.

Today, knowledge infusion is the driving force for next-generation neuro-symbolic AI methods and frameworks. It sees applications in robotics, cognitive science, self-driving cars, personal assistants, etc. With the deep infusion, we envision that the AI models will see a new form of learning, where they will learn by abstraction and control their learning behavior using stratified external knowledge. According to DARPA, the third wave of AI is about contextual adaptation and explanations, where explicit knowledge will have a significant role to play. Moreover, AI will start learning from many disciplines to realize the future potential of engaging and assisting humans in various domain-specific, low resource, and sensitive tasks.


  1. Roy, Kaushik, et al. "Knowledge-Infused Self Attention Transformers." KDD Workshop on Knowledge Augmented Methods for NLP (2023). (Link:
  2. Roy, Kaushik, et al. "Process Knowledge-infused Learning for Clinician-friendly Explanations." AAAI Second Symposium on Human Partnership with Medical Artificial Intelligence (2023). (Link:
  3. Sheth, Amit, Kaushik Roy, and Manas Gaur. "Neurosymbolic Artificial Intelligence (Why, What, and How)." IEEE Intelligent Systems 38.3 (2023): 56-62. (Link:
  4. Roy, K., Gaur, M., Rawte, V., Kalyan, A., & Sheth, A. (2023). ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance., Frontiers Big Data, 09 January 2023 (Link :
  5. Roy, K., Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023). Demo Alleviate: Demonstrating artificial intelligence enabled virtual assistance for telehealth: The mental health case. Thirty-Seventh AAAI Conference on Artificial Intelligence. (Link:
  6. Roy, Kaushik, and Vipula Rawte. "TDLR: Top Semantic-Down Syntactic Language Representation." NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives on Attention. 2022. (Link :
  7. Wickramarachchi, Ruwan and Henson, Cory and Sheth, Amit. (2022). Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems. IEEE Intelligent Systems. 37 (4), pp. 1 to 7. doi: Fulltext Citation details
  8. Lokala, Usha and Srivastava, Aseem and Dastidar, Triyasha and Chakraborty, Tanmoy and Akhtar, Md. Shad andPanahiazar, Maryam and Sheth, Amit. (2022). A Computational Approach to Understand Mental Health from Reddit:Knowledge-Aware Multitask Learning Framework. Sixteenth International AAAI Conference on Web and Social Media (ICWSM). 2022, pp. 640-650.Fulltext Citation details
  9. Sivaraman, Nirmal Kumar and Gaur, Manas and Baijal, Shivansh and Muthiah, Sakthi Balan and Sheth, Amit. (2022). Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection. International Journal of Data Science and Analytics. doi: ; Fulltext Citation details
  10. Jaimini, Utkarshani and Sheth, Amit. CausalKG: Causal Knowledge Graph Explainability Using Interventional andCounterfactual Reasoning. IEEE Internet Computing. 26 (1), 2022, pp: 43 - 50. doi: Citation details
  11. Gupta, Shrey; Agarwal, Anmol; Gaur, Manas; Roy, Kaushik; Narayanan, Vignesh; Kumaraguru, Ponnurangam; Sheth, Amit. "Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts," The Eighth Workshop on Computational Linguistics and Clinical Psychology: Mental Health in the Face of Change (CLPsych20022 @ NAACL2022), 15 July 2022.
  12. Venkataramanan, Revathy; Padhee, Swati; Rao, Saini Rohan; Sundarajan, Anirudh; Kaoshik, Ronak; and Amit Sheth. “Ki-Cook: Knowledge infused Multimodal Cooking Representation Learning”. Submitted for publication.
  13. Gaur, Manas, Kaushik Roy, Anmol Agarwal, Shrey Gupta, Amit Sheth, Ponnurangam Kumaragaru, "Research PK-iL: Explainable Dataset and Method for Suicidality Detection on Reddit using Process Knowledge in C-SSRS", Under Review.
  14. Gaur, Manas, Gunaratna, Kalpa, Bhatt, Shreyansh, Sheth, Amit, Special Issue on Knowledge Infused Learning in IEEE Internet Computing (Link: (In print)


  • PRIMATE is a mental health dataset containing 2003 user posts from Reddit with each post having 3 attributes: 1) title, 2) post text, and 3) binary yes/no labels for each of the 9 questions in PHQ-9 depending on whether the question is answerable using the content present in the post text & title. The dataset can be used to guide language models to generate non-redundant questions which have not already been answered in the user post. The dataset is released at:
  • Reddit C-SSRS Suicide Dataset : Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention. Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition, and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia.Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset. Link to the dataset :
  • Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS : Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt or complete suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e. voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing natural datasets of users experiencing suicide-related ideations, suicide-related behaviors or suicide attempt ( manifested through their communication on r/SuicideWatch and associated mental health subreddits. Through a widely recognized questionnaire to assess suicide risk severity, The Columbia Suicide Severity Rating Scale, the domain experts in the study annotated 448 users with following labels: Supportive (new add to C-SSRS and specific to social media), Suicide Ideation, Suicide Behavior, Suicide Attempt. High standards in annotation were maintained with substantial inter-rater agreement of 0.76. Link to the dataset :

Keynotes and Invited Talks

  1. Sheth, A., Knowledge-infused NLU for Addiction and Mental Health Research, Keynotes at the MAISoN21, Aug 2021, and the ASONAM21, Sept 2021.Presentation-GDriveVideo-MAISoN@IJCAI
  2. Sheth, A., Don’t Handicap AI without Explicit Knowledge, Keynotes at the IEEE Services, Sept 2021, and the DEXA2021, Oct 2021. Presenation-GDriveVideo IEEE SErvicesVideo-DEXA
  3. Amit Sheth, Semantics of the Black-Box: Towards Knowledge-infused Learning --- Keynote talk at the Semantic Machine Learning Workshop in conjunction with the 15th IEEE International Conference on Semantic Computing, February 2021,
  4. Sheth, A., Augmented Personalized Health: an explicit knowledge enhanced neural symbolic Health approach to patient empowerment for managing chronic disease burden. Keynote at the SWAT4HCLS (Semantic Web Applications and Tools for Healthcare and Life Sciences), ​​January 2022. SlidesharePresentation-GDriveVideo
  5. Amit Sheth, From NLP to NLU: Why we need varied, comprehensive, and stratified knowledge (Neuro-symbolic AI), Keynote at KnowledgeNLP at AAAI2023, February 2023. Slides Abstract Video


  1. Utkarshani Jaimini, Usha Lokala, Kaushik Roy, Amit Sheth, "Causal AI for web and health care,”Tutorial at TheWeb Conference, April 2023.
  2. Roy, Kaushik; Usha, Lokala; Manas, Gaur; Amit, Sheth; Sheth, Tutorial: NEURo-symbolic AI fOr meNtal hEalthcare , August 2022,
  3. Roy, Kaushik; Gaur, Manas; Zhang, Qi; Sheth, A. Knowledge-infused Reinforcement Learning, the Knowledge Graph Conference (KGC 20220), May 2022.
  4. Gaur, Manas and Roy, Kaushik. (2022). Tutorial: Knowledge Infused Reinforcement Learning for Social Good Applications. International Semantic Intelligence Conference, May 2020. 17-19.

Prior Relevant Publications on KiL and related topics

  1. Roy, Kaushik, et al. "Knowledge-Infused Self Attention Transformers." KDD Workshop on Knowledge Augmented Methods for NLP (2023). (Link:
  2. Roy, Kaushik, et al. "Process Knowledge-infused Learning for Clinician-friendly Explanations." AAAI Second Symposium on Human Partnership with Medical Artificial Intelligence (2023). (Link:
  3. Sheth, Amit, Kaushik Roy, and Manas Gaur. "Neurosymbolic Artificial Intelligence (Why, What, and How)." IEEE Intelligent Systems 38.3 (2023): 56-62. (Link:
  4. Roy, K., Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023). Demo Alleviate: Demonstrating artificial intelligence enabled virtual assistance for telehealth: The mental health case. Thirty-Seventh AAAI Conference on Artificial Intelligence. (Link:
  5. Roy, K., Gaur, M., Rawte, V., Kalyan, A., & Sheth, A. (2023). ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance., Frontiers Big Data, 09 January 2023 (Link :
  6. Roy, Kaushik, and Vipula Rawte. "TDLR: Top Semantic-Down Syntactic Language Representation." NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives on Attention. 2022. (Link:
  7. Jaimini, Utkarshani, Tongtao Zhang, Georgia Olympia Brikis, and Amit Sheth. "iMetaverseKG: Industrial Metaverse Knowledge Graph to Promote Interoperability in Design and Engineering Applications." IEEE Internet Computing 26, no. 6 (2022): 59-67. (Link:
  8. Keyur Faldu, Amit Sheth, Prashant Kikani, Hemang Akbari. KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding. arXiv:2104.08145 [cs.CL]
  9. Gaur, M., Kursuncu, U., Alambo, A., Sheth, A., Daniulaityte, R., Thirunarayan, K., & Pathak, J. (2018). “Let Me Tell You About Your Mental Health!”: Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 753–762.
  10. Sheth, A., Yip, H. Y., & Shekarpour, S. (2019). Extending Patient-Chatbot Experience with Internet-of-Things and Background Knowledge: Case Studies with Healthcare Applications. IEEE Intelligent Systems, 34(4), 24–30.
  11. Kadariya, D., Venkataramanan, R., Yip, H. Y., Kalra, M., Thirunarayanan, K., & Sheth, A. (2019). kBot: Knowledge-Enabled Personalized Chatbot for Asthma Self-Management. 2019 IEEE International Conference on Smart Computing (SMARTCOMP), 138–143.
  12. Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., … Pathak, J. (2019). Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention. The World Wide Web Conference, 514–525.
  13. Bhatt, S., Padhee, S., Sheth, A., Chen, K., Shalin, V., Doran, D., & Minnery, B. (2019). Knowledge Graph Enhanced Community Detection and Characterization. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 51–59.
  14. Alambo, A., Gaur, M., Lokala, U., Kursuncu, U., Thirunarayan, K., Gyrard, A., Sheth, A., Welton, R. S., Pathak, J. (2019). Question Answering for Suicide Risk Assessment Using Reddit. 2019 IEEE 13th International Conference on Semantic Computing (ICSC), 468-473. Retrieved from
  15. Yadav, S., Pallagani, V., & Sheth, A. (2020). Medical Knowledge-enriched Textual Entailment Framework. ArXiv:2011.05257 [Cs].
  16. Wickramarachchi, R., Henson, C., & Sheth, A. (2020). An evaluation of knowledge graph embeddings for autonomous driving data: Experience and practice. Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020)
  17. Purohit, H., Shalin, V. L., Sheth, A. P., & Sheth, A. (2020). Knowledge graphs to empower humanity-inspired AI systems. IEEE Internet Computing, 24(4), 48–54.
  18. Kursuncu, U., Gaur, M., & Sheth, A. (2020). Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning. Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE).
  19. Gaur, M., Desai, A., Faldu, Keyur, & Sheth, A. (2020). Explainable AI using knowledge graphs. ACM CoDS-COMAD Conference.
  20. Yadav, S., Lokala, U., Daniulaityte, R., Thirunarayan, K., Lamy, F., & Sheth, A. (2021). “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware attention framework for relationship extraction. PLOS ONE, 16(3), e0248299.
  21. Xia, K., Saidy, C., Kirkpatrick, M., Anumbe, N., Sheth, A., & Harik, R. (2021). Towards semantic integration of machine vision systems to aid manufacturing event understanding. Sensors, 21(13), 4276.
  22. Wickramarachchi, R., Henson, C., & Sheth, A. (2021). Knowledge-infused Learning for Entity Prediction in Driving Scenes. Frontiers in Big Data, 4.
  23. Sheth, A., & Thirunarayan, K. (2021). The duality of data and knowledge across the three waves of AI. IT Professional, 23(3), 35–45.
  24. Sheth, A., Shalin, V. L., & Kursuncu, U. (2021, April). Defining and detecting toxicity on social media: Context and knowledge are key. Neurocomputing.
  25. Roy, K., Zhang, Q., Gaur, M., & Sheth, A. (2021). Knowledge infused policy gradients for adaptive pandemic control. Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021).
  26. Roy, K., Zhang, Q., Gaur, M., & Sheth, A. (2021). Knowledge infused policy gradients with upper confidence bound for relational bandits, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 35-50. Springer, Cham, 2021..
  27. Roy, K., Lokala, U., Khandelwal, V., & Sheth, A. (2021). “Is depression related to cannabis?”: A knowledge-infused model for entity and relation extraction with limited Supervision. Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021).
  28. Jaimini, U., & Sheth, A. (2021). Personalized digital phenotype score, healthcare management and intervention strategies using Knowledge enabled Digital Health Framework for pediatric asthma. In Asthma. IntechOpen.
  29. Gaur, M., Roy, K., Sharma, A., Srivastava, B., & Sheth, A. (2021, August). “Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (pp. 265-269). IEEE.
  30. Gaur, M., Faldu, K., & Sheth, A. (2021). Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable? IEEE Internet Computing, 25 (1), Jan-Feb 2021, pp. 51–59.
  31. Gaur, M., Aribandi, V., Alambo, A., Kursuncu, U., Thirunarayan, K., Beich, J., Pathak, J., & Sheth, A. (2021). Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS. PloS one, 16(5), e0250448.
  32. Dolbir, N., Dastidar, T., & Roy, K. (2021). NLP is not enough - Contextualization of user input in chatbots. KGC Workshop on Knowledge-infused Learning.
  33. Sawhney, R., Neerkaje, A., & Gaur, M. (2022, May). A Risk-Averse Mechanism for Suicidality Assessment on Social Media. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 628-635).
  34. Lokala, U., Srivastava, A., Dastidar, T. G., Chakraborty, T., Akthar, M. S., Panahiazar, M., & Sheth, A. (2022). A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework. Proceedings of the Sixteenth International AAAI Conference on Web and Social Media (ICWSM2022)640 ArXiv:2203.11856 [Cs].
  35. Gaur, M., Gunaratna, K., Sriniasan, V., & Jin, H. (2022). ISEEQ: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs. 36th AAAI Conference.
  36. Wickramarachchi, R., Henson, C., & Sheth, A. (2022). Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems, arXiv:2203.16616 [Cs].
  37. U. Jaimini and A. Sheth, "CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning," in IEEE Internet Computing, vol. 26, no. 1, pp. 43-50, 1 Jan.-Feb. 2022, doi: 10.1109/MIC.2021.3133551. [Ack: EAGER]
  38. Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Anirudh Sundarajan, Ronak Kaoshik, Amit Sheth. “Ki-Cook: Knowledge infused Multimodal Cooking Representation Learning”. Submitted for publication.


  1. Gaur, Manas; Kursuncu, Ugur; Sheth, Amit; Wickramarachchi, Ruwan; and Yadav, Shweta. "ACM HT 2020 Tutorial: Knowledge-infused Deep Learning." In Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 309-310. 2020.
  2. Gaur, M., Desai, A., Faldu, Keyur, & Sheth, A. Explainable AI using Knowledge Graphs. ACM CoDS-COMAD Conference, 2020.
  3. Gaur M., Roy, Kaushik, Zhang, Qi, Sheth Amit; Tutorial on Knowledge-infused Reinforcement Learning at Knowledge Graph Conference, Link:
  4. Gaur M., Roy, Kaushik, Sheth, Amit; Tutorial on Knowledge-infused Reinforcement Learning at International Semantic Intelligence Conference (Link:

Conceptual basis that led to KiL: (conceptually related but not specifically KiL)

  1. A. Sheth, S. Perera, S. Wijeratne, and K. Thirunarayan. 2017. Knowledge will propel machine understanding of content: extrapolating from current examples. In Proceedings of the International Conference on Web Intelligence (WI '17). ACM, New York, NY, USA, 1-9. DOI: Keynote: 2017
  2. A. Sheth, K. Thirunarayanan, The Duality of Data and Knowledge Across the Three Waves of AI, IT Professional (IEEE’s 75th anniversary special issue), May 2021.


  • NSF Award#: 2133842
  • EAGER: Advancing Neuro-symbolic AI with Deep Knowledge-infused Learning
  • Timeline: 01 July 2021 - 30 June 2022
  • Award Amount: $139,999


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