Knowledge-infused Deep Learning

Kursuncu, Ugur; Sheth, Amit P.; Wickramarachchi, Ruwan; Yadav, Shweta

doi:10.1145/3372923.3404862

Cited by 15 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The technique can also exploit the learned metric space representations to provide high-quality annotations over unlabeled data. Therefore, the com-bination of knowledge-infusion [28,29,30], pre-trained GPT-3, and supervised contrastive learning presents a very effective way to handle limited supervision. The proposed model has two modules: (1) Phrase Extraction and Matching Module, which utilizes the DAO ontology augmented with the PHQ-9, SNOMED-CT, ICD-10, MeSH Terms, and Diagnostic and Statistical Manual for Mental Disorders (DSM-5) lexicons to map the input word sequence to the entities mention in the ontology by computing the cosine similarity between the entity names (obtained from the DAO) and every n-gram token of the input sentence.…”

Section: Relationship Tweetmentioning

confidence: 99%

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

Roy,

Lokala,

Khandelwal

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators. However, preliminary scientific research does not conclusively associate cannabis with improved mental health. In this study, we explore the relationship between depression and consumption of cannabis in a targeted social media corpus involving personal use of cannabis with the intent to derive its potential mental health benefit. We use tweets that contain an association among three categories annotated by domain experts -Reason, Effect, and Addiction. The state-of-the-art Natural Langauge Processing techniques fall short in extracting these relationships between cannabis phrases and the depression indicators. We seek to address the limitation by using domain knowledge; specifically, the Drug Abuse Ontology for addiction augmented with Diagnostic and Statistical Manual of Mental Disorders lexicons for mental health. Because of the lack of annotations due to the limited availability of the domain experts' time, we use supervised contrastive learning in conjunction with GPT-3 trained on a vast corpus to achieve improved performance even with limited supervision. Experimental results show that our method can significantly extract cannabis-depression relationships better than the state-of-the-art relation extractor. High-quality annotations can be provided using a nearest neighbor approach using the learned representations that can be used by the scientific community to understand the association between cannabis and depression better.

show abstract

Section: Relationship Tweetmentioning

confidence: 99%

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

Roy,

Lokala,

Khandelwal

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For APC, during COVID-19, we have identified specific challenges that include the agent handling relational features, non-homogeneity in the feature counts, and learning non black-box interpretable structures through knowledge infusion [6]. We believe that the inability to handle these issues by previous approaches can pose bottlenecks in agent models for assisting policymakers.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Infused Policy Gradients for Adaptive Pandemic Control

Roy,

Zhang,

Gaur

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take the early steps to address these challenges using Knowledge Infused Policy Gradient (KIPG) methods. Prior work on knowledge infusion does not handle soft and hard imposition of varying forms of knowledge in disease information and guidelines to necessarily comply with. Furthermore, the models do not attend to non-homogeneity in feature counts, manifesting as partial observability in informing the policy. Additionally, interpretable structures are extracted post-learning instead of learning an interpretable model required for APC. To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner. The study establishes a theory for imposing hard and soft constraints and simulates it through experiments. In comparison with knowledge-intensive baselines, we show quick sample efficient adaptation to new knowledge and interpretability in the learned policy, especially in a pandemic context.

show abstract

“…"Empirically, an explainable system would comprise of collectively exhaustive interpretable subsystems and orchestration among them. More often than not, explanations would be in natural language explaining the decision, while interpretations can be statistical or conceptual (using either generic or domain-specific KG [14], [9]) in nature pertaining to its inner functioning. "…”

Section: Defining Explainability and Interpretabilitymentioning

confidence: 99%

Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

Faldu¹,

Sheth²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL methods using knowledge-infused learning, which is one of the strategies. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches, and illustrate it with examples from natural language processing for healthcare and education applications.

show abstract

Knowledge-infused Deep Learning

Cited by 15 publications

References 8 publications

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

Knowledge Infused Policy Gradients for Adaptive Pandemic Control

Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

Contact Info

Product

Resources

About