Towards generalizable neuro-symbolic reasoners

Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedSymbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of trainable artificial neural networks. Over the course of this dissertation, we provide a brief summary of our recent efforts to bridge the neural and symbolic divide in the context of deep deductive reasoners. More specifically, We designed a novel way of conducting neuro-symbolic through pointing to the input elements. More importantly we showed that the proposed approach is generalizable across new domain and vocabulary demonstrating symbol-invariant zero-shot reasoning capability. Furthermore, We have demonstrated that a deep learning architecture based on memory networks and pre-embedding normalization is capable of learning how to perform deductive reason over previously unseen RDF KGs with high accuracy. We are applying these models on Resource Description Framework (RDF), first-order logic, and the description logic EL+ respectively. Throughout this dissertation we will discuss strengths and limitations of these models particularly in term of accuracy, scalability, transferability, and generalizabiliy. Based on our experimental results, pointer networks perform remarkably well across multiple reasoning tasks while outperforming the previously reported state of the art by a significant margin. We observe that the Pointer Networks preserve their performance even when challenged with knowledge graphs of the domain/vocabulary it has never encountered before. To our knowledge, this work is the first attempt to reveal the impressive power of pointer networks for conducting deductive reasoning. Similarly, we show that memory networks can be trained to perform deductive RDFS reasoning with high precision and recall. The trained memory network's capabilities in fact transfer to previously unseen knowledge bases. Finally will talk about possible modifications to enhance desirable capabilities. Altogether, these research topics, resulted in a methodology for symbol-invariant neuro-symbolic reasoning

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

Comparison of the Effects of Cold Compress and Xyla-P Cream on Stress Caused by Venipuncture among Hemodialysis Patients

Background: Stress caused by the insertion of the needle into the arteriovenous fistula is one of the main concerns of hemodialysis patients. Reducing the stress of patients during venipuncture is one of the main goals of nursing care.This study aimed to investigate and compare the effects of Xyla-P cream and cold compress on the severity of stress caused by venipuncture in hemodialysis patients.Methods: This clinical trial was conducted in 50 patients undergoing hemodialysis who were enrolled in the study using simple random sampling.The severity of stress was measured during two successivehemodialysis sessions in three stages including after the application of a placebo, Xyla-P cream, and cold compress. The visual analog scale was used to measure the severity of stress. The data collector and data analyzer were blinded. The collected data were analyzed using analysis of variance with repeated measures.Results: The stress scores were significantly different between the placebo group (6.69±1.66)and Xyla-P cream group (5.43±1.42)(P=0.000) and cold compress group (5.05±1.40)(P=0.000), and between Xyla-P cream group and cold compress group (P=0.026). Conclusions: Cold compress is more effective than Xyla-P cream in reducing the stress. Therefore, nurses are recommended to use this method, instead of medications, for reducing the stress.Conclusions: Cold compress is more effective than Xyla-P cream in reducing the stress. Therefore, nurses are recommended to use this method, instead of medications, for reducing the stress

Comparison of the Effects of Cold Compress and Xyla-P Cream on Stress Caused by Venipuncture among Hemodialysis Patients

Background: Stress caused by the insertion of the needle into the arteriovenous fistula is one of the main concerns of hemodialysis patients. Reducing the stress of patients during venipuncture is one of the main goals of nursing care.This study aimed to investigate and compare the effects of Xyla-P cream and cold compress on the severity of stress caused by venipuncture in hemodialysis patients.Methods: This clinical trial was conducted in 50 patients undergoing hemodialysis who were enrolled in the study using simple random sampling.The severity of stress was measured during two successivehemodialysis sessions in three stages including after the application of a placebo, Xyla-P cream, and cold compress. The visual analog scale was used to measure the severity of stress. The data collector and data analyzer were blinded. The collected data were analyzed using analysis of variance with repeated measures.Results: The stress scores were significantly different between the placebo group (6.69±1.66)and Xyla-P cream group (5.43±1.42)(P=0.000) and cold compress group (5.05±1.40)(P=0.000), and between Xyla-P cream group and cold compress group (P=0.026). Conclusions: Cold compress is more effective than Xyla-P cream in reducing the stress. Therefore, nurses are recommended to use this method, instead of medications, for reducing the stress.Conclusions: Cold compress is more effective than Xyla-P cream in reducing the stress. Therefore, nurses are recommended to use this method, instead of medications, for reducing the stress

Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment

A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We propose a neural-symbolic system to address this limitation in this paper. It is designed as a differentiable end-to-end deep memory network that learns over abstract, generic symbols to discover entailment patterns common to any reasoning task. A key component of the system is a simple but highly effective normalization process for continuous representation learning of KG entities within memory networks. Our results show how the model, trained over a set of KGs, can effectively entail facts from KGs excluded from the training, even when the vocabulary or the domain of test KGs is completely different from the training KGs