scholarly journals Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ting Liu ◽  
Xueli Pan ◽  
Xu Wang ◽  
K. Anton Feenstra ◽  
Jaap Heringa ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Gut Microbiota ◽  
Research Effort ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Test Cases ◽  
Relevant Research ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
The Relationship

AbstractGut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

scholarly journals Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

2018 ◽  
Author(s):  
Muhao Chen ◽  
Yingtao Tian ◽  
Kai-Wei Chang ◽  
Steven Skiena ◽  
Carlo Zaniolo
Keyword(s):  
Experimental Results ◽  
Knowledge Graph ◽  
Semantic Representations ◽  
Alignment Task ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Low Coverage

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

scholarly journals Summary and Evaluation of the Application of Knowledge Graphs in Education 2007–2020

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yanmei Mao
Keyword(s):  
Research Tool ◽  
Knowledge Graph ◽  
Time Zone ◽  
Relevant Research ◽  
The Past ◽  
Visualization Software ◽  
Data Source ◽  
Knowledge Graphs ◽  
Social Sciences Citation Index ◽  
Author Cocitation

Since 2007, knowledge graphs, an important research tool, have been applied to education and many other disciplines. This paper firstly overviews the application of knowledge graphs in education and then samples the knowledge graph applications in CSSCI- (Chinese Social Sciences Citation Index-) indexed journals in the past two years. These samples were classified and analyzed in terms of research institute, data source, visualization software, and analysis perspective. Next, the situation of knowledge graph applications in education was summarized and evaluated in detail. Furthermore, the authors discussed and assessed the normalization of knowledge graph applications in education. The results show that in the past 15 years, knowledge graphs have been widely used in education. The academia has reached a consensus on the paradigm of the research tool: examining the hotspots, topics, and trends in the related fields from the angles of keyword cooccurrence network (KCN), time zone map, clustering network, and literature/author cocitation, with the aid of CiteSpace and other visualization software and text analysis. However, there is not yet a thorough understanding of the limitations of the visualization software. The relevant research should be improved in terms of scientific level, normalization level, and quality.

scholarly journals RDFFrames: knowledge graph access for machine learning tools

2021 ◽  
Author(s):  
Aisha Mohamed ◽  
Ghadeer Abuoda ◽  
Abdurrahman Ghanem ◽  
Zoi Kaoudi ◽  
Ashraf Aboulnaga
Keyword(s):  
Machine Learning ◽  
Database Systems ◽  
Database System ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Learning Tools ◽  
Tabular Format ◽  
Knowledge Graphs ◽  
Rdf Database ◽  
Learning Software

AbstractKnowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

scholarly journals I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs

2019 ◽  
Vol 33 ◽  
pp. 8303-8311 ◽  
Author(s):  
Junyu Gao ◽  
Tianzhu Zhang ◽  
Changsheng Xu
Keyword(s):  
Action Recognition ◽  
Visual Cues ◽  
Feature Space ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Semantic Embedding ◽  
Structured Knowledge ◽  
Knowledge Graphs

Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework.

scholarly journals Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

2021 ◽  
Vol 04 (11) ◽  
Author(s):  
Yacouba Conde ◽  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Knowledge Graph ◽  
Machine Learning Technique ◽  
Negative Attitudes ◽  
Learning Technique ◽  
Structured Knowledge ◽  
Knowledge Graphs ◽  
N Gram ◽  
Objects Detection

In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embeddings n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

The relationship between brain structure and general psychopathology in preadolescents

2020 ◽  
Author(s):  
Louise Mewton ◽  
Briana Lees ◽  
Lindsay Squeglia ◽  
Miriam K. Forbes ◽  
Matthew Sunderland ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Surface Area ◽  
Brain Structure ◽  
Parental Education ◽  
Mental Illnesses ◽  
General Psychopathology ◽  
Control Approach ◽  
Cortical Volume ◽  
Subcortical Regions ◽  
The Relationship

Categorical mental disorders are being recognized as suboptimal targets in clinical neuroscience due to poor reliability as well as high rates of heterogeneity within, and comorbidity between, mental disorders. As an alternative to the case-control approach, recent studies have focused on the relationship between neurobiology and latent dimensions of psychopathology. The current study aimed to investigate the relationship between brain structure and psychopathology in the critical preadolescent period when psychopathology is emerging. This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study® (n = 11,721; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area. Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, and parental education. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There was evidence of a relationship between externalizing psychopathology and frontal regions of the cortex that was independent of general psychopathology. The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. The widespread and pervasive relationships between general psychopathology and brain structure may reflect cognitive dysfunction that is a feature across a range of mental illnesses.

The Food-gut Human Axis: The Effects of Diet on Gut Microbiota and Metabolome

2019 ◽  
Vol 26 (19) ◽  
pp. 3567-3583 ◽  
Author(s):  
Maria De Angelis ◽  
Gabriella Garruti ◽  
Fabio Minervini ◽  
Leonilde Bonfrate ◽  
Piero Portincasa ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Human Health ◽  
Dietary Habits ◽  
Short Chain Fatty Acids ◽  
Human Organism ◽  
Immune Functions ◽  
Intestinal Microbes ◽  
Dietary Components ◽  
Dietary Treatments ◽  
The Relationship

Gut microbiota, the largest symbiont community hosted in human organism, is emerging as a pivotal player in the relationship between dietary habits and health. Oral and, especially, intestinal microbes metabolize dietary components, affecting human health by producing harmful or beneficial metabolites, which are involved in the incidence and progression of several intestinal related and non-related diseases. Habitual diet (Western, Agrarian and Mediterranean omnivore diets, vegetarian, vegan and gluten-free diets) drives the composition of the gut microbiota and metabolome. Within the dietary components, polymers (mainly fibers, proteins, fat and polyphenols) that are not hydrolyzed by human enzymes seem to be the main leads of the metabolic pathways of gut microbiota, which in turn directly influence the human metabolome. Specific relationships between diet and microbes, microbes and metabolites, microbes and immune functions and microbes and/or their metabolites and some human diseases are being established. Dietary treatments with fibers are the most effective to benefit the metabolome profile, by improving the synthesis of short chain fatty acids and decreasing the level of molecules, such as p-cresyl sulfate, indoxyl sulfate and trimethylamine N-oxide, involved in disease state. Based on the axis diet-microbiota-health, this review aims at describing the most recent knowledge oriented towards a profitable use of diet to provide benefits to human health, both directly and indirectly, through the activity of gut microbiota.

Attention

2017 ◽  
Author(s):  
Nicolas Bommarito
Keyword(s):  
Mental Disorders ◽  
Attention Deficit ◽  
Attention Deficit Disorder ◽  
Moral Character ◽  
Moral Concern ◽  
Moral Value ◽  
The Relationship

After a brief overview of the nature of attention, I argue that attention (and inattention) can be morally virtuous or vicious independently of associated overt actions. This is not, as others have claimed, because attention itself has moral value, but because attention can manifest underlying moral concern. After discussing the relationship between attention and concern, I discuss problematic cases related to mental disorders, in particular attention-deficit disorder and scrupulosity. I then apply the account to particular virtues associated with attention: modesty and gratitude. Gratitude, I argue, involves attention to our benefits and their sources, while modesty involves special patterns of attention away from our own good qualities. This account best explains how attention can be relevant to moral character.

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