The Water Health Open Knowledge Graph
- URL: http://arxiv.org/abs/2305.11051v1
- Date: Thu, 18 May 2023 15:43:00 GMT
- Title: The Water Health Open Knowledge Graph
- Authors: Gianluca Carletti, Elio Giulianelli, Anna Sofia Lippolis, Giorgia
Lodi, Andrea Giovanni Nuzzolese, Marco Picone, Giulio Settanta
- Abstract summary: WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, infectious disease rates and drug distribution.
The WHOW-KG is developed in the context of the EU-funded WHOW (Water Health Knowledge Open) project.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, an increasing interest in the management of water and health
resources has been recorded. This interest is fed by the global sustainability
challenges posed to the humanity that have water scarcity and quality at their
core. Thus, the availability of effective, meaningful and open data is crucial
to address those issues in the broader context of the Sustainable Development
Goals of clean water and sanitation as targeted by the United Nations. In this
paper, we present the Water Health Open Knowledge Graph (WHOW-KG) along with
its design methodology and analysis on impact. WHOW-KG is a semantic knowledge
graph that models data on water consumption, pollution, infectious disease
rates and drug distribution. The WHOW-KG is developed in the context of the
EU-funded WHOW (Water Health Open Knowledge) project and aims at supporting a
wide range of applications: from knowledge discovery to decision-making, making
it a valuable resource for researchers, policymakers, and practitioners in the
water and health domains. The WHOW-KG consists of a network of five ontologies
and related linked open data, modelled according to those ontologies.
Related papers
- GLEN-Bench: A Graph-Language based Benchmark for Nutritional Health [48.94971812317643]
We introduce GLEN-Bench, the first comprehensive graph-language based benchmark for nutritional health assessment.<n>GLEN-Bench includes three linked tasks: risk detection identifies at-risk individuals from dietary and socioeconomic patterns; recommendation suggests personalized foods that meet clinical needs within resource constraints.<n>Our analysis identifies clear dietary patterns linked to health risks, providing insights that can guide practical interventions.
arXiv Detail & Related papers (2026-01-26T03:32:46Z) - An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News [1.9410699081570852]
We use an ensemble approach to extract actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs)<n>DONs is a collection of regular reports on global outbreaks curated by the WHO and the adopted decision-making processes to respond to them.<n>We provide an overview of this new dataset and describe the structure of eKG, along with the services and tools used to access and utilize the data.
arXiv Detail & Related papers (2025-09-02T12:34:31Z) - AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
arXiv Detail & Related papers (2025-07-29T17:59:48Z) - Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model.
Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics.
This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
arXiv Detail & Related papers (2025-03-13T01:50:50Z) - Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare [47.23120247002356]
Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored.
This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG.
arXiv Detail & Related papers (2024-11-29T20:35:01Z) - The KnowWhereGraph Ontology [10.600781045791642]
KnowWhereGraph is one of the largest publicly available geospatial knowledge graphs.
It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers.
These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains.
arXiv Detail & Related papers (2024-10-17T18:18:41Z) - EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems [103.91826112815384]
citation-based QA systems are suffering from two shortcomings.
They usually rely only on web as a source of extracted knowledge and adding other external knowledge sources can hamper the efficiency of the system.
We propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system.
arXiv Detail & Related papers (2024-06-14T19:40:38Z) - Navigating Healthcare Insights: A Birds Eye View of Explainability with
Knowledge Graphs [0.0]
Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research.
This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models.
We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare.
arXiv Detail & Related papers (2023-09-28T16:57:03Z) - A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises [52.31710895034573]
This work provides the first comprehensive review of healthcare knowledge graphs (HKGs)
It summarizes the pipeline and key techniques for HKG construction, as well as the common utilization approaches.
At the application level, we delve into the successful integration of HKGs across various health domains.
arXiv Detail & Related papers (2023-06-07T21:51:56Z) - A Graph-Based Modeling Framework for Tracing Hydrological Pollutant
Transport in Surface Waters [0.0]
We present a graph modeling framework for understanding pollutant transport and fate across waterbodies, rivers, and watersheds.
The graph representation provides an intuitive approach for capturing connectivity and for identifying upstream pollutant sources.
Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices.
arXiv Detail & Related papers (2023-02-10T00:30:38Z) - EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations.
Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
arXiv Detail & Related papers (2022-08-01T18:47:03Z) - What is Event Knowledge Graph: A Survey [46.56390787391834]
This paper provides a comprehensive survey of Event KG (EKG) from history, ontology, instance, and application views.
EKG plays an increasingly important role in many machine learning and artificial intelligence applications, such as intelligent search, question-answering, recommendation, and text generation.
arXiv Detail & Related papers (2021-12-31T03:42:55Z) - Applying Personal Knowledge Graphs to Health [2.294014185517203]
Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems.
A range of challenges surrounding the collection, linkage, and maintenance of personal health knowledge remains to be addressed to fully realize PHKGs.
arXiv Detail & Related papers (2021-04-15T16:44:27Z) - Health Status Prediction with Local-Global Heterogeneous Behavior Graph [69.99431339130105]
Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors.
We propose to model the behavior-related multi-source data streams with a local-global graph.
We take experiments on StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.
arXiv Detail & Related papers (2021-03-23T11:10:04Z) - A Comprehensive Review of Deep Learning Applications in Hydrology and
Water Resources [0.0]
The global volume of digital data is expected to reach 175 zettabytes by 2025.
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry.
arXiv Detail & Related papers (2020-06-17T16:57:17Z) - A Data Scientist's Guide to Streamflow Prediction [55.22219308265945]
We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow.
This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
arXiv Detail & Related papers (2020-06-05T08:04:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.