The Water Health Open Knowledge Graph

Global sustainability challenges have recently led to an increasing interest in the management of water and health resources. 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. Developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project, the WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, extreme weather events, infectious disease rates and drug distribution. Indeed, it 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. As a fully distributed system, it is sustainable over time, can handle large datasets, and allows data providers full control, establishing it as a vital European asset in the fields of water consumption and pollution.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-02-15
DOI
https://doi.org/10.1038/s41597-025-04537-4
Citations
6
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
14.89

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Water Health Open Knowledge Graph

Giorgia Lodi, Andrea Giovanni Nuzzolese, Anna Sofia Lippolis
6 citations
Scientific Data
Semantic Web and Ontologies
14.89
article

The Water Health Open Knowledge Graph

Giorgia Lodi, Andrea Giovanni Nuzzolese, Anna Sofia Lippolis
article en
6 citations

Abstract

Global sustainability challenges have recently led to an increasing interest in the management of water and health resources. 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. Developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project, the WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, extreme weather events, infectious disease rates and drug distribution. Indeed, it 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. As a fully distributed system, it is sustainable over time, can handle large datasets, and allows data providers full control, establishing it as a vital European asset in the fields of water consumption and pollution.

Scientific DataVol. 12(1)
Institute of Cognitive Sciences and Technologies (IT), University of Bologna (IT)
European Commission, Connecting Europe Facility, Innovation and Networks Executive Agency
Openalex Percentile: Top 1%
Semantic Web and Ontologies
14.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Water Health Open Knowledge Graph — Giorgia Lodi, Andrea Giovanni Nuzzolese, et al. · Scientific Data (2025) | TGRS Research Map | TGRS