Heterogeneous Knowledge Research Articles

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

MGMASR: Multi-Graph and Multi-Aspect Neural Network for Service Recommendation in Internet of Services

With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.

Explanatory Research Knowledge Sharing and Knowledge heterogeneity on Higher Education Business performance through Learning Value Sharing


 
 
 
 
 
 
 
 
 
 This study investigates the optimization of Knowledge Heterogeneity among practitioners in higher education through the dissemination of diverse expertise via the Practitioner-Led Teaching Program. The research, conducted within the context of the Kampus Merdeka initiative, employs a quantitative approach grounded in the positivist paradigm, complemented by structured interviews with practitioners to bolster questionnaire item indicators. Data was collected using saturated sampling from a population of 30 educators spanning 29 programs at a university. Employing SEM PLS analysis, the findings underscore that the sharing of knowledge and practitioner expertise significantly heightens academic performance, as manifested by an 87% enhancement in shared value across learning activities. This research underscores the pivotal role of practitioner engagement in enriching educational quality and aligning curricula with industry demands, thereby fostering valuable implications for educational institutions worldwide.
 Highlights: 
 
 
 
 
 
 
 
 
 
 
 
 Optimizing Knowledge Diversity: Investigating the impact of diverse practitioner expertise on higher education outcomes.
 Practitioner-Led Engagement: Emphasizing the role of practitioners in enriching academic quality and relevance.
 Enhanced Academic Performance: Demonstrating an 87% increase in shared value through knowledge exchange, contributing to improved learning activities and educational outcomes.
 
 
 
 
 
 
 
 
 
 
 
 Keywords: Knowledge Heterogeneity, Practitioner-Led Teaching, Value Sharing, Higher Education, Academic Performance.
 
 
 
 
 
 
 
 
 

A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks

Owing to the heterogeneity and incomplete information present in various domain knowledge graphs, the alignment of distinct source entities that represent an identical real-world entity becomes imperative. Existing methods focus on cross-lingual knowledge graph alignment, and assume that the entities of knowledge graphs in the same language are unique. However, due to the ambiguity of language, heterogeneous knowledge graphs in the same language are often duplicated, and relationship triples are far less than those of cross-lingual knowledge graphs. Moreover, existing methods rarely exclude noisy entities in the process of alignment. These make it impossible for existing methods to deal effectively with the entity alignment of domain knowledge graphs. In order to address these issues, we propose a novel entity alignment approach based on domain-oriented embedded representation (DomainEA). Firstly, a filtering mechanism employs the language model to extract the semantic features of entities and to exclude noisy entities for each entity. Secondly, a Structural Aggregator (SA) incorporates multiple hidden layers to generate high-order neighborhood-aware embeddings of entities that have few relationship connections. An Attribute Aggregator (AA) introduces self-attention to dynamically calculate weights that represent the importance of the attribute values of the entities. Finally, the approach calculates a transformation matrix to map the embeddings of distinct domain knowledge graphs onto a unified space, and matches entities via the joint embeddings of the SA and AA. Compared to six state-of-the-art methods, our experimental results on multiple food datasets show the following: (i) Our approach achieves an average improvement of 6.9% on MRR. (ii) The size of the dataset has a subtle influence on our approach; there is a positive correlation between the expansion of the dataset size and an improvement in most of the metrics. (iii) We can achieve a significant improvement in the level of recall by employing a filtering mechanism that is limited to the top-100 nearest entities as the candidate pairs.

An Overall View of the Functional and Structural Characterization of Glucose-6-Phosphate Dehydrogenase Variants in the Mexican Population.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, affecting an estimated 500 million people worldwide, is a genetic disorder that causes human enzymopathies. Biochemical and genetic studies have identified several variants that produce different ranges of phenotypes; thus, depending on its severity, this enzymopathy is classified from the mildest (Class IV) to the most severe (Class I). Therefore, understanding the correlation between the mutation sites of G6PD and the resulting phenotype greatly enhances the current knowledge of enzymopathies' phenotypic and genotypic heterogeneity, which will assist both clinical diagnoses and personalized treatments for patients with G6PD deficiency. In this review, we analyzed and compared the structural and functional data from 21 characterized G6PD variants found in the Mexican population that we previously characterized. In order to contribute to the knowledge regarding the function and structure of the variants associated with G6PD deficiency, this review aimed to determine the molecular basis of G6PD and identify how these mutations could impact the structure, stability, and function of the enzyme and its relation with the clinical manifestations of this disease.

Characterizing basin-scale subsurface hydraulic heterogeneity with multiscale geological and hydrological measurements

Mapping aquifer heterogeneity is a pressing need for managing regional groundwater resources. Many studies have shown that combining hydraulic tomographic surveys with point-scale geological information is viable for detailing meter-scale to kilometer-scale hydraulic heterogeneity of aquifers. However, due to the complexity of aquifer systems, few field investigations have been conducted on basin-scale systems. In this study, we collected datasets of stream stages, groundwater levels, and borehole logs from 35 wells in a river plain during the wet season, when flood events altered the groundwater flow fields. We then analyzed these datasets using correlation analysis and a geostatistical inverse model. The hydraulic transmissivity and storage coefficient distributions were inverted based on different numbers of geological zones. By validating the estimated parameter fields with independent datasets, we demonstrated that appropriate selection of the number of geological zones, using point-scale geological information and clustering, improved the estimated fields. Further increasing the number of zones beyond an appropriate number deteriorated the estimates at the basin-scale site. The proposed method offers a cost-effective approach for basin-scale characterization. The better knowledge of subsurface hydraulic heterogeneity can support water resource management decisions.

Around the GLOBE: Numerical Aggregation Question-answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks

One of the key AI tools for textual corpora exploration is natural language question-answering (QA). Unlike keyword-based search engines, QA algorithms receive and process natural language questions and produce precise answers to these questions, rather than long lists of documents that need to be manually scanned by the users. State-of-the-art QA algorithms based on DNNs were successfully employed in various domains. However, QA in the genealogical domain is still underexplored, and researchers in this field (and other fields in humanities and social sciences) can highly benefit from the ability to ask questions in natural language, receive concrete answers, and gain insights hidden within large corpora. While some research has been recently conducted for factual QA in the genealogical domain, to the best of our knowledge, there is no previous research on the more challenging task of numerical aggregation QA (i.e., answering questions combining aggregation functions, e.g., count, average, max). Numerical aggregation QA is critical for distant reading and analysis for researchers (and the general public) interested in investigating cultural heritage domains. Therefore, in this study, we present a new end-to-end methodology for numerical aggregation QA for genealogical trees that includes (1) an automatic method for training dataset generation, (2) a transformer-based table selection method, and (3) an optimized transformer-based numerical aggregation QA model. The findings indicate that the proposed architecture, GLOBE, outperforms the state-of-the-art models and pipelines by achieving 87% accuracy for this task compared to only 21% by current state-of-the-art models. This study may have practical implications for genealogical information centers and museums, making genealogical data research easy and scalable for experts as well as the general public.

Mobilizing mestizo Knowledges: The Changing Role of Cuban Universities Articulating Diverse Planning Knowledges, 1959–2021

Urban processes are shaped by heterogeneous knowledges produced by multiple rationalities. Acknowledging this diversity, this article asks: What is the role of universities mobilizing diverse knowledge and their articulation with planning processes? It reflects on the role of Cuban universities in translating diverse and situated knowledge. Building upon debates on epistemic justice and recognizing the nuances of the Cuban process, it provides a historical review of the changing role of universities, discussing how different knowledge paradigms have governed their urban planning work. Pondering this history, it concludes by discussing current challenges of universities to advance toward more mestizo urban knowledges.

Organizational knowledge heterogeneity: Between conceptual nuances and the quest for a measure

AbstractThis paper is a survey of relevant empirical literature pertaining to knowledge heterogeneity. Its purpose focuses on highlighting issues surrounding its definition and measurement. First, it shows that knowledge heterogeneity is definitionally close to several other concepts, each measured in a certain way. Second, the same measures have been used indiscriminately for several of these concepts. Third, different measures have been used for the same concepts. The point being made is that comparable empirical results cannot be achieved if the same measures are used for different concepts. Research in knowledge management and innovation management centered around knowledge heterogeneity could be impeded if different emphases are put on different words while studying a concept that is defined similarly across studies yet measured differently, or that is measured similarly despite being defined differently. Results in future research are more likely to converge if knowledge heterogeneity is defined separately from similar concepts such as knowledge sharing and knowledge overlap, for example.

Дунайская прародина славян в «Повести временных лет»: проблема интерпретации

Introduction. The article discusses the image of the ancestral homeland of the Slavs as described in the early 12th century Old-Russian chronicle known as the “Tale of Bygone Years.” Methods and materials. The focus of the article is on the concept of the Danube Slavic land, which, according to the chronicle, was the ancient homeland of the Slavs. In order to elucidate this concept, the author uses elements of historical-semantic analysis of the text and compares its information with data from other sources relevant for the study of this topic. Analysis. Taking into account the heterogeneity of knowledge underlying the chronicle ethnogenetic construction, the author identifies various elements of this knowledge. Results. The author comes to the conclusion that the image of the Danube homeland of the Slavs was not a bookish construction but a reflection of the ethnic realities of the early Middle Ages, when the Carpathian basin was the core area of the Slavic ethnic identity. According to the author, the formation of the idea that the Middle Danube Slavic land was the primary homeland of the Slavs was also the result of the perception of the Slavs as autochthonous inhabitants of Illyricum and Pannonia, which existed in Rome in the time of the Pannonian diocese of Methodius and probably influenced the formation of Slavic ethnogenetic legends through Cyril’s and Methodius’s cultural and ideological legacy.

KGTN: Knowledge Graph Transformer Network for explainable multi-category item recommendation

Most existing methods for recommendation assume that all the items are of the same category. However, single-category recommendations are no longer sufficient to fulfill the diverse needs of users in the real world. Existing methods cannot be directly applied to recommend multiple categories of items due to the different properties and complex relationships among them. However, a drawback of these approaches is their assumption of a fixed and homogeneous data format, making them incapable of addressing the challenge of recommending multiple categories of items. Recommendation of multiple categories of items to users concurrently has become a challenging task. To tackle this problem, we design a novel method Knowledge Graph Transformer Network (KGTN) for explainable multi-category item recommendation, inspired by advances in knowledge graph in the field of recommendation. Knowledge graph and neural network methods have shown advantages in addressing recommendation problems in heterogeneous graph structures. This is because different categories of items have unique attributes and dimensions, which can be effectively represented and integrated by incorporating knowledge graph and graph neural network techniques. That is, our approach can handle heterogeneous collaborative knowledge graphs composed of users and items, and can mine hidden path relationships without defining original paths. In addition, its algorithmic process has interpretability. Specifically, graph transformer layer converts the heterogeneous input graph into a useful meta-path graph for recommended task; graph convolution layer learns the node representation on the new meta-path graph. Finally, we calculate the inner product of user and item vector representations to output the probability for the recommendation. At the same time, we use the critical path in the learned useful meta-path graph as the explanation. Comprehensive experiments on three datasets demonstrate that KGTN achieves state-of-the-art performance over existing baselines.

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

With the rapid development of internet plus education, it is increasingly important to quickly and accurately match personalized videos for learners from massive learning videos. However, the existing resource recommendation methods have two problems. On the one hand, they do not make full use of the implicit interaction data of learners in the learning process, i.e., they rarely reflect the comprehensive consideration of learners' interest preferences and cognitive level when describing learners. On the other hand, they are mostly based on collaborative filtering algorithms, using the similarity between learners or videos to recommend, ignoring the impact of semantic relations between videos on the recommendation results. In view of this, we proposed a personalized learning video matching method (IFT‐PTransE) based on heterogeneous feature data transfer and knowledge reasoning from two aspects of learning behavior data mining and knowledge graph representation learning. In this method, firstly, a learner model is constructed. By analyzing and quantifying various implicit interaction behavior data of learners, the sparse video scoring matrix is filled as auxiliary data to complete the transfer of target scoring data. Secondly, the semantic close relationship between videos is introduced. PTransE algorithm is used to mine the multi‐path relationship between entities. All entity relationships are embedded into the low dimensional vector space, so that the semantic similarity between videos can be calculated using the distance between vectors. Finally, video score similarity and semantic similarity between frequencies are fused. video sorting is performed based on the improved collaborative filtering algorithm and then recommend the top N videos to the students. Through simulation and analysis, the effectiveness of this method in personalized video matching is proved. This method makes up for the shortcomings of collaborative filtering algorithm in using implicit information, enhances the recommendation effect at the semantic level, and solves the data sparse and cold start problems to a certain extent. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Microfluidic Liquid Biopsy Platform to Monitor Protein Biomarker Heterogeneity in Single Circulating Therapy‐Resistance Cancer Cell

AbstractTumor cells display heterogenous molecular signatures during the course of cancer and create distinct tumor cell subpopulations which challenge effective therapeutic decisions. Detection and monitoring of these heterogenous molecular events at single cell level are imperative to identify tumor cell subpopulations and to engage the best therapeutic options for the individual patient. Herein, a microfluidic liquid biopsy platform to analyze circulating tumor cells (CTCs) at single cell level is reported. The individual CTCs are captured in an alternating current‐induced microfluidic platform and analyzed by using surface‐enhanced Raman scattering spectroscopy. This platform selectively captures single CTCs from the patient's peripheral blood mononuclear cells. Using cell line models and patient samples, it is shown that the assay can simultaneously detect multiple protein biomarkers on a single CTC. The platform can stratify the CTCs into different subpopulations based on their cancer‐associated protein signature changes in response to drug treatment. This enables the identification of CTC subpopulations that are probably not responding to treatment and may assist clinicians in specifically monitoring and eliminating therapy‐resistant cancer cells within a lesion. This single CTC monitoring chip will likely have high clinical importance in disease diagnosis and treatment monitoring, and advance the knowledge of cancer heterogeneity.

Towards epistemic diversity in sustainability transitions: an exploration of hybrid socio-technical systems

A wealth of scholarly work has contributed to make visible and describe the place of indigenous peoples and knowledge in sustainability transitions. We follow suit, exploring if, and how, indigenous peoples and knowledges take part in the construction of hybrid socio-technical systems, i.e. socio-technical systems where heterogeneous knowledges already coexist and give rise to the emergence of specific and nuanced socio-technical patterns. We address the question: What are the types of interaction between techno-scientific and indigenous/local knowledges in socio-technical configurations aiming at sustainability? Our inquiry focuses on knowledge circulation patterns in hybrid socio-technical systems. Conceptually, we build on epistemic diversity and hybridisation as means to reflect on socio-technical systems. Empirically, we carry on a theory-driven literature review and ground a model on the backdrop of the MLP perspective. Results show that hybrid socio-technical systems present overlapping socio-technical assemblages coexisting in constructive tension. Such tension stems from the manyfold possible directionalities that take place in the social production of hybrid-oriented and non-hybrid-oriented performativity of knowledges. This paradoxical nature implies that each encounter performs a potential tension within the broader web of relations in the system. In terms of sustainability transitions, hybrid socio-technical systems pose particular knowledge governance challenges. Its paradoxical nature risks enabling contradictory, non-viable or illegitimate transition pathways towards sustainability.

Planning precision aquaculture activities in a changing and crowded sea

Extreme climate events are increasingly challenging the growth of the marine aquaculture sector, causing local influences on species performance and affecting production and yield - impacting where to locate cage aquaculture facilities. Here we produced scenario-based quantitative maps using modelled species-specific performance combined with predicted high-resolution future IPCC temperature scenarios. We ran a species-specific Dynamic Energy Budget mechanistic model for four model species, up to 2050, and mapped functional trait-based outcomes as: i) time to reach the commercial size, ii) feces produced and iii) uneaten food. A high spatial resolution suitability index allowed the sustainability of farming strategies for single- and multi-species to be identified across a 159.696 km2 surface extension (Italian Exclusive Economic Zone; 6% of the Mediterranean basin surface). Providing a good case study to shed light on difficult questions facing aquaculture planning around the world. Good future performance under both representative concentration pathway (RCP) scenarios were modelled for Sea bream and European seabass in inshore waters. Performance of Mediterranean mussels and Japanese oysters was found to decrease slightly when compared to the 2007–2010 time interval. Scenario-based quantitative maps represent a heterogeneous species-specific knowledge layer that is critical to better inform aquaculture management and development strategies. Yet this knowledge layer is missing from the process to develop climate-resilient risk maps and associated adaptation measures, as well as when informing stakeholders on potential site expansion and/or the establishment of nascent aquaculture industry sites.

Efficient visual fault detection for freight train braking system via heterogeneous self distillation in the wild

Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment. Although deep learning-based approaches have excelled in object detection, the efficiency of freight train fault detection is still insufficient to apply in real-world engineering. This paper proposes a heterogeneous self-distillation framework to ensure detection accuracy and speed while satisfying low resource requirements. The privileged information in the output feature knowledge can be transferred from the teacher to the student model through distillation to boost performance. We first adopt a lightweight backbone to extract features and generate a new heterogeneous knowledge neck. Such neck models positional information and long-range dependencies among channels through parallel encoding to optimize feature extraction capabilities. Then, we utilize the general distribution to obtain more credible and accurate bounding box estimates. Finally, we employ a novel loss function that makes the network easily concentrate on values near the label to improve learning efficiency. Experiments on four fault datasets reveal that our framework can achieve over 37 frames per second and maintain the highest accuracy of 98.88% in comparison with traditional distillation approaches. Moreover, compared to state-of-the-art methods, our framework demonstrates more competitive performance with lower memory usage and the smallest model size.

How do entrepreneurial activities at different levels promote business growth: a review and research agenda

PurposeThis study aims to solve the realistic dilemma between the importance of entrepreneurship and the high rate of entrepreneurial failure, and to point out the direction of subsequent research.Design/methodology/approachThe paper takes the form of a literature review.FindingsEntrepreneurial activities involve multiple dimensions. Entrepreneurs or entrepreneurial teams will be affected by multiple factors when starting a business, and sufficient attention should be paid to both the factors within the group and the factors outside the group such as institutional quality and market competition. High entrepreneurial failure rate is an essential characteristic of entrepreneurial activities, while solving this problem requires entrepreneurs to maintain passion, clarify their own motivation, improve their learning abilities and adopt appropriate entrepreneurial strategies to improve entrepreneurial performance. Meanwhile, it also urgent to build entrepreneurial teams with common goals, heterogeneous knowledge structure, outstanding learning ability, solid mutual trust, strong social influence and social capital. Successful entrepreneurship should adhere to the perspective of openness and cooperation. It should not only actively strengthen international cooperation but also fully adapt to the country’s system and culture. Sustainable growth of entrepreneurial enterprises requires not only stable commercial revenue but also responsibility to society, which in turn leads to a good reputation and high social recognition.Practical implicationsThe authors hope this review can provide some insightful viewpoints for deepening the theoretical system of entrepreneurship, improving the success rate of entrepreneurship and promoting the sustainable growth of enterprises.Originality/valueFurther research can be carried out on the promotion of business growth by entrepreneurship at the micro level in the following aspects: analyze functional mechanism between innovation and entrepreneurship; entrepreneurship research by integrating multiple institutional contexts and cultural traditions; consider the changes in emerging technologies on entrepreneurial activities; diversified mechanism between entrepreneurship education and business growth.

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

Water–Rock Interactions Driving Groundwater Composition in the Pra Basin (Ghana) Identified by Combinatorial Inverse Geochemical Modelling

The crystalline basement aquifer of the Pra Basin in Ghana is essential to the water supply systems of the region. This region is experiencing the ongoing pollution of major river networks from illegal mining activities. Water management is difficult due to the limited knowledge of hydrochemical controls on the groundwater. This study investigates its evolution based on analyses from a previous groundwater sampling campaign and mineralogical investigation of outcrops. The dominant reactions driving the average groundwater composition were identified by means of a combinatorial inverse modelling approach under the hypothesis of local thermodynamical equilibrium. The weathering of silicate minerals, including albite, anorthite, plagioclase, K-feldspar, and chalcedony, explains the observed median groundwater composition in the transition and discharge zones. Additional site-specific hypotheses were needed to match the observed composition of the main recharge area, including equilibration with carbon dioxide, kaolinite, and hematite in the soil and unsaturated zones, respectively, and the degradation of organic matter controlling the sulfate/sulfide content, thus pointing towards kinetic effects during water–rock interactions in this zone. Even though an averaged water composition was used, the inverse models can “bridge” the knowledge gap on the large basin scale to come up with quite distinct “best” mineral assemblages that explain observed field conditions. This study provides a conceptual framework of the hydrogeochemical evolution for managing groundwater resources in the Pra Basin and presents modelling techniques that can be applied to similar regions with comparable levels of heterogeneity in water chemistry and limited knowledge of aquifer mineralogy. The combinatorial inverse model approach offers enhanced flexibility by systematically generating all plausible combinations of mineral assemblages from a given pool of mineral phases, thereby allowing for a comprehensive exploration of the reactions driving the chemical evolution of the groundwater.

Hydrogeologic Constraints for Drinking Water Security in Southwest Coastal Bangladesh: Implications for Sustainable Development Goal 6.1

Transitioning from the Millennium Development Goals (MDG) to the Sustainable Development Goals (SDG) is a big challenge, particularly for SDG 6.1, as the effective delivery of drinking water services drops due to more stringent indicators, especially for water quality constraints. Salinity in groundwater has received less attention compared to arsenic and E. Coli in the MDG era, while its presence and widespread variability has considerable implications in larger coastal areas for achieving SDG 6.1 targets. This article analyzes drinking water security in southwest coastal Bangladesh, through an in-depth field investigation. It reveals that the exponential growth of groundwater-based technologies, such as tube wells, does not necessarily indicate the actual safe drinking water coverage in coastal areas, due to complex hydrogeology with the high spatial variability of groundwater salinity risks. The spatial variability of hydrogeologic constraints and groundwater salinity risks also reinforces concerns of access, reliability, and affordability with different water supply technologies. National estimates can be misleading as the presence of salinity substantially lowers the effective drinking water coverage. Infrastructural investments for drinking water supply need to consider a sound knowledge of hydrogeologic heterogeneity, and the monitoring of water quality, if the SDG 6.1 targets are to be met.