Uncertain Knowledge Research Articles

Discursive Patterns of Opinion-Based Knowledge in the Production of Health-Related Social Media Posts in Turkey

Online media platforms play a central role in presenting opinions, facilitating information exchange, and supporting knowledge production. However, on most social media platforms, verifying the credibility of posts and comments remains highly challenging due to their open nature and lack of a strong governance structure. At the beginning of the COVID-19 pandemic, social media platforms became tainted by uncertain knowledge, partly caused by the limited control these platforms have over user-generated content. This lack of credible information fueled a collective state of panic (Peschke, 2021). Since social media spaces largely involve individuals engaging and sharing their opinions, the credibility of knowledge production on these platforms must be questioned. Despite the proliferation of disinformation, uncertainty, and misinformation on social media platforms (Wardle & Derakhshan, 2017), opinion-based knowledge production has also emerged from these channels, which are challenging to monitor. This article analyzes the communication patterns on health of Turkish posts and comments on Facebook and Twitter during October and November 2021. The data were evaluated against opinion-based content, allowing this study to trace the dynamics of posts and comments by categorizing them as opinion, information, or knowledge production. Data interpretation is grounded in these classifications, with particular attention to the interplay between public engagement and health information. Understanding how the media-based public creates opinion-based knowledge during the uncertainty of the COVID-19 pandemic underscores the potential for collaboration between media-based publics and scientific authorities.

SCARF: Scalable Continual Learning Framework for Memory‐efficient Multiple Neural Radiance Fields

AbstractThis paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi‐layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo‐realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross‐scene weight matrix and a set of scene‐specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo‐realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state‐of‐the‐art rendering quality of continual learning NeRF on NeRF‐Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.

Making sense of clandestine graves: Material epistemology and the political geography of uncertain knowledge

How might we begin to establish meaning, understanding and justice out of something that is meant not to be found? In this article, we approach the growing problem of clandestine graves to ask what can be read from them, including why they matter, why they are found, and how they are becoming an intractable part of life for millions of citizens. Departing from the clandestine graves of São Paulo and Mexico, we argue that these material spaces are produced by ambiguous governance structures, and in turn reproduce them in ways that are unevenly knowable. The characteristics of these spaces that are otherwise shrouded in suspicion and deliberate efforts to make them unknowable reveal patterns and practices of political order while simultaneously creating certainty and fear about the governance they perpetuate. In taking the mass grave as an epistemology, we seek to establish identifiable tenents and patterns for further research and action while recognizing the challenges in asserting a knowledge claim about material spaces that are so intentionally, but unevenly, unknowable.

Model-Centric Integration of Uncertain Expert Knowledge into Importance Sampling-Based Parameter Estimation

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated.

Science for transformative change: the IPCC, boundary work and the making of useable knowledge

While there has been much discussion about what kind of expertise the IPCC needs to develop to (better) guide climate policy, little has been said about how the experts themselves assess the challenges of making science policy-relevant. The paper aims to address this gap by exploring how leading IPCC experts reflect on and evaluate their work. The empirical material consists of an interview study with experts currently or recently involved in the IPCC. The selection strategy aimed to achieve a broad range of experience among those with key roles in the assessment work, including experts from all three working groups, from different regions, and of different genders. Data from the interviews was analyzed thematically using NVivo. The concept of boundary work was used to analyze the distinctions and boundaries in this work; how the IPCC experts draw boundaries between science and policy, between policy-relevance and policy-prescriptiveness, and between certain and uncertain knowledge. By analyzing the experts’ own experiences and ideas about what makes science relevant to policy-making, the paper contributes to the discussion about current and future challenges for the IPCC.

Wilson’s bottleneck

Planetary sustainability is in trouble, heading towards what pioneer of evolutionary biology, E.O Wilson, twenty-two years ago called a “bottleneck”. Created through the actions of humanity this is an increasingly narrow passage through which only some species can pass, and on which humans depend to provide the sources of re-radiation. What is lost is hard to impossible to restore. Keeping this passage as wide as possible is crucial, but the trends are not yet promising. At a time when those trends appear to be converging to a human and ecological crisis of planetary but finite duration, changed priorities are required whilst at the same time providing opportunity. In particular, strategies, such as experimental governance devised to act in the face of unknowns and uncertain knowledge provide a basis for action to hold open and successfully pass through the bottleneck, a goal which is of the highest importance for humans as we seek to achieve a sustainable future.

To Map, To Ascertain

This paper offers a reflection on the theme of this special issue, how mapmakers deal with uncertainty, from a digital humanities perspective. For modern geohumanities scholars and digital mapmakers working in the field of history, dealing with the dual uncertainties of historical data and historical societies themselves can be a difficult task. How do they deal with these challenges? Do we use similar solutions to deal with uncertainty? Can we learn from the practices of early modern cartographers? And to what extent is (un)certainty itself a fruitful research topic in the geohumanities? To answer these questions, it is important to consider the historical development of the field of geohumanities and why it has learned to adapt to dealing with uncertain or ambiguous knowledge. Practical examples from my own research demonstrate how modern geohumanities scholars are affected by the notion of uncertainty in different ways. These examples are linked to the contributions and questions raised in this special issue. The engagement with early modern cartographers shows how important it is for geohumanities scholars not only to invest in geospatial analysis tasks, but also to become cartographers themselves.

Intuitionistic Fuzzy Sequential Three-Way Decision Model in Incomplete Information Systems

As an effective method for uncertain knowledge discovery and decision-making, the three-way decisions model has attracted extensive attention from scholars. However, in practice, the existing sequential three-way decision model often faces challenges due to factors such as missing data and unbalanced attribute granularity. To address these issues, we propose an intuitionistic fuzzy sequential three-way decision (IFSTWD) model, which introduces several significant contributions: (1) New intuitionistic fuzzy similarity relations. By integrating possibility theory, our model defines similarity and dissimilarity in incomplete information systems, establishing new intuitionistic fuzzy similarity relations and their cut relations. (2) Granulation method innovation. We propose a density neighborhood-based granulation method to partition decision attributes and introduce a novel criterion for evaluating attribute importance. (3) Enhanced decision process. By incorporating sequential three-way decision theory and developing a multi-level granularity structure, our model replaces the traditional equivalent relation in the decision-theoretic rough sets model, thus advancing the model’s applicability and effectiveness. The practical utility of our model is demonstrated through an example analysis of “Chinese + vocational skills” talent competency and validated through simulation experiments on the UCI dataset, showing superior performance compared to existing methods.

Compliance with kauri forest protection in New Zealand’s regional parks: the mediating role of trust on local versus visitor populations

Realising behavioural change in long invested environmental practices is often difficult to achieve, especially when scientific understanding of the issues is still unfolding. Having confidence in one’s action requires knowledge that actions will be effective in improving environmental outcomes. Currently, we know little about the role of social trust in mediating complex and uncertain knowledge of environmental problems and the required actions needed to address them. In this quantitative study, we surveyed 472 users of endangered kauri forests in New Zealand to better explore the role of trust in relation to pro-environmental behaviours (PEB) designed to mitigate effects of the devastating plant disease, kauri dieback. Findings show uncertainty about the scientific knowledge of the issue, recommended actions and efficacy of proposed solutions significantly influenced PEB for both residents and visitors of forests; however, this relationship was partially mediated by trust, particularly among locals residing within 5 km of infected forest areas. These findings indicate the need for closer engagement with local residents to develop institutional and scientific trust in kauri dieback interventions. We outline activities that may help build trust and recommend new areas of research to support higher compliance with environmental protection initiatives.

Confidence Prediction Based on Uncertain Knowledge Graph Structure Embedding

Abstract The development of large-scale knowledge graphs (KGs) has given rise to uncertain relational facts, leading to research on uncertain knowledge graph (KG) embeddings. While various studies have been conducted on the task of uncertain KG embeddings, they often employ simplistic scoring functions based on the internal interaction information among triplets to fit confidence scores, neglecting the rich neighborhood information. In light of this, we propose a novel model UKGSE for uncertain KG embeddings that captures the subgraph structural features formed by the neighbors of triplets, aiming to predict confidence scores for triplets. To validate the effectiveness of our model, we conduct confidence prediction tasks on benchmark datasets. The experimental results indicate that the performance of our proposed model surpasses mainstream embedding methods.

Uncertain Knowledge: The Medicalisation of Intersex People and the Production of Ignorance

Ignorance is produced through mechanisms related to power relations and socio-cultural context. This article examines whether the theoretical conceptualisation of agnotology may be useful when exploring intersex and the way it has been erased socially and physically. Specifically, based on the work of a PhD in Sociology and History of Science, it proposes categorising three types of mechanisms of ignorance production—cultural, epistemological and physical—with the aim of providing a greater understanding of how medicine, science and technology participate in a continuous process of erasing intersex bodies and lived experiences. Using medical literature, interviews and observations, the article focuses on a specific area of biomedical knowledge and intervention: the prenatal ‘treatment’ of Congenital Adrenal Hyperplasia (CAH) with dexamethasone or ‘prenatal DEX’. It shows how this procedure was pioneered by French doctors and how it continues to be practised in France despite numerous uncertainties and controversies inside and outside of the medical sphere.

Data-and knowledge-driven belief rule learning for hybrid classification

In hybrid classification problems, apart from labeled data, some related expert knowledge may also be obtained. If the partial information from these two sources can be jointly used well, the performance may be effectively improved. To this end, we develop a new framework for hybrid classification by modeling the uncertain data and knowledge in the form of belief rules under the framework of belief function theory. First, a data-driven belief rule learning algorithm is proposed to learn a compact and interpretable belief rule model from labeled training data. Then, a knowledge-driven belief rule learning algorithm is proposed to learn a model which is complementary to that learned from data via active learning strategy. Finally, the genetic integration of belief rules is developed in order to integrate data-driven belief rules with knowledge-driven ones to reduce the rule redundancy and rule conflict by considering both classification accuracy and model interpretability. Experiments based on both simulated and real data sets demonstrate the superiority of the proposed model for integrating data and knowledge.

Uncertainty modeling and propagation for groundwater flow: a comparative study of surrogates

We compare sparse grid stochastic collocation and Gaussian process emulation as surrogates for the parameter-to-observation map of a groundwater flow problem related to the Waste Isolation Pilot Plant in Carlsbad, NM. The goal is the computation of the probability distribution of a contaminant particle travel time resulting from uncertain knowledge about the transmissivity field. The latter is modelled as a lognormal random field which is fitted by restricted maximum likelihood estimation and universal kriging to observational data as well as geological information including site-specific trend regression functions obtained from technical documentation. The resulting random transmissivity field leads to a random groundwater flow and particle transport problem which is solved realization-wise using a mixed finite element discretization. Computational surrogates, once constructed, allow sampling the quantities of interest in the uncertainty analysis at substantially reduced computational cost. Special emphasis is placed on explaining the differences between the two surrogates in terms of computational realization and interpretation of the results. Numerical experiments are given for illustration.

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.

Application of E-Learning entertainment learning based on intelligent interactive experience in English reading assistance mode

In the process of E-Learning, interactive entertainment experiences and digital technology provide new models for strengthening the development of online education. This article studies the application of E-Learning entertainment learning based on intelligent interactive experience in English reading assistance mode. The E-Learning entertainment learning mode based on intelligent interactive experience focuses on creating interest and interaction, designing an interesting and stimulating learning environment, selecting appropriate technology tools to achieve E-Learning intelligent interactive experience, and designing appropriate learning content and activities according to learning objectives. Firstly, this article designs a semantic inference system that is more convenient in calculating and storing resources, and can perform semantic inference on uncertain knowledge. Secondly, this article delves into the basic principles of Bayesian network algorithms and explores the construction mechanism of Bayesian networks. Then compared with traditional algorithms, the accuracy and stability of this method were demonstrated. Digital technology and artificial intelligence provide new technological support for E-Learning, allowing students to better experience entertaining learning experiences and customize learning content according to personal needs, enhancing educational value. Experiments have shown that using computer-assisted English reading can effectively improve students’ English proficiency. This system not only reduces the boredom in traditional English reading, but also provides students with a brand new English learning atmosphere, which can effectively improve their learning efficiency.

The Application of Fuzzy Cognitive Mapping in Education: Trend and Potential

Fuzzy cognitive mapping (FCM) is a valuable tool for understanding complex issues due to its ability to express complex and uncertain knowledge domains with dynamic modelling capabilities. This study focuses on the application of FCM in education, using a bibliometric analysis and systematic review of publications in the Scopus database from 2000 to October 2023. Using keywords related to FCM and education, the study retrieved fifty-four publications for frequency analysis and citation metrics. This study showcases its results by leveraging standard bibliometric indicators, encompassing metrics such as publication growth, top-cited publications, country contributions, and preferred publication titles. Findings from the systematic review reveal the potential of FCM in providing explanatory, predictive, reflective, and strategic insights and solutions to numerous educational issues.

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information.

Rumour: A Lexicographical Description of the Word Meaning From Evidentiality Perspective

Current media studies as well as the analysis of social interaction put forward the notion of rumours playing a significant role in human communication and making it a full-fledged object of linguistic analysis. The article is devoted to the description and unification of the lexicographical meaning of the word ‘rumour’ which is basic for indirect knowledge transmission. The proposed research perspective is preconditioned by the interest in uncertain knowledge and informal communication practices including such phenomena as rumors, gossip, etc., inherent in professional communication, as well as in the category of evidentiality. The empirical material comprises dictionary interpretations, the inconsistency of which stipulated the application of the ideas of lexical semantics. Componential analysis is chosen as the main method to define meanings. As a result, the polysemy of the word under study has been exposed, with its structure including five lexico-semantic variants with different types of relations. The invariant set of ‘without confirmation’, ‘uncertain origin’, ‘questionable accuracy’ components in the structure of the meanings indicates that there is a direct connection of the word semantics with the linguistic categories of evidentiality and epistemic modality. The analysis shows that when interpreting the values obtained, the concept of evidence as a source of information has an explanatory potential, the crucial factor being its distinct classification in terms of levels. The extensive typology of reportive evidentials enables to define the central meaning of the word ‘rumour’ as information characterized as transmitted knowledge, realized through informal statements, categorized as a proper reportative marked by the third level of evidence. The core ‘questionable accuracy’ seme testifies to the correlation of the reportative evidential value and the epistemic evaluation of the acquired knowledge which manifests itself in the fact that indirect mediated access to information potentiates its qualification as lacking confirmation.

Uncertainty quantification in logistic regression using random fuzzy sets and belief functions

Evidential likelihood-based inference is a new approach to statistical inference in which the relative likelihood function is interpreted as a possibility distribution. By expressing new data as a function of the parameter and a random variable with known probability distribution, one then defines a random fuzzy set and an associated predictive belief function representing uncertain knowledge about future observations. In this paper, this approach is applied to binomial and multinomial regression. In the binomial case, the predictive belief function can be computed by numerically integrating the possibility distribution of the posterior probability. In the multinomial case, the solution is obtained by a combination of constrained nonlinear optimization and Monte Carlo simulation. In both cases, computations can be considerably simplified using a normal approximation to the relative likelihood. Numerical experiments show that decision rules based on predictive belief functions make it possible to reach lower error rates for different rejection rates, as compared to decisions based on posterior probabilities.

Three-teaching: A three-way decision framework to handle noisy labels

Learning with noisy labels represents a prevalent weakly supervised learning paradigm. Uncertain knowledge resulting from noisy labels poses significant challenges for knowledge analysis. Given the memorization effect observed in deep neural networks, training on instances with minimal loss holds promise for effectively handling noisy labels. “Co-teaching”, which is the state-of-the-art training method in this field, is characterized by the simultaneous training of two deep neural networks using instances with low loss. While this approach has demonstrated promising performance, its effectiveness heavily relies on the predictive capabilities of two neural networks. If these networks fail to provide reliable predictions, the overall learning performance may be unsatisfactory. In order to solve this problem and inspired by three-way decision, we propose a powerful learning paradigm named “Three-teaching”, which employs the “voting mechanism” to guarantee the prediction quality incrementally. In this approach, both neural networks make predictions for all the data. However, only the data that exhibits consistent prediction results and has a low loss is retained to feed into the third neural network for updating its parameters. The learning process will proceed by alternating these three neural networks’ roles. The experimental results obtained from benchmark datasets illustrate that “Three-teaching” surpasses numerous state-of-the-art methods.