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Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open-source knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.

Crop diseases and pests pose a critical threat to global food security and agricultural sustainability. Traditional control methods are often limited by delayed diagnosis and a lack of capability for personalized solutions. To address these challenges, we developed a knowledge-enhanced diagnostic and treatment agent, optimized through multi-source knowledge fusion and farmer feedback. The agent integrates disease identification results from a visual model, multi-factor contextual parameters, and a crop knowledge graph. These components form a unified multi-source knowledge representation. The system converts multi-criteria farmer evaluations into reward signals, enabling continuous optimization of action strategies through interaction with real-world environments. Under the combined guidance of multi-source knowledge and farmer feedback-driven reinforcement learning, the agent can generate accurate and practically applicable treatment recommendations without additional task-specific fine-tuning of the generative model. Experiments on multiple baseline models demonstrate that combining these components consistently achieves the best performance. BERTScore increases by 25.23% on average, accuracy based on large language model evaluation improves by 30.27%, and practicality increases by 37.67%. These results validate the effectiveness and generalization capability of the proposed method.

Knowledge plays a vital role in multi-attribute group decision-making (MAGDM), where experts from different-fields present their knowledge to support decision-making by employing multi-granularity linguistic model. The main goals of current work are targeted to present a novel MAGDM approach by integrating the extended Archimedean Copulas (EACs), group-individual best-worst method (GIBWM) and multi-attributive border approximation area comparison (MABAC) approach to fuse multi-source knowledge with multi-granularity intuitionistic 2-tuple linguistic information (I2TLI) with unknown weight information of attributes and experts. To begin with, for the sake of modeling the relationships between attributes (experts), the Copula-based aggregation operators with I2TLI are recommended together with some of its variations discussed; In addition, taking the merits of GIBWM methods, an algorithm of weight information of expert and attributes is designed; Thirdly, considering the decision maker’s behaviour preference and psychology, a modified MABAC method is proposed by modified prospect matrix. Simultaneously, an algorithm for MAGDM based on I2TLI with different granularity is designed by integrating GIBWM and modified MABAC approach. Last of all, an example is furnished to manifest the significance of the proposed method along with related discussions, the advantages of this method are analyzed by comparing with the extant decision-making approaches.

Along with the wide application of intelligent systems in various fields, the combination of data fusion and knowledge graph has become the key to enhance the system’s problem solving capability. However, existing data fusion methods still face challenges when dealing with multi-source heterogeneous data, especially in how to effectively combine knowledge graph. Therefore, this paper systematically reviews existing data fusion methods based on knowledge graph and classifies them into three categories: fusion of raw data, fusion of raw data with knowledge graph, and fusion of knowledge graphs. Each category of methods is described and analyzed in detail by combining a general framework with specific examples. In addition, this paper also discusses the future research direction of data fusion based on knowledge graph, and analyzes the challenges and opportunities it faces. This paper provides a theoretical framework and practical guidance for the problem of multi-source heterogeneous data fusion, and provides methodological support for the development of intelligent systems.

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.

Multi-source heterogeneous knowledge graph fusion faces significant challenges due to schema heterogeneity, entity conflicts, and relationship inconsistencies across different knowledge sources. This paper proposes CausalFusion, a novel adaptive fusion algorithm that leverages causal discovery principles to guide the knowledge graph integration process. The algorithm incorporates a constraint-based causal discovery component specifically designed for relational data, an adaptive weight learning mechanism that dynamically adjusts source contributions based on causal strength, and a conflict resolution strategy that prioritizes causal consistency over statistical correlation. Experimental evaluation on benchmark datasets including DBpedia, Freebase, YAGO, and Wikidata demonstrates significant improvements in fusion quality, with the proposed method achieving 91.2% precision and 88.7% recall, outperforming state-of-the-art baselines by 1.9% and 1.5% respectively. The results validate the effectiveness of incorporating causal inference into knowledge graph fusion, particularly for preserving meaningful causal relationships while resolving heterogeneity conflicts.

Wildfire is one of the main hazards affecting large areas and causes great damage all over the world, and the rapid development of the wildland-urban interface (WUI) increases the threat of wildfires that have ecological, social, and economic consequences. As one of the most widely used methods for tracking fire, remote sensing can provide valuable information about fires, but it is not always available, and needs to be supplemented by data from other sources. Social media is an emerging but underutilized data source for emergency management, contains a wealth of disaster information, and reflects the public’s real-time witness and feedback to fires. In this paper, we propose a fusion framework of multi-source data analysis, including social media data and remote sensing data, cellphone signaling data, terrain data, and meteorological data to track WUI fires. Using semantic web technology, the framework has been implemented as a Knowledge Base Service and runs on top of WUIFire ontology. WUIFire ontology represents WUI fire–related knowledge and consists of three modules: system, monitoring, and spread, and tracks wildfires happening in WUIs. It provides a basis for tracking and analyzing a WUI fire by fusing multi-source data. To showcase the utility of our approach in a real-world scenario, we take the fire in the Yaji Mountain Scenic Area, Beijing, China, in 2019 as a case study. With object information identified from remote sensing, fire situation information extracted from Weibo, and fire perimeters constructed through fire spread simulation, a knowledge graph is constructed and an analysis using a semantic query is carried out to realize situational awareness and determine countermeasures. The experimental results demonstrate the benefits of using a semantically improved multi-source data fusion framework for tracking WUI fire.

Correct segmentation of stroke lesions from magnetic resonance imaging (MRI) is crucial for neurologists and patients. However, manual segmentation relies on expert experience and is time-consuming. The complicated stroke evolution phase and the limited samples pose challenges for automatic segmentation. In this study, we propose a novel deep convolutional neural network (Res-CNN) to automatically segment acute ischemic stroke lesions from multi-modality MRIs. Our network draws on U-shape structure, and we embed residual unit into network. In Res-CNN, we use residual unit to alleviate the degradation problem and use multi-modality to exploit the complementary information in MRIs. Before training the model, we use data fusion and data augmentation methods to increase the number of training images. Seven neural networks are extensively evaluated on two acute ischemic stroke datasets. Res-CNN shows good performance compared with other six networks both in single modality and multi-modality. Furthermore, compared with the gold standard segmentation manually labeled by two neurologists on a local test dataset, our network achieves the best results in seven neural networks. The average Dice coefficient and Hausdorff distance of our method are 74.20% and 2.33 mm, respectively. Our proposed network may provide a useful tool for segmentation lesion of acute ischemic stroke.

Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of remote sensing observations with sector-specific disaster statistical data to construct a knowledge system that supports early warning decision-making remains a significant challenge. This study aims to address the bottleneck in the “data-information-knowledge-service” transformation process by constructing an integrated natural disaster early warning knowledge graph that incorporates multi-source heterogeneous data. We first designed an ontological schema layer comprising six core elements: disaster type, event, anomaly information, impact information, warning information, and decision information. Subsequently, multi-source data were integrated from various sources, including the Emergency Events Database (EM-DAT), sector-specific websites, encyclopedic pages, and remote sensing imagery such as Gaofen-2 (GF-2) and Sentinel-1. A Bidirectional Encoder Representations from Transformers with a Conditional Random Field layer (BERT-CRF) model was employed for entity and relation extraction, and the knowledge was stored and visualized using the Neo4j graph database. The core innovation of this research lies in proposing a quantitative methodology for assessing disaster intensity, impact, and trends based on remote sensing evaluation, establishing a knowledge conversion mechanism with sector-specific warning levels, and designing explicit warning issuance rules. A case study on a specific wildfire event (2017-0417-PRT, Coimbra, Portugal) demonstrates that the knowledge graph not only achieves organic integration and visual querying of multi-source disaster knowledge but also facilitates warning decision-making driven by remote sensing assessment indicators. For this event, quantitative analysis of Gaofen-2 imagery yielded intensity, impact, and trend levels of 4, 3, and 3, respectively, which, when applied to our warning rule (intensity ≥ 1 or impact ≥ 1 or trend ≥ 3), automatically triggered an early warning, thereby validating the rule’s practicality. A preliminary performance evaluation on 50 historical wildfire events demonstrated promising results, with an F1-score of 74.3% and an average query response time of 128 ms, confirming the system’s practical responsiveness and detection capability. In conclusion, this study offers a novel and operational technical pathway for the deep interdisciplinary integration of remote sensing and disaster science, effectively bridging the gap between data silos and actionable warning knowledge.