Search Results (1,371)

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents. Full article

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text. Full article

Urban waterlogging is one of the major “diseases” faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban waterlogging over time. Therefore, the objective of this study is to analyze the time, events, properties, geographic objects, and activities associated with urban waterlogging emergency responses from the geographic spatial and temporal processes perspective and to construct an urban waterlogging emergency knowledge graph by combining top-down and bottom-up approaches. We propose a conceptual model of urban waterlogging emergency response ontology based on spatiotemporal processes by analyzing the basic laws and influencing factors of urban waterlogging occurrence and development. Secondly, we describe the construction process of the urban waterlogging emergency response knowledge graph from knowledge extraction, knowledge fusion, and knowledge storage. Finally, the knowledge graph was visualized using 159 urban waterlogging events in China from 2020–2022, with a quality assessment indicating 81% correctness, 65.5% completeness, and 95% data conciseness. The results show that this method can effectively express the spatiotemporal process of an urban waterlogging emergency response and can provide a reference for the spatiotemporal modeling of the knowledge graph. Full article

Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-art performance. In order to understand the current research status of TKGC methods based on deep learning and promote further development in this field, in this paper, for the first time, we summarize the deep learning-based methods in TKGC research. First, we detail the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Then, we divide the existing deep learning-based TKGC methods into eight fine-grained categories according to their core technology and summarize them. Finally, we conclude the paper and present three future research directions for TKGC. Full article

Urban blue-green infrastructure (BGI) not only serves an ecological purpose but also contributes to the physical and psychological well-being of residents by providing cultural ecosystem services (CES), which are the nonmaterial benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences. CES is a rising BGI research and management subject, with a growing number of papers in recent years. To identify and differentiate the latest research on the development of features based on cultural ecosystem services within blue-green infrastructure, we employed CiteSpace bibliometric methodologies to analyze pertinent papers for focusing on the developmental processes and key research areas. The publishing trend, research clusters, highly cited literature, research history, research frontiers and hot areas, and high-frequency and emerging keywords were studied and assessed after reviewing 14,344 relevant papers by CiteSpace software 6.3.1 from Web of Science. The standard domains concerned, according to the keyword visualization and high-value references, are implemented cultural ecosystem services assessment combined with natural-based solutions in green spaces, urban regions, residential areas, and sustainable development. In conclusion, the following recommendations are made: (1) When urban decision-makers incorporate the perspective of cultural ecosystem services into the strategic formulation of BGI, a broader spectrum of urban BGI types should be taken into account; (2) all categories of CES should be considered; (3) research on the application of cultural ecosystem services in urban blue-green infrastructure should be more effectively and flexibly integrated into urban governance; and (4) CES should be strategically employed to improve the physical health and psychological well-being of urban residents. Full article

Learning Bayesian networks from data aims to create a Directed Acyclic Graph that encodes significant statistical relationships between variables and their joint probability distributions. However, when using real-world data with limited knowledge of the original dynamical system, it is challenging to determine if the learned DAG accurately reflects the underlying relationships, especially when the data come from multiple independent sources. This paper describes a methodology capable of assessing the credible interval for the existence and direction of each edge within Bayesian networks learned from data, without previous knowledge of the underlying dynamical system. It offers several advantages over classical methods, such as data fusion from multiple sources, identification of latent variables, and extraction of the most prominent edges with their respective credible interval. The method is evaluated using simulated datasets of various sizes and a real use case. Our approach was verified to achieve results comparable to the most recent studies in the field, while providing more information on the model’s credibility. Full article

In recent years, applying building information modeling (BIM) digital technologies to cultural heritage management, monitoring, restoration, with the objective of advancing the sustainable development of both cultural heritage protection and tourism in China, has become a prominent research focus. However, there are a few studies that comprehensively investigate the relationship between BIM, Chinese cultural heritage, and sustainable tourism development. In order to explore the application of BIM in the protection and inheritance of Chinese cultural heritage, as well as its potential in promoting the sustainable development of cultural heritage tourism, this paper adopts the quantitative research method of bibliometrics to explore the research hotspots, development background, and evolution trends of BIM-driven sustainable development in Chinese cultural heritage tourism. By using data obtained from the China Knowledge Network database, multi-level bibliometrics analysis has been conducted through visualized knowledge graphs. The results suggest that the popular research keywords for driving sustainable cultural heritage tourism in China through BIM since year 2000 (23 years) include heritage tourism, heritage protection, building heritage, digital technology, and tourism development. Three research hotspots have been identified, which are cultural heritage protection, cultural heritage tourism development, and cultural heritage tourism management. In terms of tourism development and management, building virtual interactive scenes of cultural heritage facilitated by BIM to enhance tourism experience of tourists, using BIM to assist in efficient management, intelligent decision-making, and personalized services of cultural heritage tourism, assist in better promoting the sustainable development of cultural heritage tourism. In terms of coordinating and managing stakeholders in cultural heritage tourism, BIM technology provides technical support to the government, industry managers, and community residents in information communication, and industry management by constructing a digital model of cultural heritage to better balance the rights and interests of stakeholders. Full article

The purpose of entity linking is to connect entity mentions in text to real entities in the knowledge base. Existing methods focus on using the text topic, entity type, linking order, and association between entities to obtain the target entities. Although these methods have achieved good results, they ignore the exploration of candidate entities, leading to insufficient semantic information among entities. In addition, the implicit relationship and discrimination within the candidate entities also affect the accuracy of entity linking. To address these problems, we introduce information about candidate entities from Wikipedia and construct a graph model to capture implicit dependencies between different entity decisions. Specifically, we propose a cascade attention mechanism and develop a novel local entity linkage model termed CAM-LEL. This model leverages the interaction between entity mentions and candidate entities to enhance the semantic representation of entities. Furthermore, a global entity linkage model termed DG-GEL based on a dynamic graph is established to construct an entity association graph, and a random walking algorithm and entity entropy are used to extract the implicit relationships within entities to increase the differentiation between entities. Experimental results and in-depth analyses of multiple datasets show that our model outperforms other state-of-the-art models. Full article

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method’s effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss. Full article

Off-label practice in pediatrics requires relentless engagement from all the health professionals involved. Community pharmacists are the last ones in the prescribing–dispensing chain; therefore, they have the key responsibility of verifying the correctness of a treatment. A cross-sectional study was conducted for assessing the awareness and views of Romanian community pharmacists, regarding off-label drugs in the pediatric population, through a 28-item questionnaire comprising five sections of different topics (general knowledge, frequency of prescribing and dispensing off-label medication, views, and attitudes). The sample size was 236 questionnaires with a response rate of 41.11%. A statistical analysis of the obtained data was performed with GraphPad Prism v.9. The results indicate that 55.1% of the community pharmacists have a good general knowledge and awareness regarding the off-label practice, although the legal frame is unclear. The responses highlight a high frequency of prescribing and request of medication for respiratory conditions (45.3%) and antibiotics (23.5%), with a concerning gap regarding the adverse events related to the off-label treatments (56.7%). A very small percentage of pharmacists (7.1%) contact a fellow healthcare professional when encountering an off-label prescription. In conclusion, in addition to the pharmacist’s conduct towards the best interest of the patient, there is a clear need to improve the doctor–pharmacist collaboration in order to make an off-label treatment successful. Full article

A regular map is an abstract generalization of a Platonic solid. It describes a group, a topological cell decomposition of a 2-manifold of type $\{p, q\}$ with only p-gons, such that q of them meet at each vertex in a circular manner, and we have maximal combinatorial symmetry, expressed by the flag transitivity of the symmetry group. On the one hand, we have articles on topological surface embeddings of regular maps by F. Razafindrazaka and K. Polthier, C. Séquin, and J. J. van Wijk.On the other hand, we have articles with polyhedral embeddings of regular maps by J. Bokowski and M. Cuntz, A. Boole Stott, U. Brehm, H. S. M. Coxeter, B. Grünbaum, E. Schulte, and J. M. Wills. The main concern of this partial survey article is to emphasize that all these articles should be seen as contributing to the common body of knowledge in the area of regular map embeddings. This article additionally provides a method for finding symmetrical equivelar polyhedral embeddings of type $\{3, 7\}$ based on symmetrical graph embeddings on convex surfaces. Full article

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on “High-rise Building Construction (General) Safety and Health Guidelines”, using ‘scaffolding’ as the keyword, demonstrates the framework’s effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as ‘fall’, ‘drop’, and ‘scaffolding’, with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management. Full article

With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT’s robust text representation capabilities, the GCN’s feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training. Full article

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. Full article

This article primarily delves into the automatic generation approach of the park road network. The design of the park road network not only comprehensively takes into account environmental factors like terrain, vegetation, water bodies, and buildings, but also encompasses functional factors such as road coverage and accessibility. It constitutes a relatively complex design task, and traditional design methods rely significantly on the professional proficiency of designers. Based on the park vector terrain, in combination with the graphics skeleton algorithm, this study proposes an automatic generation method of the park road network considering environmental constraints. Through the utilization of the modified Douglas–Peucker algorithm and convex hull operation, the semantic information of environmental constraints is retained, domain knowledge is integrated, the skeleton graph is optimized, and issues such as road smoothness are addressed. This method can not only generate road network schemes rapidly, scientifically, and precisely, but also furnish the requisite digital model for the quantitative evaluation of the road network. Eventually, the study quantitatively assesses the experimental results via the spatial syntax theory to substantiate the efficacy of the method. Full article