Knowledge Mining Research Articles

An interoperable software system to store, associate, visualize, and publish global open science data of earth surface system

Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS.

Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pre-trained language models

Background: The research and utilization of ancient traditional Chinese medicine (TCM) books are relatively limited at present. With the rapid development of artificial intelligence (AI), knowledge graph related technology has brought light on this field. Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well to provide methodological references for the study of ancient TCM books. Methods: The types of knowledge entities and relationships between entities in Shen Nong Ben Cao Jing were analyzed. A training corpus dataset was produced by using the BIO sequence labeling method; a self-developed CNLP text labeling system was used for text labeling; the BERT model was used to recognize named entities; the relationships between entities were set based on rules and semantic associations; the data into the Neo4j-community 4.4.9 graph database was imported by using Cypher language for storage and visualization display after knowledge fusion; finally, a knowledge graph was constructed. Results: The knowledge graph of Shen Nong Ben Cao Jing included 5, 273 nodes and 11, 064 relationships. The schema layer contained 14 entity types and 15 relationship types. Through the query, knowledge can be visualized from the aspects of classification, property, and seven mutual relationships of herbal combination. Conclusion: The knowledge graph constructed in this study directly reflects the knowledge recorded in Shen Nong Ben Cao Jing and the relationship between them, which is suitable for knowledge mining and intuitive multi-dimensional display of ancient TCM books.

Social domain integrated semantic self-discovery method for recommendation

Recommender systems effectively improve the convenience for users to access interesting information from vast amounts of data resources. However, the issue of data sparsity significantly impacts recommendation performance. Heterogeneous information network (HIN), as a form of heterogeneous graph data representation, remarkably enhances recommendation performance. Despite this, current HIN-based recommendations insufficiently consider the social attributes and multidimensional interaction information of users. Additionally, the problem of expert experience dependence on extracting semantic features leads to limited generalization performance. To address these issues, we propose the social domain integrated semantic self-discovery method for recommendation (SSRec). This method constructs a social domain information fusion network to fuse multidimensional interaction information of users in the social domain and designs a multidimensional semantic knowledge mining method to extract semantic knowledge of different meta-paths. We also propose a semantic relationship self-discovery method of heterogeneous nodes that utilizes deep reinforcement learning to address the limited efficiency of traditional manual meta-paths selection. Simulation results show that the SSRec can effectively improve the recommendation performance compared with baseline methods, achieving high-precision recommendations.

Product Improvement Using Knowledge Mining and Effect Analogy

Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.

Uncertainty measures and feature selection based on composite entropy for generalized multigranulation fuzzy neighborhood rough set

With the continuous advancement of information technology, the information and data covered by various information systems become increasingly complex and diverse, it is essential to perform knowledge mining from multiple perspectives to extract valuable insights. Fuzzy neighborhood multigranulation rough set, as an excellent feature selection model, is capable of handling heterogeneous datasets more effectively, significantly improving learning efficiency. In this study, we investigate a feature selection method based on a generalized multigranulation fuzzy rough set (GMFNRS) in fuzzy decision systems. First, the concepts of fuzzy neighborhood rough sets and generalized multigranulation rough sets are introduced. Subsequently, the GMFNRS model is established to enable data mining and rule extraction from various perspectives. Secondly, from an informational perspective, the study investigates uncertainty measurement methods through fuzzy neighborhood joint entropy. Furthermore, a novel fuzzy neighborhood generalized composite entropy is proposed by integrating the GMFNRS model with uncertainty measures. Finally, a forward greedy feature selection algorithm is considered to extract essential information from complex datasets. Experimental results on 15 public datasets demonstrate that the proposed model effectively selects important features in fuzzy systems and exhibits excellent classification performance.

A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems

In the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees’ nodes represent the independent variables, while the trees’ leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach.

The sectional formation technology of an anthropogenic deposit with its subsequent mining using a hydraulic pull shovel

The paper provides a justification for the parameters of the designed technology for the sectional formation of tailing dumps and its subsequent mining using hydraulic pull shovels. The authors have found dependences for the amount of man-made feedstock lost in a section upon the section width due to economic and technical factors. During the study, a modern approach was used to solve the tasks assigned – comprehensive research that included estimation of the existing state of knowledge for anthropogenic deposit mining, study of the spatial structure and the relationship of the pull shovel specifications with the anthropogenic facility. The study has shown that high-grade tailing materials were diluted during the mining of an anthropogenic deposit due to losses and clogging, thus also reducing mining efficiency. The designed technology for the sectional formation of an anthropogenic deposit ensures selective excavation, decreasing the raw material dilution within sections, increasing mining safety and intensity, and reducing the mining costs in the future.

Domain-specific knowledge graph rule pattern mining based on generative adversarial networks

Most existing knowledge graphs (KGs) in specific domains suffer from problems of insufficient structural knowledge mining, superficial constraint of rules, incomplete system of rule patterns and higher error rate in the process of automated rule generation. In this paper, we present an adversarial generative approach for rule mining based on generative adversarial networks (GANs). The method firstly extracted a rule set according to a specific rule pattern defined manually, the rule set is then used as the adversarial training dataset for the GAN, That is, the discriminator determines whether a rule is true or not by learning the pattern of the rule set, and the generator tricks the discriminator by forging rules and improves according to the feedback from the generator.Finally, a generator is obtained to generate new rules that conform to the rule pattern, and a discriminator is obtained to determine the confidence of the automatically constructed triples.

Towards accurate correspondence between BIM and construction using high-dimensional point cloud feature tensor

The correspondence between BIM and construction instances is crucial to construction management. However, spatial deviation and geometric heterogeneity between BIM and construction point clouds pose great challenges. This paper establishes high-dimensional point cloud feature tensor to devise a point cloud semantic segmentation network and an incremental point-to-point correspondence estimation strategy against spatial deviation and geometric heterogeneity. In the construction of the stadium for the 31st Summer World University Games, the method achieved semantic segmentation accuracy (OA) of 93.8% - 99.9% and reduced BIM-and-construction correspondence error from 16 cm to 3 cm, and automatically documented four-phase construction progresses of 38 317 instances with progress monitoring error of 1%. These results demonstrate the effectiveness and applicability of the proposed method in BIM-to-construction semantic transfer and BIM-and-construction instance correspondence for large complex buildings. Future research will design transfer learning networks to achieve fully BIM-driven semantic understanding and knowledge mining for intelligent construction.

A Method for Ancient Book Named Entity Recognition Based on BERT-Global Pointer

Correct identification of entities in ancient books and documents is the basic step of analyzing ancient Chinese texts, and provides an important prerequisite for in-depth mining of humanistic knowledge in ancient books and documents. In CCL2023 named entity recognition task of ancient books, according to the task definition and the re-quirements of the task organizer, this paper proposes the BERT Global Pointer named entity recognition model; Fine tune the field adaptation training based on the unlabeled 24 history ancient book text data; SWA, FGM, cross validation and post-processing are used to improve the recognition accuracy of the model. The experimental results show that the model and the strategy proposed in this paper have good recognition effect in the multi dynasties, cross domain ancient book entity recognition scene. F1 value on the final line reaches 95.083%.

Influential Exemplar Replay for Incremental Learning in Recommender Systems

Personalized recommender systems have found widespread applications for effective information filtering. Conventional models engage in knowledge mining within the static setting to reconstruct singular historical data. Nonetheless, the dynamics of real-world environments are in a constant state of flux, rendering acquired model knowledge inadequate for accommodating emergent trends and thus leading to notable recommendation performance decline. Given the typically prohibitive cost of exhaustive model retraining, it has emerged to study incremental learning for recommender systems with ever-growing data. In this paper, we propose an effective model-agnostic framework, namely INFluential Exemplar Replay (INFER). INFER facilitates recommender models in retaining the earlier assimilated knowledge, e.g., users' enduring preferences, while concurrently accommodating evolving trends manifested in users' new interaction behaviors. We commence with a vanilla implementation that centers on identifying the most representative data samples for effective consolidation of early knowledge. Subsequently, we propose an advanced solution, namely INFERONCE, to optimize the computational overhead associated with the vanilla implementation. Extensive experiments on four prototypical backbone models, two classic recommendation tasks, and four widely used benchmarks consistently demonstrate the effectiveness of our method as well as its compatibility for extending to several incremental recommender models.

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

A Method for Inspiring Radical Innovative Design Based on Cross-Domain Knowledge Mining

The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device.

Institutionalising global mining knowledge: the rise of engineering education in late Qing China, 1870–95

Abstract The history of mining education in late Qing China has received relatively little attention in scholarly literature. Through the lens of institutionalising global mining knowledge, this article addresses the following questions: How did the demand for expertise within the framework of mining bureaucracy evolve under the impact of Western imperialism? How did mining education enter scholarly discourse and become eventually institutionalised in the late Qing China? Drawing on evidence from the collections of Sheng Xuanhuai's archive series, it investigates two previously overlooked ‘failures’ of Sheng's mining-related enterprises, namely his earliest mining practices in Hubei in the 1870s and his proposal for establishing a mining school in Shandong in 1888–89. It then reconnects these efforts with the histories of Western learning, late Qing mining bureaucracy, the monetary crisis of that era, and the global recruitment of engineers despite a pronounced distrust of foreign expertise. It argues that these seemingly discrete efforts or ‘failures’ in fact paved the way for initiating China's first engineering university as well as other mining colleges in around 1895 and eventually led to the rise of engineering education before the entire educational system was transformed in China.

Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Abstract Real-world clinical evaluation of traditional Chinese medicine (RWCE-TCM) is a method for comprehensively evaluating the clinical effects of TCM, with the aim of delving into the causality between TCM intervention and clinical outcomes. The study explored data science and causal learning methods to transform RWD into reliable real-world evidence, aiming to provide an innovative approach for RWCE-TCM. This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM. The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. The goal is to establish a method for clinical positioning, applicable population screening, and mining the structural association of TCM characteristic therapies. In addition, the study adopts the principle of real-world research and a causal learning method for TCM clinical data. We constructed a multidimensional clinical knowledge map of “disease-syndrome-symptom-prescription-medicine” to enhance our understanding of the diagnosis and treatment laws of TCM, clarify the unique therapies, and explore information conducive to individualized treatment. The causal inference process of observational data can address confounding bias and reduce individual heterogeneity, promoting the transformation of TCM RWD into reliable clinical evidence. Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM. The proposed data science methodology for TCM can handle complex data, ensure high-quality RWD acquisition and analysis, and provide in-depth insights into clinical benefits of TCM. This method supports the intelligent translation and demonstration of RWD in TCM, leads the data-driven translational analysis of causal learning, and innovates the path of RWCE-TCM.

New Approach to the Analysis of Manufacturing Processes with the Support of Data Science

This article introduces process mining as an innovative approach to enterprise data analysis, offering a systematic method for extracting, analyzing, and visualizing digital traces within information systems. The technique establishes connections within data, forming intricate process maps that serve as a foundation for the comprehensive analysis, interpretation, and enhancement of internal business processes. The article presents a methodical procedure designed to analyze processes using process mining. This methodology was validated through a case study conducted in the Fluxicon Disco software (version 3.6.7) application environment. The primary objective of this study was to propose and practically validate a methodical procedure applied to industrial practice data. Focusing on the evaluation and optimization of manufacturing processes, the study explored the integration of a software tool to enhance efficiency. The article highlights key trends in the field, providing valuable insights into process flows and identifying areas for improvement. The results contribute to the growing body of knowledge in process mining, emphasizing its applicability in fostering a more efficient and competitive manufacturing environment. In the model example, we successfully achieved a reduction in the time required for production cycles by 15% and improved resource utilization by 20%. This resulted in an increased process efficiency and a potential reduction in the required number of workers by up to 10%. These outcomes offer promising evidence of the advantages of our method and its application in an industrial setting.

MalOSDF: An Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning

The evolution of malware poses significant challenges to the security of cyberspace. Machine learning-based approaches have demonstrated significant potential in the field of malware detection. However, such methods are partially limited, such as having tremendous feature space, data inequality, and high cost of labeling. In response to these aforementioned bottlenecks, this paper presents an Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning (MalOSDF). Inspired by traditional code slicing technology, this paper proposes a feature engineering method based on opcode slice for malware detection to better capture malware characteristics. To address the challenges of high expert costs and unbalanced sample distribution, this paper proposes the SSEAL (Semi-supervised Ensemble Active Learning) algorithm. Specifically, the semi-supervised learning module reduces data labeling costs, the active learning module enables knowledge mining from informative samples, and the ensemble learning module ensures model reliability. Furthermore, five experiments are conducted using the Kaggle dataset and DataWhale to validate the proposed framework. The experimental results demonstrate that our method effectively represents malware features. Additionally, SSEAL achieves its intended goal by training the model with only 13.4% of available data.

Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.

ChartKG: A Knowledge-Graph-Based Representation for Chart Images.

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified manner.Further, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG.

Using Natural Language Processing Techniques to Study the Development Dynamics of the Subei Mongolian Language and Its Shaping of the Chinese National Community Consciousness

Abstract Language and society are a pair of covariance; language reflects the changes in society, and the changes in society have an important impact on the language. The urbanization process in the Subei area is reflected in the current Mongolian language use situation. This paper explores the intrinsic connection between language and the Chinese national community, in which the emotional identity of Mongolian script is one of the factors affecting the development of Mongolian language in Subei. Mongolian emotions are represented semantically using the SOFT self-attention mechanism. The computation of emotion word weights and distributions is carried out sequentially to enhance the emotion word tokens that semantic rules represent. Through the mining of emotion prior knowledge, new emotion words are found from the Mongolian emotion corpus with the help of emotion seed words, and a set of candidate emotion words is constructed. The emotion words that were mined in Mongolia are merged into emotion sentences, and then they are emotionally masked. An empirical survey is designed to visit and examine the local Mongolian residents in Subei using random sampling. The psychological distance of Mongolians towards intermarriage is the lowest among the subject ethnic groups, with a mean value of 1.7585. The development of the Mongolian language in Subei is a map of the progress of the Chinese nation’s consciousness and contributes to the formation of a sense of community among its people.