Knowledge Extraction Methods Research Articles

Reclaiming and Unlocking Ancient Heritage Knowledge from Ethiopia’s Ancient Cultural Heritages

Abstract The purpose of this paper is to reclaim and unlock heritage knowledge related to the secular value of time and labor from a sample Ethiopian ancient Ge’ez parchment manuscript. We claim that Ethiopia’s ancient Ge’ez parchment manuscripts could be a source of heritage knowledge if systematically studied. We conducted an extensive and intensive survey to identify and select relevant ancient Ge’ez parchment manuscripts from ancient churches and monasteries in Tigray, Ethiopia. We selected one Ge’ez parchment manuscript for content analysis related to secular value time and another Ge’ez parchment manuscript for the secular value of labor and work ethics. Content analysis and interpretation including language translation from Ge’ez to Tigrinya and English was used as a method of heritage knowledge extraction, as were unlocking approaches from the selected sample Ge’ez parchment manuscripts. Digital content was also produced in order to have access to the digital content, which facilitates further content analysis. A critical evaluation of the physical condition of the sample manuscripts including their palaeography, folios, marginalia, and provenance was made in order to establish their relative age and credibility. From our content analysis, we extract and unlock valuable information and heritage knowledge domains related to the secular value of time and labor. The striking aspect in this study is that the sources of such heritage knowledge are the ancient Ge’ez manuscripts preserved for centuries in religious institutions like ancient churches and monasteries. These sources of heritage knowledge could be easily accepted by Christian farmers to address and solve their earthly problems. We, therefore, assert that the analysis of manuscripts democratizes access to knowledge, making historical collections accessible to a broader audience. Therefore, this current research paper extends the scope of previous studies from merely identifying preservation challenges and making ancient manuscripts accessible through digitization to conducting content analysis, unlocking relevant heritage knowledge domains, and disseminating the knowledge as well as preventing the loss of valuable heritage knowledge. Finally, we suggest making a paradigm shift from preserving heritage for its own sake to focusing on heritage knowledge transfer and education. This is mainly because we currently stand at a crossroads, facing a dilemma between valuing and utilizing heritage on one hand and ensuring its sustainable preservation on the other.

Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

AbstractAs software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

A Method for Extracting Fine-Grained Knowledge of the Wheat Production Chain

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with “disease names” and “pest names”, the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved.

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

Структура информационного хранилища системы поддержки принятия клинических решений

The construction of an information repository for a clinical decision support system (CDSS) in the weakly formalized subject area of treating bronchopulmonary diseases is considered. An overview of approaches to creating knowledge bases in this subject area is provided. A method for extracting knowledge is described, which is based on rules from clinical recommendations and the search for dependencies between words in sentences, taking into account the se-quence of rule application. The information repository of the CDSS is populated with ontological and production knowledge bases using the proposed knowledge extraction method. An ontology for the selected subject area was de-veloped, and studies of its quality were conducted through an analysis of the graph topology using cognitive ergonom-ics metrics. The effectiveness of the described knowledge extraction method is demonstrated. An original architecture for a clinical decision support system has been developed.

Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain

As the core competitiveness of the national industry, large-scale equipment such as ships, high-speed rail and nuclear power equipment, their production process involves in-depth personalization. It includes complex processes and long manufacturing cycles. In addition, the equipment’s supply chain management is extremely complex. Therefore, the development of a supply chain management knowledge graph is of significant strategic significance. It not only enhances the synergistic effect of the supply chain management but also upgrades the level of intelligent management. This paper proposes a distant supervision knowledge extraction and knowledge graph construction method in the supply chain management of large equipment manufacturing, which achieves digital and structured management and efficient use of supply chain management knowledge in the industry. This paper presents an approach to extract entity-relation knowledge using limited samples. We achieve this by establishing a distant supervision model. Furthermore, we introduce a fusion gate mechanism and integrate ontology information, thereby enhancing the model’s capability to effectively discern sentence-level semantics. Subsequently, we promptly modify the weights of input features using the gate mechanism to strengthen the model’s resilience and address the issue of vector noise diffusion. Finally, an inter-bag sentence attention mechanism is introduced to integrate different sentence bag information at the sentence bag level, which achieves more accurate entity-relation knowledge extraction. The experimental results prove that compared with the latest distant supervision method, the accuracy of relation extraction is improved by 2.8%, and the AUC value is increased by 3.9%, effectively improving the quality of knowledge graph in supply chain management.

Weakly privileged learning with knowledge extraction

Learning using privileged information (LUPI) has shown promise in improving supervised learning by embedding additional knowledge. However, its reliance on the assumption of readily available privileged information may not hold true in practical scenarios due to limitations in access or confidentiality. To address these challenges, this paper presents a novel weakly privileged learning (WPL) framework, integrating knowledge extraction methods within the LUPI context. An effective strategy is proposed to implement the WPL framework, where knowledge extraction techniques generate a weight matrix as weak privileged information. Extensive experiments employing various existing knowledge extraction techniques demonstrate that the proposed WPL outperforms traditional supervised learning and approaches the performance of standard privileged learning where privileged information is given in advance. This research establishes WPL as a promising learning paradigm, addressing limitations in privileged information availability and advancing the field of machine learning in practical settings.

Automatic Extraction of Standard Multimodal Knowledge for the Petroleum Field

As the development of network and digitalization is accelerated, huge amount of multi-modal data is generated in the standardization activities of petroleum industry exploration and development, and the number of standards is large, covering a wide range of domains and being specialized. Based on the inherent characteristics of multi-modal data and standard knowledge organization framework, this paper proposes a method for automatic knowledge extraction of standard information from standard data of petroleum industry exploration and development. This paper advances the theory depth of knowledge organization of multi-modal standard knowledge and provides effective support for standard knowledge ordination, precise knowledge service and deep knowledge discovery in petroleum industry.

Research on Entity and Relationship Extraction with Small Training Samples for Cotton Pests and Diseases

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model’s ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction.

A case for cognitive mapping constructive approach to teaching civics in higher education programs design

Abstract In the context of the information age, Civics education in colleges and universities faces new challenges. Cognitive mapping, an emerging technology, and its application in the design of Civics teaching in college courses is expected to enhance the teaching effect and quality. The purpose of this paper is to explore the use of cognitive mapping in the teaching of Civics in colleges and universities. The study uses the methods of data collection, preprocessing, knowledge extraction, fusion, and map construction to create a cognitive mapping-based model for teaching Civics in colleges and universities. The results show that the model can significantly improve the teaching effectiveness. In the named entity recognition experiments, the precision, recall, and F1 value of the BiLSTM-CRF-GNN model reached 93.47%, 93.58%, and 93.88%, respectively. In addition, the analysis of the hotspots of Civics research shows that the most frequent keywords include “Civics curriculum”, “Civics education” and “teaching practice”. This study not only advances the application of cognitive mapping theoretically, but also provides practical guidance for teaching Civics in colleges and universities, which is of great significance for improving the quality and efficiency of Civics teaching.

Retraction notice to "A video images-aware knowledge extraction method for intelligent healthcare management of basketball players" [Mathematical Biosciences and Engineering 20(2) (2023) 1919-1937

Retraction notice to "A video images-aware knowledge extraction method for intelligent healthcare management of basketball players" [Mathematical Biosciences and Engineering 20(2) (2023) 1919-1937

Knowledge-induced Multiple Kernel Fuzzy Clustering.

The introduction of domain knowledge opens new horizons to fuzzy clustering. Then knowledge-driven and data-driven fuzzy clustering methods come into being. To address the challenges of inadequate extraction mechanism and imperfect fusion mode in such class of methods, we propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm. Firstly, to extract knowledge points better, the Relative Density-based Knowledge Extraction (RDKE) method is proposed to extract high-density knowledge points close to cluster centers of real data structure, and provide initialized cluster centers. Moreover, the multiple kernel mechanism is introduced to improve the adaptability of clustering algorithm and map data to high-dimensional space, so as to better discover the differences between the data and obtain superior clustering results. Secondly, knowledge points generated by RDKE are integrated into KMKFC through a knowledge-influence matrix to guide the iterative process of KMKFC. Thirdly, we also provide a strategy of automatically obtaining knowledge points, and thus propose the RDKE with Automatic knowledge acquisition (RDKE-A) method and the corresponding KMKFC-A algorithm. Then we prove the convergence of KMKFC and KMKFC-A. Finally, experimental studies demonstrate that the KMKFC and KMKFC-A algorithms perform better than thirteen comparison algorithms with regard to four evaluation indexes and the convergence speed.

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Abstract The purpose of this paper is to present a pedagogical model for mapping subject knowledge in the field of music education. The model includes two aspects: the extraction of entities and inter-entity relationships within the subject domain and the construction of a subject ontology. In the construction process of subject knowledge mapping, this paper proposes a remote supervised music subject knowledge extraction method based on a convolutional neural network combined with an attention mechanism, which realizes the extraction of entities in the music subject domain and the relations between corresponding entities. This paper proposes a keyword extraction method for obtaining the set of discipline concepts in ontology construction. To improve performance, this paper proposes a framework structure for knowledge fusion that includes three aspects: data preprocessing, similarity calculation, and knowledge fusion. As a result of the study, the teaching model can improve the performance of music majors on average by 13.3. In terms of self-efficacy, on average reached 3.58 (SD = 0.535), which is at a good level. The results demonstrated the effectiveness of the music teaching model based on subject knowledge mapping.

A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously.

An Effective Method for Constructing a Robot Operating System Node Knowledge Graph Based on Open-Source Robotics Repositories

Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes.

Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Process knowledge is an important part of intelligent manufacturing, which has become the trend of global manufacturing industry. However, process planning in manufacturing heavily relies on experience and historical knowledge that cannot be effectively reused. Also, process knowledge is complicatedly structured, widely distributed, and weakly associated, which makes it more difficult to be shared and reused. Moreover, existing methods of organizing process knowledge are labor-intensive, inefficient and inevitable to extensive manual annotation. To overcome the above problems, a systematic method of knowledge graph construction for process knowledge is presented. First, through key concepts and relationships analysis, a domain ontology for process knowledge is proposed. Second, a pattern-based bootstrapping framework with a 2-level masking technique is established to perform process knowledge extraction, which does not require manual annotation and avoids overfitting issues. Third, a meta path-based question answering over knowledge graph approach is presented to support process planning, which can effectively capture keywords of the input question, present the intention and match the designed path. Finally, taking the production process of radiator as a research object, a corresponding manufacturing process knowledge graph is constructed and applied to process planning scenario, and experiments validate the feasibility of the proposed methods.

An indicator-based sustainability assessment framework in manufacturing organisations

Several tools and methodologies were developed for manufacturing organizations to evaluate their sustainability performance. However, the growing number of methods and tools casts some doubt upon the applicability, efficiency, and capability of those methods by real manufacturing companies. Therefore, tools that offer a standard assessment of the sustainability performance of the organization with a systematic view are much needed. On the other hand, tools are not only expected to qualitatively assess sustainability but to be capable of recognizing the opportunities that help manufacturers have a more sustainable organization. To that point, the present study will focus on the following questions: “How can we help manufacturing organizations in terms of assessing sustainability?” and “How can we help manufacturing organizations discover opportunities to reach a better state of sustainability?”. To respond to the questions, the study will use the indicator-based sustainability assessment framework that has been previously introduced by the authors and to develop a composite sustainability indicator. As a contribution to the literature, knowledge extraction and formalisation methods like Formal Concept Analysis (FCA) and Association Rule Mining (ARM) are employed in the development process of the composite Indicator. In fact, FCA and ARM helped create a novel method to select and weigh and aggregate indicators and therefore develop a composite sustainability index. Furthermore, the effectiveness of the proposed framework will be examined by a real case company.

Spam review detection with Metapath-aggregated graph convolution network

The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task.