Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus by Jeffrey C. Witt

“…In each case, special attention is given to how the availability of a domain specific knowledge graph helps us both properly prepare the corpus for analysis and visualize the results in ways that enhance research. …”
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Vers une ontologie de domaine pour l’analyse des tissus anciens. Le projet Silknow et le cas du patrimoine soyeux européen by Marie Puren, Pierre Vernus

“…We present the methodology used to develop a knowledge graph, and in particular the steps that were necessary to create the underlying data model, based on the CIDOC-CRM or CIDOC Conceptual Reference Model. …”
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Research on a knowledge-defined polymorphic network attainable service architecture by Jingyu WANG, Zirui ZHUANG

“…Given the consideration of future generation mobile communication network systems with decoupled administration, control, and data planes, a knowledge-defined polymorphic network (KDPN) attainable service architecture was proposed.In the proposed architecture, while mimicking the biological polymorphism, “network knowledge” was acted as a genetic core connecting all the planes, where local network knowledge was extracted from different scenarios and layers of the network.By constructing a network knowledge graph with unified logic, and depending on the service requirement for specific knowledge, different local knowledge could be exchanged and collaborated.Network knowledge will provide guidance and foundation for general network optimization and management across different service scenarios, service layers, and service objectives.KDPN will help the communication networks cope with sophisticated and dynamic service demands and provide dynamic and diversified services to general users promptly and effectively with quality of experience assurances.…”
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Graph neural network-based address classification method for account balance model blockchain by Zhiyuan LI, Binglei XU, Yingyi ZHOU

“…To regulate the transactional activities on the public blockchain involving account balance models, it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method, named AJKGS-ABCM (attention jumping knowledge graph SAGE account-based blockchain classification model), was proposed to categorize blockchain addresses, providing effective support for blockchain transaction tracking.Blockchain transaction data was represented as a graph structure, with addressed as nodes and transactions as edges.The AJK-GraphSAGE algorithm was introduced to learn embedded representations of the graph, where the model’s input required only nodes and their sampled neighboring node sets.Simultaneously, attention mechanisms and skip-connection knowledge integration strategies were incorporated into the model, allowing for adaptive weight allocation across different layers and information sharing between various levels, thereby enhancing training speed and generalization capabilities.Finally, experimental comparisons are conducted, demonstrating superior performance in terms of accuracy, recall, and F1 score compared to other methods.…”
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Ontological analysis in the problems of container applications threat modelling by A. I. Brazhuk, E. V. Olizarovich

“…A machine-readable set (dataset) of 200 data flow diagrams is developed; each diagram is obtained from the configuration of a real container application and is presented as an ontology and a knowledge graph. An ontological two-level domain-specific threat model of container applications is formed. …”
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Conceptualising Information Production in the Context of the SDHSS Ontology Ecosystem by Francesco Beretta

“…In this paper, we challenge the vision of creating a giant knowledge graph of reusable research data from multiple projects and platforms, based on Semantic Web technologies and a shared conceptualisation in the form of a commonly used ontology ecosystem, promoted by the Semantic Data for Humanities and Social Sciences project (SDHSS). …”
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Sports Big Data: Management, Analysis, Applications, and Challenges by Zhongbo Bai, Xiaomei Bai

“…Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. …”
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Survey on terminology extraction from texts by Kang Xu, Yifan Feng, Qiandi Li, Zhenjiang Dong, Jianxiang Wei

“…Abstract Automatic extraction of domain-related terminology from natural language texts is an important research topic with many practical application scenarios, such as text summarization, knowledge graph construction. It has a great impact on improving the quality of topic construction and the accuracy of semantic retrieval. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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Spatiotemporal data modeling and prediction algorithms in intelligent management systems by Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

“…In order to solve the problem of difficulty in learning semantic pattern representations between user dynamic interest sequences using path based and knowledge graph based entity embedding methods, the author proposes research on spatiotemporal data modeling and prediction algorithms in intelligent management systems. …”
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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model by Chengyao Lv, Deng Pan, Yaxiong Li, Jianxin Li, Zong Wang

“…To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. …”
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Un wiki sémantique pour l’édition scientifique d’une correspondance du xixe siècle by Bertrand Duménieu, Danièle Poublan, Jean-Damien Généro, Francine Filoche, Patricia Bleton

“…The outcome of this work is a renewed publication using Semantic MediaWiki, which publishes this corpus in a version enriched with a large number of semantic relations linking the letters and their actors within a knowledge graph exposed on the Web of data. These links are exploited to build a dynamic database of authors and recipients along with people mentioned in the letters and the places from which they were sent. …”
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DarkOnto: An Ontology Construction Approach for Dark Web Community Discussions Through Topic Modeling and Ontology Learning by Randa Basheer, Bassel Alkhatib

“…We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class-topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. …”
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Textual knowledge entity extraction of hidden dangers in coal mine accidents based on probabilistic fusion algorithm by LI Jing, LI Zequan, SHI Futai, HAO Qiang

“…Given the unstructured nature of text data related to hidden dangers in coal mine accidents, extracting latent knowledge is crucial for constructing a knowledge graph of hidden dangers in coal mine accidents. …”
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Natural Language Processing (NLP): Identifying Linguistic Gender Bias in Electronic Medical Records (EMRs) by Site Xu MPH, Mu Sun MD

“…Sentiment classification and knowledge graph with ICD-11 were used to determine the prevalence of these features in the medical records, and logistic regression was applied to test gender differences. …”
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Multigraph-based Deep Programming Ability Tracing Method for Students by Yue Wang, Guanwen Zhang

“…Specifically, MDPAT devises the multigraph programming modelling via conducting the program knowledge graph and the program iteration graph to generate the submission representations of programming exercises, which effectively captures static structure information and dynamic structure information within the programming process. …”
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The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomi... by Joana Maria Pujadas-Mora, Alícia Fornés, Oriol Ramos Terrades, Josep Lladós, Jialuo Chen, Miquel Valls-Fígols, Anna Cabré

“…However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. …”
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Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo, Keqing Ning

“…We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. …”
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RiskTree: Decision trees for asset and process risk assessment quantification in big data platforms by Zhan Haomou, Yang Jiawei, Guo Zhenyang, Cao Jin, Zhang Dong, Zhao Xingwen, You Wei, Li Hui

“…For situations where automated detection is not feasible or permitted, we provide a customized questionnaire system to collect assets and data processing procedures. We utilize a knowledge graph (KG) to integrate and analyze the collected data. …”
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Semi-automated data provenance tracking for transparent data production and linkage to enhance auditing and quality assurance in Trusted Research Environments by Katherine O'Sullivan, Milan Markovic, Jaroslaw Dymiter, Bernhard Scheliga, Chinasa Odo, Katie Wilde

“…Results The resulting Provenance Explorer for Trusted Research Environments (PE-TRE) interactive tool displays the data linkage information extracted from a knowledge graph described using the derived SHP ontology and results of rule-based validation checks. …”
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