Automated Knowledge Graph Learning in Industrial Processes

With the rise of Industry 4.0, control systems have taken on increasing importance in industrial processes, and ensuring their security has become a pressing issue. While recent research has focused on cybersecurity threats, the security risks inherent to industrial processes themselves have been overlooked. Additionally, existing tools cannot simultaneously analyze both cyber vulnerabilities and processes anomaly in industrial settings. This paper aims to address these issues through two main contributions. First, we develop a knowledge graph to integrate information on security risks across cybersecurity and industrial processes, providing a foundation for comprehensively assessing threats. Second, we apply the link prediction task to the knowledge graph, introducing an embedding-based approach to unveil previously undiscovered knowledge. Our experiments demonstrate that the proposed method exhibits comparable performance on link prediction and is capable of mining valuable and diverse potential risks in industrial processes.

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.

Aiming at the defect traceability challenges caused by the complexity of modern industrial process operation mechanisms and high-dimensional parameter coupling, a quality anomaly diagnosis framework integrating interpretable machine learning and knowledge graph is proposed. The quantitative contributions of process parameters to quality indicators are assessed by constructing an architecture combining XGBoost prediction model and SHAP (SHapley Additive exPlanation) interpretable analysis. Structured storage of variable association patterns of historical data based on a knowledge graph. The K Nearest Neighbors (KNN) algorithm is further introduced to construct a data matching mechanism to achieve causal traceability of real-time anomalies through similarity-driven historical defect retrieval. The experimental results show that the method has a good ability to identify the root cause parameters of defects under multiple working condition scenarios, and provides a solution with both data-driven characteristics and knowledge interpretability for the quality control of complex industrial processes.

The paper reviews, represents and captures knowledge about industrial water treatment processes and predicative models, in order to facilitate management of relevant knowledge. The proposed approach is based on a Knowledge Graph (KG), which integrates process knowledge and predictive models, adding context to their application and usage; improves problem and data understanding by facilitating communication between data analysts and process engineers, providing clear, human-readable explanations; and facilitates answering process-related knowledge questions and provides answers that include relevant data elements, models, and key performance indicators (KPIs).Further, the paper includes examples of how the KG can be used in practice. Directions and recommendations are provided, as well as research guidelines of how the KG can augment generative AI approaches, paving the way for the development of retrieval-augmented knowledge management models and systems.

Numerous large knowledge graphs, such as DBpedia, Wikidata, Yago and Freebase, have been developed in the last decade, which contain millions of facts about various entities in the world. These knowledge graphs have proven to be incredibly useful for intelligent Web search, question understanding, in-context advertising, social media mining, and biomedicine. As some researchers have pointed out, a knowledge graph is not just a graph database, but it should have a layer of conceptual knowledge, which is usually represented as a set of first-order rules. However, it is challenging to automatically extract first-order rules from large knowledge graphs. In particular, traditional models are usually unable to handle rule learning in large knowledge graphs. This chapter aims to present state-of-the-art techniques and models on learning first-order rules using representation learning. By first recalling some basics of rule learning in knowledge graphs, we will introduce useful techniques of embedding-based rule learning through major models such as RLvLR and TyRuLe, which embed paths in knowledge graphs into latent spaces for candidate rule search. Then we evaluate the rule learning efficiency and the quality of automatically learned rules by applying them in link prediction. Before the chapter is concluded, we will also discuss some future research problems in the area.

Purpose Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becoming a focus in explainable recommendation research. However, limited studies about explainable recommendation problems result in low model transparency and poor persuasiveness, affecting user experience. Therefore, the goal is to provide accurate and interpretable recommendations for recommendations. Design/methodology/approach This study proposes a recommendation reasoning method based on knowledge graph and reinforcement learning. To alleviate the noise problem in the state space, a multi-head attention mechanism is used to learn state expressions. A dual critic network is used to optimize long-term and short-term rewards simultaneously, achieving path reasoning in the knowledge graph to provide short-term and long-term value explanations for recommendation decisions. Findings We conduct extensive experiments on the real-world benchmark dataset and the domain dataset to validate the effectiveness of our method on the recommendation and explanation tasks, which proves the method's ability to generate high-quality and interpretable course recommendations. Originality/value Developing explainable recommendation methods based on the combination of knowledge graph and reinforcement learning is crucial to overcome the current limitations. A recommendation system that integrates knowledge reasoning, autonomous learning and interpretability may meet the needs of the modern education field for explainable recommendation systems.

With the digitalization of many industrial processes and the increasing interconnection of devices, the number of data sources and associated data sets is constantly increasing. Due to the heterogeneity of these large amounts of data sources, finding, accessing and understanding them is a major challenge for data consumers who want to work with the data. In order to make these data sources searchable and understandable, the paradigms of Ontology-Based Data Access (OBDA) or Ontology-Based Data Integration (OBDI) are used today. An important part of these paradigms is the creation of a mapping, such as a semantic model, between a previously defined ontology and the existing data sources. Although there are already many approaches that automate the creation of this mapping by using data-driven or data-structure-driven approaches, none of them focuses on the fact that the underlying ontology evolves over time. However, this is essential in today’s age of large amounts of data and ever-growing number of data sources.

The representation learning of knowledge graph refers to embedding entities and relations in knowledge graph into a low-dimensional dense vector space. Existing knowledge graph embedding models mostly chose Euclidean Space as their vector space and consider each fact triple in knowledge graph independently. However, Euclidean Space is unable to represent knowledge effectively due to its strict constraints and mathematical expression. Besides, entities in knowledge graph are not isolated, while these models ignore the association between entities. To solve the problems above, we proposed a text-enhanced knowledge graph representation learning model in Hyperbolic Space. We utilize rich semantic information of entity description by using Transformer Encoder to enhance the ability of knowledge representation. Besides, we embed entities and relations into Hyperbolic Space, which can better capture hierarchical information of the knowledge graph. Experiments on benchmark dataset show that our method achieves better performance compared with other state-of-art methods.KeywordsKnowledge graphRepresentation learningEmbedding modelHyperbolic space

In dairy herd management, it is significant and irreplaceable for veterinarians to make rapid and effective diagnosis of dairy cow diseases. Based on electronic medical records, deep learning (DL) has been widely used to support clinical decisions for humans. However, this method is rarely adopted in veterinary diagnosis. In addition, most DL models are driven by large datasets, failing to utilize the knowledge acquired by veterinarians in subjective experience, which is critical to disease diagnosis. To address these problems, this paper proposes a DL method for disease diagnosis of dairy cow: convolutional neural network (CNN) based on knowledge graph and transfer learning (KGTL_CNN). Firstly, the structural knowledge was extracted from a knowledge graph of dairy cow diseases, and treated as part of the inputs to the CNN based on knowledge graph (KG_CNN). Then, the model performance was enhanced through pre-training by transfer learning. To verify its performance, experiments were carried out on dairy cow clinical datasets. The results show that our model performed satisfactorily on disease diagnosis: the KG_CNN and KGTL_CNN achieved an F1-score of 85.87% and 86.77%, respectively, higher than that of typical CNN by 6.58% and 7.7%. The research results greatly promote the effective, fast, and automatic clinical diagnosis of dairy cow diseases.

AI-driven security intelligence has emerged as a strategic field at the intersection of cybersecurity, data science, risk governance, and applied decision systems. This manuscript develops a unified academic framework for understanding how graph learning and multimodal reasoning can be designed, evaluated, and governed in high-stakes security environments. The book argues that security intelligence should be treated not as a narrow classification task but as a relational and evidentiary process in which heterogeneous signals must be transformed into defensible judgments under uncertainty, adversarial pressure, and institutional constraint. Across five chapters, the manuscript connects foundational research design, statistical explanation and causal inference, predictive machine learning, scalable analytics engineering, and sectoral translation into policy and industry practice. Particular attention is given to ontology design, uncertainty calibration, interpretability, generalization, reproducibility, MLOps, and governance artifacts that render intelligent systems auditable and operationally credible. The discussion is structured to support researchers seeking rigorous methods, practitioners building deployable systems, and policymakers responsible for lawful, accountable, and context-sensitive adoption. By integrating methodological reasoning with real deployment conditions across diverse regions, the manuscript offers a comprehensive roadmap for building trustworthy security intelligence systems that are technically advanced, institutionally grounded, and globally adaptable. Keywords AI-driven security intelligence, graph learning, multimodal reasoning, cybersecurity analytics, causal inference, statistical modeling, trustworthy AI, uncertainty modeling, reproducible research, MLOps, big data engineering, knowledge graphs, anomaly detection, decision support, governance-by-design, critical infrastructure security, fraud analytics, public-sector intelligence

Representation Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low dimensional space. There exits two kinds of representation methods for entities in Knowledge Graphs (KGs), including structure-based representation and description-based representation. Most methods represent entities with fact triples of KGs through translating embedding models, which can't integrate the rich information in entities descriptions with triple structure information. In this paper, we propose a novel RL method named as Representation Learning with Complete semantic Description of Knowledge Graphs (RLCD), which can exploit all semantic information of entity descriptions and fact triples of KGs, to enrich the semantic representations of KGs. More specifically, we explore Doc2Vec encoder model to encode all semantic information of entity descriptions without losing the relevance in the context of entities descriptions, and further learn knowledge representations from triples with entity descriptions. The experiment results show that RLCD gets better performance that state-of-the-art method DKRL, in terms of mean rank value and HITS. Moreover, RLCD is much faster than DKRL.

In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

A novel framework of knowledge transfer system for construction projects based on knowledge graph and transfer learning

PurposeRecommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.Design/methodology/approachThis research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.FindingsThe proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.Originality/valueThis study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

The representing learning makes specialty of knowledge graph and it indicates the difference between different entities. The knowledge graph representing with the low-dimensional space. In fact, most of the method usually concern of description of entity which is hard for existing strategies to take benefit of. Here, we recommend a new representing learning method with knowledge graphs that uses entity description. We evaluate our method on two tasks like knowledge graph and entity classification. Experimental effects on actual-world datasets show that our version plays higher than different baseline fashions, especially under the zero-short setting, which indicate that our technique for novel the entity description.