Knowledge Graph Research Articles

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledge graphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledge graphs in improving zero-shot information retrieval performance in the biomedical domain.

Text Command Intelligent Understanding for Cybersecurity Testing

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses.

Retraction Note: Intelligent recommendation method integrating knowledge graph and bayesian network

Retraction Note: Intelligent recommendation method integrating knowledge graph and bayesian network

An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don't capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information-a problem known as hallucination-which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information.

SSQ[formula omitted]: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition simtime based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition simpre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method SSQTKG, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that SSQTKG can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.

Leveraging Knowledge Graphs for Enhanced Medical Reasoning in Personalized Medicine for Rare Diseases

Leveraging knowledge graphs offers a promising approach to enhancing medical reasoning in the personalized treatment of rare diseases. Knowledge graphs provide an effective framework for organizing and interpreting complex biomedical data, facilitating a more comprehensive approach to diagnosis, prognosis, and treatment of rare diseases. By integrating diverse data sources—such as genomic profiles, clinical histories, and pharmacological information—into a cohesive graph structure, the project aims to improve clinical outcomes by enabling patient-specific insights and treatment recommendations. Rare diseases pose unique challenges due to limited data and complex symptoms, making traditional diagnostic methods inadequate. Knowledge graphs have the potential to bridge these gaps by linking disparate datasets, allowing clinicians to identify biomarkers, make more accurate diagnoses, and formulate personalized treatment pathways. This project involves constructing a multi-layered knowledge graph, applying machine learning and reasoning algorithms for pattern detection, and validating its accuracy in real-world settings. Expected outcomes include improved diagnostic accuracy, the discovery of new biomarkers, and the development of a scalable knowledge graph framework adaptable for a wide range of diseases. This work aims to contribute to the advancement of personalized medicine, offering a model for AI-driven reasoning that supports more informed and timely clinical decisions in the treatment of rare diseases. By organizing complex biomedical data into interconnected graphs, clinicians can gain comprehensive insights into rare disease mechanisms, facilitating more accurate diagnoses and personalized treatment strategies. Integrating diverse data sources, such as genomic, clinical, and pharmacological information, the knowledge graph structure enables the identification of relationships and patterns that are often missed in traditional analyses. Machine learning algorithms further support this process by identifying potential biomarkers and recommending treatment pathways based on patient-specific data. Through real-world validation with clinical partners, the knowledge graph approach is expected to improve diagnostic accuracy and support early intervention strategies. Ultimately, this framework not only addresses the unique challenges of treating rare diseases but also establishes a scalable model that can be adapted for broader applications in personalized medicine.

Model management to support systems engineering workflows using ontology-based knowledge graphs

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.

GeoPredict-LLM: Intelligent tunnel advanced geological prediction by reprogramming large language models

With the improvement of multisource information sensing and data acquisition capabilities inside tunnels, the availability of multimodal data in tunnel engineering has significantly increased. However, due to structural differences in multimodal data, traditional intelligent advanced geological prediction models have limited capacity for data fusion. Furthermore, the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data. To address these challenges, we utilize the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models (LLMs) to establish an intelligent advanced geological prediction model (GeoPredict-LLM). First, we develop an advanced geological prediction ontology model, forming a knowledge graph database. Using knowledge graph embeddings, multisource and multimodal data are transformed into low-dimensional vectors with a unified structure. Secondly, pre-trained LLMs, through reprogramming, reconstruct these low-dimensional vectors, imparting linguistic characteristics to the data. This transformation effectively reframes the complex task of advanced geological prediction as a "language-based" problem, enabling the model to approach the task from a linguistic perspective. Moreover, we propose the prompt-as-prefix method, which enables output generation, while freezing the core of the LLM, thereby significantly reduces the number of training parameters. Finally, evaluations show that compared to neural network models without pre-trained models, GeoPredict-LLM significantly improves prediction accuracy. It is worth noting that as long as a knowledge graph database can be established, GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.

ChatKG: Visualizing time-series patterns aided by intelligent agents and a knowledge graph

Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using Intelligent Agents (IA), Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visual analytics strategy that allows exploratory data analysis of a Knowledge Graph that associates temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, the related knowledge of each given pattern gathered from a multi-agent IA, and the IA’s suggestions of related datasets for further analysis visualized as annotations. We exemplify and informally evaluate ChatKG by analyzing the world’s life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, populates the Knowledge Graph to be visualized, and, during user interaction, inquires the multi-agent IA for related information and suggests related datasets to be displayed as visual annotations. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.

Joint embedding in hierarchical distance and semantic representation learning for link prediction

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (h, r, t) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.

Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model

Assembly instructions are process documents in detail describing the operation steps, materials, tools, fixtures, and assembly sequences in assembly procedures. Due to assembly instructions including numerous contents, and the content being easy for workers to understand, process designers need to spend lots of time thinking and authoring assembly instructions to ensure that workers can complete the assembly task according to the assembly instructions. Focusing on the difficulties of the variety of assembly instructions and the process factors implicit in the standard languages of assembly instructions, a method of assembly instruction generation for helicopter subassemblies is proposed. First, a data representation model of multi-source heterogeneous knowledge and information based on knowledge graphs is designed and established. Then, a hierarchical pruning VF3 algorithm is presented to reuse assembly instructions according to hybrid similarity. Finally, a process factor revision model based on RoBERTa-BiLSTM-CRF is proposed to generate revised assembly instructions. Helicopter subassemblies, which contain 11,240 assembly procedures, are used to evaluate the performance of the method for generating assembly instructions. The proposed method greatly reduces the time cost of assembly instruction authoring and promotes the intelligent development of assembly process design.

Technostress and conflict: a systematic literature review

The purpose of this literature review is to highlight the relationship between technostress and conflict, which is having great resonance in various fields, including economics, sociology and psychology. The theme has different declinations, becoming a polysemic concept and applicable, in a broad sense, in any sphere of our lives. We need only think of the extent to which technology pervades our lives, for example wearable devices for health monitoring, work that is carried out entirely through technological devices, leisure time that is increasingly devoted to online activities, the opening up of the metaverse and artificial intelligence in the world of work. By using knowledge graphs and bibliometrics, the aim of the paper was to systematically analyse (qualitatively and quantitatively) and synthesise literature that addresses the relationship between conflict and technostress. We discuss the main research topics, current challenges and the status of studies on the topic, in order to provide a reference for researchers conducting related studies, laying the foundation for possible areas of future development and empirical analyses.

Advancing Efficacy Prediction for EHR-based Emulated Trials in Repurposing Heart Failure Therapies.

The High mortality rates associated with heart failure (HF) have propelled the strategy of drug repurposing, which seeks new therapeutic uses for existing, approved drugs to enhance the management of HF symptoms effectively. An emerging trend focuses on utilizing real-world data, like EHR, to mimic randomized controlled trials (RCTs) for evaluating treatment outcomes through what are known as emulated trials (ET). Nonetheless, the intricacies inherent in EHR data-comprising detailed patient histories in databases, the omission of certain biomarkers or specific diagnostic tests, and partial records of symptoms-introduce notable discrepancies between EHR data and the stringent standards of RCTs. This gap poses a substantial challenge in conducting an ET to accurately predict treatment efficacy. The objective of this research is to predict the efficacy of drugs repurposed for HF in randomized trials by leveraging EHR in ET. We proposed an ET framework to predict drug efficacy, integrating target prediction based on biomedical databases with statistical analysis using EHR data. Specifically, we developed a novel target prediction model that learns low-dimensional representations of drug molecules, protein sequences, and diverse biomedical associations from a knowledge graph. Additionally, we crafted strategies to improve the prediction by considering the interactions between HF drugs and biological factors in the context of HF prognostic markers. Our validation of the drug-target prediction model against the BETA benchmark demonstrated superior performance, with an average AUCROC of 97.7%, PRAUC of 97.4%, F1 score of 93.1%, and a General Score of 96.1%, surpassing existing baseline algorithms. Further analysis of our ET framework on identifying 17 repurposed drugs-derived from 266 phase 3 HF RCTs-using data from 59,000 patients at the Mayo Clinic highlighted the framework's remarkable predictive accuracy. This analysis took into account various factors such as biological variables (e.g., gender, age, ethnicity), HF medications (e.g., ACE inhibitors, Beta-blockers, ARBs, Loop Diuretics), types of HF (HFpEF and HFrEF), confounders, and prognostic markers (e.g., NT-proBNP, bUn, creatinine, and hemoglobin). The ET framework significantly improved the accuracy compared to the baseline efficacy analysis that utilized EHR data. Notably, the best results were improved in AUC-ROC from 75.71% to 93.57% and in PRAUC from 78.66% to 90.34%, compared to the baseline models. Our study presents an ET framework that significantly enhances drug efficacy emulation by integrating EHR-based analysis with target prediction. We demonstrated substantial success in predicting the efficacy of 17 HF drugs repurposed for phase 3 RCTs, showcasing the framework's potential in advancing HF treatment strategies.

Who Should I Engage With at What Time? A Missing Event-Aware Temporal Graph Neural Network.

Temporal graph neural network (GNN) has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal GNNs that achieve remarkable results. However, these works focus on future event prediction and are performed under the assumption that all historical events are observable. In real-world applications, events are not always observable, and estimating event time is as important as predicting future events. In this article, we propose MTGN, a missing event-aware temporal GNN, which uniformly models evolving graph structure and timing of events to support predicting what will happen in the future and when it will happen. MTGN models the dynamic of both observed and missing events as two coupled temporal point processes (TPPs), thereby incorporating the effects of missing events into the network. Experimental results on several real-world temporal graphs demonstrate that MTGN significantly outperforms the existing methods with up to 89% and 112% more accurate time and link prediction. Code can be found on https://github.com/HIT-ICES/TNNLS-MTGN.

Adaptive Prototype Interaction Network for Few-Shot Knowledge Graph Completion.

Few-shot knowledge graph completion (FKGC), which aims to infer new triples for a relation using only a few reference triples of the relation, has attracted much attention in recent years. Most existing FKGC methods learn a transferable embedding space, where entity pairs belonging to the same relations are close to each other. In real-world knowledge graphs (KGs), however, some relations may involve multiple semantics, and their entity pairs are not always close due to having different meanings. Hence, the existing FKGC methods may yield suboptimal performance when handling multiple semantic relations in the few-shot scenario. To solve this problem, we propose a new method named adaptive prototype interaction network (APINet) for FKGC. Our model consists of two major components: 1) an interaction attention encoder (InterAE) to capture the underlying relational semantics of entity pairs by modeling the interactive information between head and tail entities and 2) an adaptive prototype net (APNet) to generate relation prototypes adaptive to different query triples by extracting query-relevant reference pairs and reducing the data inconsistency between support and query sets. Experimental results on two public datasets demonstrate that APINet outperforms several state-of-the-art FKGC methods. The ablation study demonstrates the rationality and effectiveness of each component of APINet.

Fault detection and treatment method of power converter station based on man-machine cooperative intelligence

Abstract Aiming at the problems of low efficiency of human operation and maintenance and high cost of resources faced by converter station equipment in the current new power system, this work studies and proposes an intelligent converter station anomaly collaborative processing method. Based on the data set of the existing data structure of the converter station, the operation of the power equipment is monitored by the sensor equipment. According to the abnormal signal and the designed fault analysis technology and knowledge graph, the fault intelligent analysis and fault disposal strategy are realized. The experimental results show that the proposed method can effectively monitor the abnormal data in the intelligent converter station and realize efficient fault collaborative processing.

A Rolling Bearing Fault Diagnosis Method Based on Multimodal Knowledge Graph

A Rolling Bearing Fault Diagnosis Method Based on Multimodal Knowledge Graph

Learning Heterogeneous Relation Graph and Value Regularization Policy for Visual Navigation.

The goal of visual navigation is steering an agent to find a given target object with current observation. It is crucial to learn an informative visual representation and robust navigation policy in this task. Aiming to promote these two parts, we propose three complementary techniques, heterogeneous relation graph (HRG), a value regularized navigation policy (VRP), and gradient-based meta learning (ML). HRG integrates object relationships, including object semantic closeness and spatial directions, e.g., a knife is usually co-occurrence with bowl semantically or located at the left of the fork spatially. It improves visual representation learning. Both VRP and gradient-based ML improve robust navigation policy, regulating this process of the agent to escape from the deadlock states such as being stuck or looping. Specifically, gradient-based ML is a type of supervision method used in policy network training, which eliminates the gap between the seen and unseen environment distributions. In this process, VRP maximizes the transformation of the mutual information between visual observation and navigation policy, thus improving more informed navigation decisions. Our framework shows superior performance over the current state-of-the-art (SOTA) in terms of success rate and success weighted by length (SPL). Our HRG outperforms the Visual Genome knowledge graph on cross-scene generalization with ≈ 56% and ≈ 39% improvement on Hits@ 5* (proportion of correct entities ranked in top 5) and MRR * (mean reciprocal rank), respectively. Our code and HRG datasets will be made publicly available in the scientific community.

Few-Shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network

Few-Shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network

Towards Knowledge-Aware and Deep Reinforced Cross-Domain Recommendation Over Collaborative Knowledge Graph

Towards Knowledge-Aware and Deep Reinforced Cross-Domain Recommendation Over Collaborative Knowledge Graph