Improving Medical X-ray Report Generation by Using Knowledge Graph

Multitype view of knowledge contrastive learning for recommendation

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.

Recent studies have shown that the problems of cold processing and data sparsity in recommender systems can be well alleviated with the help of knowledge graph information. Meanwhile, since the information in recommender systems can be represented by graph data structure, graph neural network has great advantages in recommendatory system. Therefore, graph neural network can better mine the deep relationship between objects in the recommender systems which combines with information from knowledge graph. This paper reviews the application of graph neural network in knowledge graph aided recommendation systems. Specifically, this paper firstly sorts out the related knowledge of knowledge graph, recommendation system and graph neural network, including the cold processing and data sparsity, the structure of knowledge graph, and the basic modules of graph neural network. This paper also summarizes the methods such as KGAT and CKAN used in knowledge graph aided recommendation systems in recent years, and gives some discussion on the development of this field.

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.

In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.

Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GAN-GRU and a maximum increase in R2 of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches. Our code is available at: https://github.com/MODAL-UNINA/GRAPHITE.

Entity Alignment (EA) aims to identify equivalent entities across different Knowledge Graphs (KGs), enabling knowledge fusion and integration. In recent years, Graph Neural Networks (GNNs) have emerged as a powerful paradigm for EA by leveraging structural information in KGs. However, most existing studies emphasize novel message passing mechanisms while overlooking other crucial GNN design components. This paper presents a comprehensive and systematic evaluation of GNN-based EA methods, focusing on three key aspects: message passing strategies, the number of GNN layers, and the construction of final entity representations. We benchmark a diverse set of GNN models originally developed for tasks such as node classification and knowledge graph completion, and we assess their adaptability to the EA task. Additionally, we explore the effectiveness of skip connection techniques, activation functions, and relational information integration. Our experiments, conducted on standard EA benchmarks including DBP15K and SRPRS, reveal several counterintuitive findings: (1) message passing is indispensable for EA; (2) many node classification GNNs are highly competitive for EA; (3) one or two GNN layers generally achieve optimal performance; and (4) activation functions have minimal impact, while skip connections significantly enhance results. This study provides a principled framework and empirical foundation for designing more effective GNN-based EA models. Code and datasets are publicly available at <uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/kg-bnu/GNN-EA">https://github.com/kg-bnu/GNN-EA</uri>.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

This study proposes an AI-OSINT framework for transnational religious figures, linking knowledge graphs, graph neural networks and Bayesian updating into a computable evidence chain to reconstruct and quantify the overseas assets and foreign contacts of Shi Yongxin and the Shaolin system (2024-11-2025-06). Combining GCN/GAT with unsupervised anomaly detection (Isolation Forest, LOF) on heterogeneous time-series graphs and Bayesian modelling to form monthly outputs of Religious Diplomatic Risk Index (RDRI, 0-100), assessed by ROC-AUC/AUPRC; link prediction by MRR/Hits@k. The results show that the Vatican meeting (2025-02-01) triggered a short-term peak, with the RDRI rising from c. 24-27 to c. 42, and reaching c. 45 in 2025-03; the subsequent chain of "foundations/cultural centres → out-of-country activities" maintained the index at a medium-term high of c. 40 (±3), with a medium-term high of c. 40 (±3), and a medium-term high of c. 40 (±3). The "Foundation/Cultural Centre → Outbound Activities" chain maintains the index at a medium-term high of ~40 (±3), showing a double-engine rhythm of "event amplification + resource penetration". The reported uncertainty interval is ±3; and the conclusion level is shown to be stable under a priori/likelihood ±10% perturbation. The framework enhances the systematic and verifiable study of transnational religious networks without relying on internal intelligence and is transferable to other religious or transnational NGO contexts; to avoid misuse, the RDRI is defined as an early warning scale rather than a factual or judicial characterisation (all judgements cross-checked against registered and chained anchors).Keywords: knowledge graph; graph neural networks; religious diplomacy; financial anomalies; OSINT

In recent years, entertainment content, such as movies, music, and anime, has been gaining attention due tothe stay-at-home demand caused by the expansion of COVID-19. In the content domain, research in the field ofknowledge representation is primarily concerned with accurately describing metadata. Therefore, different knowledgerepresentations are required for applications in downstream tasks. In this study, we aim to clarify effectiveknowledge representation for predicting users’ latent preferences through a case study of an anime recommendationtask. We developed hypotheses from both quantitative and qualitative aspects on how to represent work knowledgeto improve recommendation performance, and verified them by changing the structure of knowledge representationaccording to the hypothesis. Initially, we constructed a Knowledge Graph (KG) by integrating domain-specific andgeneral-purpose data sources through the process of entity matching and imposing constraints on the properties.Subsequently, we constructed multiple KGs by varying the knowledge configuration. Specifically, we changed thecomposition of the data sources considered in the KG construction or excluded a triplet associated with an arbitraryproperty. After that, we fed the constructed KGs into the graph neural network recommender model and comparedthe recommendation performance. As a result, it was shown that the recommendation performance based on the KGcomposed of multiple data sources was the best, thus supporting the hypothesis from a quantitative aspect. Next,an ablation study on the properties revealed that knowledge characterizing the work itself contributed to the recommendationperformance, thus supporting the hypothesis from a qualitative aspect. Furthermore, we constructed atext-based KG by generating a new vocabulary from the “synopsis” text. It can describe the work’s storyline andworldview in more detail. We take it as an input to a Large Language Model (LLM) and extend the existing metadatabasedKG. The results showed that the KG considering both metadata and text had the best overall recommendationperformance, again confirming the hypothesis.

A review of graph neural networks and pretrained language models for knowledge graph reasoning

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.

Research on gas turbine health assessment method based on physical prior knowledge and spatial-temporal graph neural network

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional 'omics data and set transformers to integrate low-dimensional representations of 'omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimer's disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights functional AD biomarkers.

With the rise of multi-modal methods, multi-modal knowledge graphs have become a better choice for storing human knowledge. However, knowledge graphs often suffer from the problem of incompleteness due to the infinite and constantly updating nature of knowledge, and thus the task of knowledge graph completion has been proposed. Existing multi-modal knowledge graph completion methods mostly rely on either embedding-based representations or graph neural networks, and there is still room for improvement in terms of interpretability and the ability to handle multi-hop tasks. Therefore, we propose a new method for multi-modal knowledge graph completion. Our method aims to learn multi-level graph structural features to fully explore hidden relationships within the knowledge graph and to improve reasoning accuracy. Specifically, we first use a Transformer architecture to separately learn about data representations for both the image and text modalities. Then, with the help of multimodal gating units, we filter out irrelevant information and perform feature fusion to obtain a unified encoding of knowledge representations. Furthermore, we extract multi-level path features using a width-adjustable sliding window and learn about structural feature information in the knowledge graph using graph convolutional operations. Finally, we use a scoring function to evaluate the probability of the truthfulness of encoded triplets and to complete the prediction task. To demonstrate the effectiveness of the model, we conduct experiments on two publicly available datasets, FB15K-237-IMG and WN18-IMG, and achieve improvements of 1.8 and 0.7%, respectively, in the Hits@1 metric.