Knowledge Graph-Based JingFang Granules Efficacy Analysis for Influenza-Like Illness

Graph convolutional neural networks have difficulties in model training takes a lot of time and lack of universality in meta-path design when processing knowledge graphs. To solve these issues, this paper proposes an efficient recommendation algorithm integrating knowledge graphs with graph convolutional networks(EKG-GCN). The algorithm is mainly based on the graph convolution algorithm. Firstly, the relationship between users and entities is standardized and scored, then a new neighborhood aggregation method is proposed. Some nodes in the knowledge graph are set as central nodes. A distance-influence function is set up according to the distance from other nodes to the central node, and the feature extraction of information in the knowledge graph is completed according to the distance-influence function. Finally, the knowledge graph features are used as an auxiliary to complete the recommendation task. In the experiment, three data sets were selected to compare with other advanced models, and it was found that the model is in an advantageous position in terms of training time and recommendation effect, and has a certain degree of universality.

Exploring Novel Endpoints for Clinical Trials in Kidney Disease: Challenges and Opportunities

IoT devices generate substantial amounts of measurements and use many different formats to store this data. In order to make IoT devices interoperable, a number of ontologies have been developed, such as the Smart Applications REFerence (SAREF) ontology, to create knowledge graphs that can represent all kinds of measurements from IoT devices. We call the resulting knowledge graphs: IoT measurement knowledge graphs. In this thesis, we set out to investigate what differentiates IoT measurement knowledge graphs from regular knowledge graphs. In the first half, we describe in detail how to create IoT measurement knowledge graphs from real-world measurement data. In the second half, we investigate what happens when we use the graphs in different scenarios, with different methods and applications. In order to generate an IoT measurement knowledge graph, we create a mapping to transform the measurement data coming from IoT devices. We explore multiple ways to create this mapping and evaluate the resulting IoT measurement knowledge graph with competency questions. Using our mapping, we create OfficeGraph, by transforming IoT measurement data recorded over a year from 444 IoT devices located in an office building. This IoT measurement knowledge graph is validated by answering competency questions created with the support of the building owners, showing that we can answer questions we were not able to answer without it. Besides the entities, literals, and relations in knowledge graphs, the combination of these, the context of entities, provides additional knowledge. Therefore, if we want to use entities in knowledge graphs to train machine learning models, it would be a waste to take only the entities from the graph, because this would leave out information. Representation models, such as RDF2Vec and Graph Convolutional Networks (GCNs), can be used to learn (embedding) representations for entities that take context into account, creating a representation based on which relations, entities, and literals occur near the entity in the graph. We learn over IoT measurement knowledge graphs using multiple representation models and experiment with the effect of making more knowledge available to the representation models through semantic enrichment. This is done by making implicit information, such as (e.g.) consecutive measurements, explicitly available, by adding a relation between two measurements. Results show that the semantic enrichment has a positive effect on the learnability, based on the evaluation score, of the entity representations. Due to the dynamic nature of IoT measurement knowledge graphs, it is to be expected that new measurements are made after the initial IoT measurement knowledge graphs are created. Therefore, we investigate the possibility of estimating representations for new entities that are based on the existing entity representations. We introduce the embedding estimation method, which used the numerical attributes of entities, the measurement values, to find entities similar to the new entity. Then by averaging the embedding representations of those entities, it creates a new, estimated, embedding for the new entity. We perform experiments to test the embedding estimation method, and show that there is a trade-off between the amount of time saved by not re-training the entire pipeline, but also a decrease in accuracy, with the estimated embeddings. This thesis offers a focused research into IoT measurement knowledge graph. We provide insight into how IoT measurement knowledge graph can be created and how they differ from regular knowledge graphs, specifically in being numerical, dynamic, and shallow knowledge graphs. We suggest and validate remedies to the shallowness of IoT measurement knowledge graphs, through the semantic enrichments. Furthermore we show how the numerical aspect can be a strength, through the embedding estimation method, which can help with the dynamic nature of IoT measurement knowledge graph.

Rule-enhanced iterative complementation for knowledge graph reasoning

Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly benefit from modeling the entity relatedness through Graph Convolutional Networks (GCN). Nevertheless, existing GCN entity linking models fail to take into account the fact that the structured graph for a set of entities not only depends on the contextual information of the given document but also adaptively changes on different aggregation layers of the GCN, resulting in insufficiency in terms of capturing the structural information among entities. In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge. The graph structure in our model is dynamically computed and modified during training. Through aggregating knowledge from dynamically linked nodes, our GCN model can collectively identify the entity mappings between the document and the knowledge graph, and efficiently capture the topical coherence among various entity mentions in the entire document. Empirical studies on benchmark entity linking data sets confirm the superior performance of our proposed strategy and the benefits of the dynamic graph structure.

Knowledge graphs display various entities and their relationships in the real world based on knowledge representation and can analyze and predict the intrinsic relationship between knowledge embeddings through data mining and information processing, which are widely used in search engines, web analytics and smart recommendation areas. As existing knowledge graph information is continuously created and grown, it is a curial task to determine whether the information in knowledge graph is correct and to complete the missing information. In response to this challenge, the researchers proposed a number of graph convolutional network (GCN)-based models to characterize knowledge graphs. The state-of-the-art model is R-GCN, which can effectively extract features. This paper deeply studies the algorithm ideas and results of the R-GCN model, explores whether the model can be further optimized in entity classification and link prediction, and finally, improves the original model and proposes a relational aggregation graph convolutional network. Specifically, this paper finds that a subset of the set of entities may be directly connected to a central entity. All the entities in this subset possess partially identical attributes. At the same time, the relationships between these entities and the central entity may be similar. These similar attributes and relationships can be abstractly aggregated into virtual entities and virtual relationships, respectively, to better extract the topological relationship features. This paper uses the FB15k dataset to evaluate the performance of the proposed model on knowledge graph completion tasks. The experimental results show that the proposed RA-GCN model achieves a certain level improvement compared with the original model and that it can extract knowledge graph topological relationship characteristics more effectively.

A multi-hierarchical aggregation-based graph convolutional network for industrial knowledge graph embedding towards cognitive intelligent manufacturing

Background: Drug target discovery is a vital step in drug development. Novel computational methods taking advantage of multimodal data sets and diverse algorithmic approaches are beginning to provide key insights to inform this process. Comprehensive biomedical knowledge graphs provide a particularly promising paradigm for target discovery and prioritization, being able to encode multi-modal relational evidence without the need for feature engineering or selection. Within this context, target prediction can be formulated as a link prediction problem using knowledge graph embeddings (KGEs), which learn vector representations of graph entities and provide a flexible framework to simultaneously prioritize drug targets and interpret them within the broader context of their underlying biology. Approach: We developed a comprehensive data-driven biomedical knowledge graph spanning over 4 billion relations across over 1 million entities including genes, diseases, drugs, pathways, tissues, cell types and other biomedical entities. Using a subset of this knowledge graph derived directly from molecular networks as well as analyses of large-scale cancer genomics data (including gene and protein expression, CRISPR sensitivity, somatic mutations and hypermethylation), we trained, evaluated and optimized 17 state-of-the-art KGE models, further incorporating a specialized negative sampling strategy and custom stopping criteria. The KGE models were used to predict disease-target associations as well as identify target classes along with their inter- and intra-class interaction profile signatures. Results: Cancer target predictions were evaluated using a benchmark set of known drug targets. The best KGE methods sharing molecular and drug target information across cancer types achieved an average AUPRC of 0.794 and significantly outperformed standard machine learning approaches trained on each cancer separately. KGE gene embeddings formed distinct clusters aligned with target-disease associations and gene-gene interaction profiles. Analysis of these clusters revealed sets of cancer drug targets enriched in distinct biological functions, including immune checkpoints, GPCRs, cell cycle regulators, and ribosomally-associated genes. Many of the predicted new targets were embedded close to genes with established target roles - providing alternative target candidates which share common molecular pathways and/or biological mechanisms with already established targets. Notably, we also identified gene clusters with few or no known targets whose cancer relationships were nevertheless strongly supported by the underlying data, highlighting the potential of our approach to discover target candidates within new functional classes. Citation Format: Bob Zimmermann, Radosław Bielecki, Roy Ronen, Janusz Dutkowski. Knowledge graph AI-based prioritization of drug target candidates across 503 cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB112.

Harnessing the unlimited potential of RNA-sequencing data requires efficient and scalable solutions. In this research, we explore the power of knowledge graphs to analyze variant-level information extracted from RNA-sequences of COVID-19 patients. We propose a unified knowledge graph specifically tailored to the real-world relationships amongst entities obtained from Variant Call Format (VCF) files, annotated VCF files using the SnpEff tool, and associated CADD scores files while also maintaining a level of generality that permits future extrapolation and expansion. Utilizing available graph storage such as Blazegraph, we establish a large, scalable knowledge graph containing billions of nodes and edges that not only enables efficient storage, querying, and dataset creation but also serves as a foundation for several downstream tasks. To demonstrate its versatility, we present two compelling case studies using two different subsets of the graph, one containing 73 million nodes and 112 million edges and the other graph containing 1 million nodes and 2 million edges and employ graph machine learning techniques for node classification tasks. Several challenges, such as efficient scalability, capturing real-world knowledge at a large scale, and attaining a Graph Neural Network (GNN) with good performance and evaluation metrics, have been considered and dealt with. We compare two GNN models, GraphSAGE and Graph Convolutional Network (GCN), to identify the range of raw scores it can belong to and the GraphSAGE model to identify the deleteriousness of a variant using different model hyperparameter settings. Through the experiments conducted, we demonstrate that neighborhood knowledge can tremendously aid the model in learning and that the GraphSAGE model outperforms GCN for the CADD category node classification task. By seamlessly integrating a variety of RNA-seq genomic information into a coherent knowledge graph framework, our approach not only addresses the immediate challenges of accessing and parsing variant-level analysis but also lays the groundwork for future advancements using knowledge graphs in genomic research. Finally, we demonstrate the workings of our tool named VariantKG and present usage scenarios, namely, graph enrichment, graph creation, and graph machine learning and inference.

The traditional construction of a knowledge graph in the field of credit evaluation for international trade enterprises relies on manual collection and organization of data, which can easily lead to errors and biases. Relying on specific data sources cannot fully reflect a company's credit status. Optimizing the knowledge graph of international trade enterprise credit evaluation using AI technology can objectively evaluate the credit status of enterprises. This article collected enterprise registration information and financial data from multiple data sources, performed preprocessing operations such as data cleaning, and used recurrent neural network technology to identify entities and extract relationships from the data. It then stored entities and relationships through graph database technology, constructed a knowledge graph, and regularly updates the data. This article used graph convolutional neural network technology to analyze and predict the relationships between entities in the knowledge graph, filling in the missing relationships in the knowledge graph. After optimizing the knowledge graph using AI technology, the data coverage can reach 90%, which can provide credit evaluations for more enterprises. The optimized knowledge graph can comprehensively evaluate and analyze the credit situation of enterprises, providing more reliable and authentic credit evaluation results. This efficiently builds and updates a knowledge graph to help users make clearer decisions.

Textual entailment is a fundamental task in natural language processing. Most approaches for solving this problem use only the textual content present in training data. A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task. However, the proposed models do not fully exploit the information in the usually large and noisy KGs, and it is not clear how it can be effectively encoded to be useful for entailment. We present an approach that complements text-based entailment models with information from KGs by (1) using Personalized PageRank to generate contextual subgraphs with reduced noise and (2) encoding these subgraphs using graph convolutional networks to capture the structural and semantic information in KGs. We evaluate our approach on multiple textual entailment datasets and show that the use of external knowledge helps the model to be robust and improves prediction accuracy. This is particularly evident in the challenging BreakingNLI dataset, where we see an absolute improvement of 5-20% over multiple text-based entailment models.

Prospective Clinical Trial Registration: A Prerequisite for Publishing Your Results.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Incorporating knowledge graphs (KGs) into recommender systems to provide explainable recommendation has attracted much attention recently. The multi-hop paths in KGs can provide auxiliary facts for improving recommendation performance as well as explainability. However, existing studies may suffer from two major challenges: error propagation and weak explainability. Considering all paths between every user-item pair might involve irrelevant ones, which leads to error propagation of user preferences. Defining meta-paths might alleviate the error propagation, but the recommendation performance would heavily depend on the pre-defined meta-paths. Some recent methods based on graph convolution network (GCN) achieve better recommendation performance, but fail to provide explainability. To tackle the above problems, we propose a novel method named K nowledge-aware R easoning with G raph C onvolution N etwork (KR-GCN). Specifically, to alleviate the effect of error propagation, we design a transition-based method to determine the triple-level scores and utilize nucleus sampling to select triples within the paths between every user-item pair adaptively. To improve the recommendation performance and guarantee the diversity of explanations, user-item interactions and knowledge graphs are integrated into a heterogeneous graph, which is performed with the graph convolution network. A path-level self-attention mechanism is adopted to discriminate the contributions of different selected paths and predict the interaction probability, which improves the relevance of the final explanation. Extensive experiments conducted on three real-world datasets show that KR-GCN consistently outperforms several state-of-the-art baselines. And human evaluation proves the superiority of KR-GCN on explainability.

Recently, recommendation algorithms based on Graph Convolution Network (GCN) have achieved many surprising results thanks to the ability of GCN to learn more efficient node embeddings. However, although GCN shows powerful feature extraction capability in user-item bipartite graphs, the GCN-based methods appear powerless for knowledge graph (KG) with complex structures and rich information. In addition, all of the existing GCN-based recommendation systems suffer from the over-smoothing problem, which results in the models not being able to utilize higher-order neighborhood information, and thus these models always achieve their best performance at shallower layers. In this paper, we propose a Deep Light Graph Convolution Network for Knowledge Graph (KDL-GCN) to alleviate the above limitations. Firstly, the User-Entity Bipartite Graph approach (UE-BP) is proposed to simplify knowledge graph, which leverages entity information by constructing multiple interaction graphs. Secondly, a Deep Light Graph Convolution Network (DLGCN) is designed to make full use of higher-order neighborhood information. Finally, experiments on three real-world datasets show that the KDL-GCN proposed in this paper achieves substantial improvement compared to the state-of-the-art methods.