KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation

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.

Graph convolutional networks (GCN), aiming to learn meaningful representations for graph data, has been popularly used in recommender systems since user-item interactions can be represented by a bipartite graph. However, GCN often suffers from the over-smoothing issue when it goes deeper, which implies that long paths between users and items are not very useful to embedding learning in recommender systems. To deal with long paths, introducing user and item links is a promising way to shorten the average path between users and items. However, due to introducing the user and item linkages, the bipartite graph becomes a heterogeneous graph. To this end, we propose a multi-aspect heterogeneous GCN model, which is to power GCN towards recommendation tasks by introducing user-user and item-item links to build the heterogeneous user- item graph. Then, we decompose it into three subgraphs and keep the global heterogeneous graph to preserve the original structure information. Specifically, we apply GCN to learn user and item representations from both the subgraphs and the global graph. After that, we have multi-aspect attention to integrate the representations from GCN to obtain the final user and item representation. Extensive experiments on three real-world datasets show that the proposed approaches achieve significant improvements over state-of-the-art methods.

In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.

Multi-omics data are instrumental in obtaining a comprehensive picture of complex biological systems. This is particularly useful for women's health conditions such as endometriosis, which has been historically understudied despite having a high prevalence (around 10% of women of reproductive age). Subsequently, endometriosis has limited genetic characterization: current genome-wide association studies explain only 11% of its 47% total estimated heritability, underscoring the need for integrative approaches. Graph representations provide an intuitive and meaningful way to harmonize biological data, using nodes to represent biological concepts (e.g., genes, single nucleotide polymorphisms, proteins, and phenotypes) and edges to represent their relationships. We present DRIVE-KG (Disease Risk Inference and Variant Exploration Knowledge Graph), which uses a heterogeneous graph representation to integrate data from diverse multi-omics datasets. We trained two distinct models using DRIVE-KG: a link prediction model to suggest associations between SNPs and two pilot phenotypes (endometriosis and obesity), and a graph convolutional network (GCN) for patient-level classification of endometriosis/adenomyosis as a combined phenotype. We conducted patient-level classification using data from 1,441 Penn Medicine BioBank participants with gold standard chart-reviewed endometriosis/adenomyosis status. The link prediction model uncovered 66 high-confidence (model score ≥ 0.95) candidate SNP-endometriosis associations, representing largely distinct genetic signals (R2 < 0.1). These variants were enriched for obesity/body mass index traits (24.2%), lipid metabolism (6%), and depressive disorders (4.5%), showing agreement with emerging hypotheses about endometriosis etiology. In contrast, of the high-confidence, candidate SNP-obesity associations that could be evaluated using LDlink, 38.22% were in high linkage disequilibrium (R2 ≥ 0.8) with known obesity or comorbidity associations. The GCN to classify patient endometriosis/adenomyosis status had an F1 score of 0.752 compared to 0.698 for a genetic risk score. Despite this moderate improvement, we found that the GCN learned meaningful stratification of underlying adenomyosis signal and severe endometriosis grades. Together, these results demonstrate that heterogeneous integration of multi-omics data is valuable for diverse downstream tasks-including discovery and clinical prediction-particularly for understudied diseases where traditional genomic approaches are insufficient.

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features through message passing and computes ‘deep’ node representations. Despite significant progress in the field, designing GCN architectures for heterogeneous graphs still remains an open challenge. Due to the schema of a heterogeneous graph, useful information may reside multiple hops away. A key question is how to perform message passing to incorporate information of neighbors multiple hops away while avoiding the well-known over-smoothing problem in GCNs. To address this question, we propose our GCN framework Deep Heterogeneous Graph Convolutional Network (DHGCN), which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away. It first computes representations of the target nodes based on their schema-derived ego-network (SEN). It then links the nodes of the same type with various pre-defined metapaths and performs message passing along these links to compute final node representations. Our design choices naturally capture the way a heterogeneous graph is generated from the schema. The experimental results on real and synthetic datasets corroborate the design choice and illustrate the performance gains relative to competing alternatives.

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.

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

This study presents a novel approach to evaluate the efficacy of JingFang granules in treating influenza-like illness by integrating knowledge graph technology with clinical trial data. We developed an innovative knowledge graph-based pharmacological analysis method and validated its effectiveness through a randomized controlled clinical trial. A knowledge graph was constructed by extracting drug-disease entities and their relationships from the literature using a machine learning workflow. Deep mining of the knowledge graph was performed using a graph convolutional network and T5 mini-model to analyze the association between JingFang and various diseases. Subsequently, a randomized controlled clinical trial involving 106 patients was conducted. Results showed that the cure rate in the JingFang combined treatment group (92.5%) was significantly higher than in the control group (81.1%), especially among the middle-aged and elderly population. Subgroup analysis revealed that JingFang had a more pronounced therapeutic effect on patients aged 34 and above, consistent with the knowledge graph analysis results. The innovation of this study lies in proposing a novel framework for evaluating therapeutic efficacy by combining knowledge graphs with clinical trial results. This approach not only provides new analytical tools for similar drug development but also improves the efficiency and accuracy of drug development by systematically validating literature efficacy data and integrating it with actual clinical trial results. Furthermore, applying a knowledge graph to evaluate the therapeutic effects of traditional Chinese medicines like JingFang is an innovative and unique approach, bringing new perspectives to this under-explored field. This method holds potential for broad application in drug development and repurposing, particularly in the context of Traditional Chinese Medicine.

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.

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.

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

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.

&lt;p&gt;Recommender systems have been extensively utilized to meet users&amp;rsquo; personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and commodity entities, or in the embedding representations between entities of the knowledge graphs. Therefore, LighterKGCN, a recommender system model based on bi-layer graph convolutional networks was proposed in accordance with the KGCN model and the LightGCN model. In the first layer of GCN, the model first learned the embedding representations of users and commodity entities on the user-commodity entity interaction graph. Then, the attained user embedding and commodity embedding were used as the data source for the second layer of GCN. In the second layer, the entity v and its neighborhoods were calculated using the hybrid aggregation function proposed in this paper. The result was taken as the new entity v. According to tests on three public datasets and comparison results with the KGCN, LighterKGCN improved by 0.52% and 51.16% in terms of AUC and F1 performances, respectively on the dataset of MovieLens-20M; LighterKGCN improved by 0.67% and 45.0% in terms of AUC and F1 performances, respectively on the dataset of Yelp2018; and the number was 0.67% and 36.35% in AUC and F1 performances, respectively on the dataset of Last.FM.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;