Relational Representation Augmented Graph Attention Network for Knowledge Graph Completion

Knowledge graph embedding models are used to learn low‐dimensional representations of entities and relations in knowledge graphs. In this paper, we propose Multi‐RAttE, an attention‐based learning method for multiple relational knowledge graph embedding representation, which divides the information transfer in the knowledge graph into cross‐relational information transfer and relation‐specific information transfer, and divides the embedding of knowledge graph entities into structural embedding and multi‐relational embedding for joint learning. To objectively analyse the performance of the Multi‐RAttE model, we select two typical datasets and different representative baseline models for experimental evaluation on several tasks such as link prediction, multi‐relation prediction and node classification. The experimental results show that the Multi‐RAttE model improves 8% over the state‐of‐the‐art model Composition‐based Multi‐Relational Graph Convolutional Networks (CompGCN) in terms of Hits@1 metric on the link prediction task on FB15k‐237 dataset; on the multi‐relation prediction task, the accuracy improves by 1.8% and 3.7% in the auc metric and F1 metric, respectively. The experimental results have proved that the Multi‐RAttE model can effectively perform the representation of multiple relations.

Knowledge graph completion (KGC) aims to predict missing links based on observed triples. However, current KGC models are still limited by the following two aspects. (1) the entity semantics is implicitly learned by neural network and merely depends on existing facts, which mostly suffers from less additional specific knowledge. Although previous studies have noticed that entity type information can effectively improve KGC task, most of them rely on labeled type-specific data. (2) the recent graph-based models mainly concentrate on Graph Neural Network (GNN) to update source entity representation, regardless of the separate role that neighborhood information plays and may mix noisy neighbor features for target prediction. To address the above two issues, we propose a neighborhood re-ranking model with relation constraint for KGC task. We suggest that both relation constraint and structured information located in triples can boost the model performance. More importantly, we automatically generate explicit constraints as additional type feature to enrich entity representation instead of depending on human annotated labels. Meanwhile, we construct a neighborhood completion module to re-rank candidate entities for full use of the neighbor structure rather than traditional GNN updating manner. Extensive experiments on seven benchmarks demonstrate that our model achieves the competitive results in comparison to the recent advanced baselines.

Knowledge graph completion is an important research issue in knowledge graph construction, knowledge engineering, and natural language processing. A knowledge graph is a knowledge support for realizing accurate knowledge services in general and professional fields. It is also an important breakthrough foundation in information retrieval, question-and-answer interactions, and information recommendation. The low quality and small scale of the knowledge graph are the main bottlenecks that hinder its wide applications. The purpose of knowledge graph completion is to build a large-scale and high-quality knowledge graph for continuously updating and expanding the knowledge graph. Aiming at the difficulty of knowledge graph completion methods to extract deep semantic features from auxiliary information, such as unstructured texts, this study proposes a knowledge graph completion method based on parsing graph embedding and weighted graph convolutional network. This method uses the weighted graph convolutional network to model the semantic dependency parsing of the entity description and construct the semantic dependency parsing graph embedding. Furthermore, it introduces a multi-grained sentence-embedding generation method of the entity description, which is intended to build entity representation learning that can capture multi-grained semantics and deep-level semantic features. The experimental results on two public datasets show that the proposed knowledge graph completion approach outperforms the existing methods, thereby demonstrating its effectiveness and superiority.

Knowledge graphs have shown increasing value in semantic search, intelligent Q&A, data analysis, natural language processing, visual understanding, IoT devices, etc. It is undeniable that knowledge graphs have become the mandatory path for the artificial intelligence fields development. However, the information contained in existing knowledge graphs is incomplete, which attracts a large number of researchers to enhance the completeness of knowledge graphs utilizing knowledge graph completion methods. Most traditional embedding-based knowledge graph completion models use structural information within data-rich triples from the knowledge graph is limited by the long-tail distribution of the relations in the triples. To address this problem, we propose a Bert-based knowledge graph completion algorithm for few-shot knowledge graphs, with the main goal of implementing the knowledge graph completion task with only a few sample triples of training instances. We improve the baseline model GMatching for handling few-shot knowledge graphs by introducing the Bert pre-trained linguistic representation model to enhance the semantic representation of entities and relations in the triples. Through experiments, we demonstrate that our improved model B-GMatching achieves good results.

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.

Knowledge Graph Completion (KGC) is an essential task aimed at mitigating the issue of incompleteness in knowledge graphs, thereby enhancing their utility for various downstream applications. Existing KGC models predominantly fall into two categories: structure-based and semantic-based approaches. Structure-based methods often encounter challenges with long-tail entities due to the scarcity of structural information and imbalanced entity distributions. Conversely, semantic-based methods, while addressing those limitations, necessitate extensive training of language models and specific finetuning for each knowledge graph, thus constraining their practical efficiency. To alleviate those limitations in both approaches, in this paper, we propose KICGPTv2, an innovative framework that synergizes a large language model (LLM) with traditional KGC methods. This integration effectively mitigates the long-tail entity problem without incurring significant additional training overhead. Central to the KICGPTv2 model is a novel in-context learning strategy, termed Knowledge Prompt, which encodes structural knowledge into demonstrations to effectively guide the LLM. Comprehensive evaluations on various KGC tasks, including link prediction, relation prediction, and triple classification, underscore the efficacy of the KICGPTv2 model, highlighting its ability to achieve competitive performance with reduced training demands and without the need for finetuning

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.

Knowledge graphs (KG) contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Knowledge Graph Completion (KGC) task aims to findin missing or errant relationships with the goal of improving the general quality of KGs. Recent years have witnessed great advance of represent learning (RL) based KGC models, which represent entities and relations as elements of a continuous vector space. However, with the deepening of the research, the scale of parameters and the complexity of KGC models become larger and larger, resulting in a serious imbalance between accuracy and computational complexity. Finally, not only the efficiency is not satisfactory, but also the training cost becomes high, which seriously restricts the flexibility and scalability of the KGC model. Therefore, this paper investigates how to enhance the simplicity of KGC model and achieve a reasonable balance between accuracy and complexity. Extensive experiments show that the proposed framework improves the performance of the current represent learning models for KGC task.

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>.

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.

Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.

MRGAT: Multi-Relational Graph Attention Network for knowledge graph completion

Sparsity is a challenge for knowledge graph completion. The negative sample space, which is much larger than the positive sample space, has many negative samples similar to the positive samples or potential positive samples, which affects the performance of the knowledge graph completion model. We propose a novel knowledge graph completion method, hard negative examples mining bilinear model (HNEMB). This model uses two components to model entities and relations, namely models relations into relation matrices and inverse relation matrices to learn latent inverse semantics of relations, and models entities into head vectors and tail vectors to learn different representations of entity in different positions. Then we calculate the bilinear scores of triples, we choose correct candidates based on the scores. To address the problem of sparsity of the knowledge graph, we first propose a method for mining hard negative examples for knowledge graph completion, to make model effectively capture the associations between head entities, relations and tail entities. Finally, experiments on multiple knowledge graph completion datasets shows that, as compare with baselines, our model achieves significant improvements and shows its advantages to model sparse knowledge graph.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose DyERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.