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Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.

Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools, which has extended the application of KGs to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. For example, the amount of the political discourses in social media is overwhelming yet can be hijacked and misused by spammers to spread misinformation and false news. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a domain-based social credibility module to ensure data quality and trustworthiness. The utility of the proposed framework is verified by means of experiments conducted on two constructed KGs. The KGs are then embedded in low-dimensional semantically-continuous space using several embedding techniques. The effectiveness of embedding techniques and social credibility module is further demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

Knowledge graph completion is the task of predicting correct facts that can be expressed by the vocabulary of a given knowledge graph, which are not explicitly stated in that graph. Broadly, there are two main approaches for solving the knowledge graph completion problem. Sub-symbolic approaches embed the nodes and/or edges of a given graph into a low-dimensional vector space and use a scoring function to determine the plausibility of a given fact. Symbolic approaches learn a model that remains within the primary representation of the given knowledge graph. Rule-based approaches are well-known examples. One such approach is AnyBURL. It works by sampling random paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is close to current state of the art with the additional benefit of offering an explanation for a predicted fact. In this paper, we propose several improvements and extensions of AnyBURL. In particular, we focus on AnyBURL’s capability to be successfully applied to large and very large datasets. Overall, we propose four separate extensions: (i) We add to each rule a set of pairwise inequality constraints which enforces that different variables cannot be grounded by the same entities, which results into more appropriate confidence estimations. (ii) We introduce reinforcement learning to guide path sampling in order to use available computational resources more efficiently. (iii) We propose an efficient sampling strategy to approximate the confidence of a rule instead of computing its exact value. (iv) We develop a new multithreaded AnyBURL, which incorporates all previously mentioned modifications. In an experimental study, we show that our approach outperforms both symbolic and sub-symbolic approaches in large-scale knowledge graph completion. It has a higher prediction quality and requires significantly less time and computational resources.

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The majority of existing knowledge graphs mainly concentrate on organizing and managing textual knowledge in a structured representation, while paying little attention to the multimodal resources (e.g., pictures and videos), which can serve as the foundation for the machine perception of a real-world data scenario. To this end, in this survey, we comprehensively review the related advances of multimodal knowledge graphs, covering multimodal knowledge graph construction, completion and typical applications. For construction, we outline the methods of named entity recognition, relation extraction and event extraction. For completion, we discuss the multimodal knowledge graph representation learning and entity linking. Finally, the mainstream applications of multimodal knowledge graphs in miscellaneous domains are summarized.

To address the challenge of analyzing large-scale penetration attacks under complex multi-relational and multi-hop paths, this paper proposes a graph convolutional neural network-based attack knowledge inference method, KGConvE, aimed at intelligent reasoning and effective association mining of implicit network attack knowledge. The core idea of this method is to obtain knowledge embeddings related to CVE, CWE, and CAPEC, which are then used to construct attack context feature data and a relation matrix. Subsequently, we employ a graph convolutional neural network model to classify the attacks, and use the KGConvE model to perform attack inference within the same attack category. Through improvements to the graph convolutional neural network model, we significantly enhance the accuracy and generalization capability of the attack classification task. Furthermore, we are the first to apply the KGConvE model to perform attack inference tasks. Experimental results show that this method can infer implicit relationships between CVE-CVE, CVE-CWE, and CVE-CAPEC, achieving a significant performance improvement in network attack knowledge inference tasks, with a mean reciprocal rank (MRR) of 0.68 and Hits@10 of 0.58, outperforming baseline methods.

Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

Knowledge graph completion (KGC) refines missing entities, relationships, or attributes from a knowledge graph, which is significant for referral systems, biological informatics, and search engines. As an effective KGC approach, a graph neural network (GNN) learns to aggregate information from neighboring nodes by iteratively passing messages between them. However, the semantic and relational information contained in knowledge graphs is rarely used in the existing GNN-based approaches for KGC (i.e., only structure information is used). Hence, a semantic- and relation-based GNN (SR-GNN) model, which combines the semantic similarity information between neighboring entities and the relational features of knowledge graphs, is proposed. First, we develop an entity semantic aggregation module that learns semantic similarity information among neighboring entities connected to the same central entity via an RNN. Second, we propose a relational aggregation module that captures the different semantics among different types of relations through a GRU. This enables the model to better comprehend semantic relationships and be applied to KGC tasks requiring relationship embedding vectors. Extensive studies conducted on the FB15k-237, WN18RR, WN18 and YAGO3-10 datasets reveal that, when compared to 17 baseline models, the SR-GNN exhibits state-of-the-art performance in terms of the MRR and H@n metrics. Significantly, the MRR metric improves by 10.2% on the FB15K-237 dataset and by 4.2% on the WN18RR dataset over those of the rival models.

Drug repurposing aims to find new therapeutic applications for existing drugs in the pharmaceutical market, leading to significant savings in time and cost. The use of artificial intelligence and knowledge graphs to propose repurposing candidates facilitates the process, as large amounts of data can be processed. However, it is important to pay attention to the explainability needed to validate the predictions. We propose a general architecture to understand several explainable methods for graph completion based on knowledge graphs and design our own architecture for drug repurposing. We present XG4Repo (eXplainable Graphs for Repurposing), a framework that takes advantage of the connectivity of any biomedical knowledge graph to link compounds to the diseases they can treat. Our method allows methapaths of different types and lengths, which are automatically generated and optimised based on data. XG4Repo focuses on providing meaningful explanations to the predictions, which are based on paths from compounds to diseases. These paths include nodes such as genes, pathways, side effects, or anatomies, so they provide information about the targets and other characteristics of the biomedical mechanism that link compounds and diseases. Paths make predictions interpretable for experts who can validate them and use them in further research on drug repurposing. We also describe three use cases where we analyse new uses for Epirubicin, Paclitaxel, and Predinisone and present the paths that support the predictions.

Temporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating temporal dimensions that reflect their evolving states. These variations add complexity to the task of knowledge graph completion. Introducing temporal granularity can make the representation of facts more precise. In this paper, we propose Learning Temporal Granularity with Quadruplet Networks (LTGQ), which addresses the inherent heterogeneity of TKGs by embedding entities, relations, and timestamps into distinct specialized spaces. This differentiation enables a finer-grained capture of semantic information across the temporal knowledge graph. Specifically, LTGQ incorporates triaffine transformations to model high-order interactions between the elements of quadruples, such as entities, relations, and timestamps, in TKGs. Simultaneously, it leverages Dynamic Convolutional Neural Networks (DCNNs) to extract representations of latent spaces across different temporal granularities. By achieving more robust alignment between facts and their respective temporal contexts, LTGQ effectively improves the accuracy of temporal knowledge graph completion. The proposed model was validated on five public datasets, demonstrating significant improvements in TKG completion tasks, thereby confirming the effectiveness of our approach.