ABSTRACT Knowledge graphs (KGs) have shown great power in many downstream natural language processing (NLP) tasks, such as recommendation system and question answering. Despite the large amount of knowledge facts in KGs, KGs still suffer from an issue of incompleteness, namely, lots of relations between entities are missing. Link prediction, also known as knowledge graph completion (KGC), aims to predict missing relations between entities. The models based on tensor decomposition, such as Rescal and DistMult, are promising to solve the link prediction task. However, previous Rescal model lacks the ability to scale to large KGs due to the large amount of parameters. DistMult simplifies Rescal by using diagonal matrices to represent relations, while it suffers from the limitation of dealing with antisymmetric relations. To address these problems, in this paper, we propose a SparseMult model, which is a novel tensor decomposition model based on sparse relation matrix. Specifically, we view KGs as 3D tensors and decompose them as entity vectors and relation matrices. To reduce the number of parameters in relation matrices, we represent each relation matrix as a sparse block diagonal matrix. Thus, the complexity of relation matrices grow linearly with the embedding size, making it able to scale up to large KGs. Moreover, we analyze the ability of modeling different relation patterns and show that our SparseMult is capable to model symmetry, antisymmetry, and inversion relations. We conduct extensive experiments on three widely used benchmark datasets FB15k‐237, WN18RR, and CCKS2021 KGs. Experimental results demonstrate that our SparseMult model outperforms most of the state‐of‐the‐art methods.

Temporal knowledge graph embedding (TKGE) aims to learn the embedding of entities and relations in a temporal knowledge graph (TKG). Although the previous graph neural networks (GNN) based models have achieved promising results, they cannot directly capture the interactions of multi-facts at different timestamps. To address the above limitation, we propose a time-aware relational graph attention model (TARGAT), which takes the multi-facts at different timestamps as a unified graph. First, we develop a relational generator to dynamically generate a series of time-aware relational message transformation matrices, which jointly models the relations and the timestamp information into a unified way. Then, we apply the generated message transformation matrices to project the neighborhood features into different time-aware spaces and aggregate these neighborhood features to explicitly capture the interactions of multi-facts. Finally, a temporal transformer classifier is applied to learn the representation of the query quadruples and predict the missing entities. The experimental results show that our TARGAT model beats the GNN-based models by a large margin and achieves new state-of-the-art results on four popular benchmark datasets.

Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause several major problems, including performance restraint due to the insufficiency of available seed alignments and ignorance of pre-aligned links that are useful in contextual information in-between nodes. In this article, we propose DuGa-DIT, a dual gated graph attention network with dynamic iterative training, to address these problems in a unified model. The DuGa-DIT model captures neighborhood and cross-KG alignment features by using intra-KG attention and cross-KG attention layers. With the dynamic iterative process, we can dynamically update the cross-KG attention score matrices, which enables our model to capture more cross-KG information. We conduct extensive experiments on two benchmark datasets and a case study in cross-lingual personalized search. Our experimental results demonstrate that DuGa-DIT outperforms state-of-the-art methods.

The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks. Although GAT has reached the state-of-the-art (SOTA) performance as a recent success in relation prediction in knowledge graph, the current model is still limited by the following two aspects: (1) the existing model only considers the neighbors from the inbound-direction of the given entity, but ignores the rich neighborhood information from outbound-directions; (2) the existing model only uses the k-th hop output to learn the multi-hop embeddings, which leads to the loss of a large amount of early-stage embedding information (e.g., one-hop) at the graph attention step. In this study, we propose a novel bidirectional graph attention network (BiGAT) to learn the hierarchical neighbor propagation. In our proposed BiGAT, an inbound-directional GAT and an outbound-directional GAT are introduced to capture sufficient neighborhood information before propagating the bidirectional neighborhood information to learn the multi-hop feature embeddings in a hierarchical manner. Experiments conducted on the four publicly available datasets show that BiGAT achieves the competitive results in comparison to other SOTA methods.

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years.Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results.However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs.In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets.The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.

Traditional Chinese Medicine (TCM) has been used by practitioners for millennia to prevent and treat disease, but has struggled to gain broad acceptance in the West. In 2019, the World Health Organisation officially recognized TCM as a form of medical treatment, a step towards internationalizing TCM and integrating it with Western medicine (WM). The proposed dissertation research aims to bridge eastern and western medical philosophies by applying named entity recognition (NER) and information retrieval (IR) models supported by medical and cross lingual knowledge graphs, to enhance the retrieval performance as well as to increase the model explainability.

Open source softwares play an important role in information retrieval research. Most of the existing open source information retrieval systems are implemented in Java or C++ programming language. In this paper, we propose Parrot1, a Python-based interactive platform for information retrieval research. The proposed platform has mainly three advantages in comparison with the existing retrieval systems: (1) It is integrated with Jupyter Notebook, an interactive programming platform which has proved to be effective for data scientists to tackle big data and AI problems. As a result, users can interactively visualize and diagnose a retrieval model; (2) As an application written in Python, it can be easily used in combination with the popular deep learning frameworks such as Tersorflow and Pytorch; (3) It is designed especially for researchers. Less code is needed to create a new retrieval model or to modify an existing one. Our efforts have focused on three functionalists: good usability, interactive programming, and good interoperability with the popular deep learning frameworks. To confirm the performance of the proposed system, we conduct comparative experiments on a number of standard test collections. The experimental results show that the proposed system is both efficient and effective, providing a practical framework for researchers in information retrieval.