Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs

In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

In recent years, interest in knowledge graphs (KGs) has increased exponentially in both the scientific and industrial communities. Integration of various KGs is a pressing problem and is used, for example, to develop complex digital twins of industrial systems. Knowledge graph integration is also necessary when combining KGs extracted from natural language texts using large language models. One component of solving the KG integration problem is entity alignment (EA), which attempts to identify entities in different KGs that describe the same real-world object. In reality, many entities in real KGs have no equivalents in other KGs. In particular, each knowledge graph fragment extracted from a single publication may have its own structure of entity names and identifiers, which significantly complicates the task of identifying entities. This paper describes experiments on entity alignment in the presence of unmatchable entities using a Russian-English dataset as an example.

The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.

Entity alignment refers to discovering identical entity pairs in 2 knowledge graphs, which is a significant task in knowledge fusion. Early automated entity alignment techniques are based mainly on similarity calculation and comparing symbolic features, i.e., entity names, between entities. Nevertheless, such methods’ performance would reduce significantly when the difference between knowledge graphs is enormous because of relying on predefined comparison rules. Recently, embedding-based methods calculate the similarity between entity pairs through vector embeddings and thus can deal with different knowledge graphs. However, embedding-based methods mostly require humans to annotate data, which is laborious. Therefore, we learn from each other to propose an unsupervised entity alignment framework in this work, which can generate initial alignment seeds automatically by considering symbolic similarities. It can effectively avoid the waste of human resources and is suitable for handling multiple types of knowledge graphs. In addition, we investigate improving the quality and quantity of initial alignment by integrating multiple symbolic similarity features of entities and dealing with the situation of entity information missing better. Experimental results on 3 real datasets demonstrate its state-of-the-art performance.

Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thereby limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport model (CPL-OT) for entity alignment. The key idea is to iteratively pseudo-label alignment pairs empowered with conflict-aware optimal transport (OT) modeling to boost the precision of entity alignment. CPL-OT is composed of two key components—entity embedding learning with global-local aggregation and iterative conflict-aware pseudo labeling—that mutually reinforce each other. To mitigate alignment conflicts during pseudo labeling, we propose to use optimal transport as an effective means to warrant one-to-one entity alignment between two KGs with the minimal overall transport cost. Extensive experiments on benchmark datasets validate the superiority of CPL-OT over state-of-the-art baselines under both settings with and without prior alignment seeds.

A comprehensive survey of entity alignment for knowledge graphs

Entity alignment (EA) is the task of finding equivalent entities in various knowledge graphs (KGs) that represent the same object in the real world, which aims at building a unified large-scale knowledge graph with rich and high-quality content. Existing EA works have achieved promising performance. These works mainly focus on learning the entity embeddings by capturing the structural semantics of entities on the relational triples in KG. Some works also take utilization of attributes information in entity embeddings. However, they are still hindered by the lack of prior seed alignment known as labeled training data. In this paper, we propose a novel approach for KG entity alignment based on transfer learning named DAEA, which incorporates both attributes triples and relational triples to generate the entity embeddings. DAEA first uses the J-GCN model to learn the entity representation model in source domain KG. Then, it combines domain adversarial network to learn entity representation model of the target domain. Since DAEA can transfer common entity information from the source domain to the target domain, it mitigates the problem of poor alignment performance due to lack of prior seed alignment. Experiments results show that DAEA significantly outperforms the state-of-the-art EA approaches on both monolingual and cross-lingual entity alignment datasets. Ablation studies on different components further demonstrate the effectiveness of our approach.

CTEA: Context and Topic Enhanced Entity Alignment for knowledge graphs

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>.

An effective knowledge graph entity alignment model based on multiple information

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our framework more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.

Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities' similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities' similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

Finding similar entities among knowledge graphs is an essential research problem for knowledge integration and knowledge graph connection. This paper aims at finding semantically similar entities between two knowledge graphs. This can help end users and search agents more effectively and easily access pertinent information across knowledge graphs. Given a query entity in one knowledge graph, the proposed approach tries to find the most similar entity in another knowledge graph. The main idea is to leverage graph embedding, clustering, regression and sentence embedding. In this approach, RDF2Vec has been employed to generate vector representations of all entities of the second knowledge graph and then the vectors have been clustered based on cosine similarity using K medoids algorithm. Then, an artificial neural network with multilayer perception topology has been used as a regression model to predict the corresponding vector in the second knowledge graph for a given vector from the first knowledge graph. After determining the cluster of the predicated vector, the entities of the detected cluster are ranked through sentence-BERT method and finally the entity with the highest rank is chosen as the most similar one. To evaluate the proposed approach, experiments have been conducted on real-world knowledge graphs. The experimental results demonstrate the effectiveness of the proposed approach.