Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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 o ur 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.

Semantic-aware entity alignment for low resource language knowledge graph

Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for EA.Most EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language models.However,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource KGs.Recently,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often ignored.To address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity alignment.First,we generate pseudo sentences according to the relation triples and produce representations using pre-trained models.Second,our approach explores semantic information from the connected relations by a graph neural network.Our model captures expanded feature information from KGs.Experimental results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.

MHGCN:Multiview Highway Graph Convolutional Network for Cross-Lingual Entity Alignment

Knowledge graphs(KGs)provide a wealth of prior knowledge for the research on social networks.Crosslingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies.Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs.However,these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs.In this paper,we propose a new embedding-based framework named multiview highway graph convolutional network(MHGCN),which considers the entity alignment from the views of entity semantic,relation semantic,and entity attribute.To learn the structural features of an entity,the MHGCN employs a highway graph convolutional network(GCN)for entity embedding in each view.In addition,the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding.The alignment entities are identified based on the similarity of entity embeddings.The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods.The research also will benefit knowledge fusion through cross-lingual KG entity alignment.

An integrated pipeline model for biomedical entity alignment

Biomedical entity alignment,composed of two subtasks:entity identification and entity-concept mapping,is of great research value in biomedical text mining while these techniques are widely used for name entity standardization,information retrieval,knowledge acquisition and ontology construc-tion.Previous works made many efforts on feature engineering to employ feature-based models for entity identification and alignment.However,the models depended on subjective feature selection may suffer error propagation and are not able to uti-lize the hidden information.With rapid development in health-related research,researchers need an effective method to explore the large amount of available biomedical literatures.Therefore,we propose a two-stage entity alignment process,biomedical entity exploring model,to identify biomedical entities and align them to the knowledge base interactively.The model aims to automatically obtain semantic information for extracting biomedical entities and mining semantic relations through the standard biomedical knowledge base.The experiments show that the proposed method achieves better performance on entity alignment.The proposed model dramatically improves the FI scores of the task by about 4.5%in entity identification and 2.5%in entity-concept mapping.