To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities...Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities of enterprise architecture(EA)in interrelating different business/IT viewpoints and elements,the development of EA is superior to support BITA measurement.Extant BITA measurement literature is sparse when it concerns EA.The literature tends to explain how EA viewpoints or models correlate with BITA,without discussing where to collect and integrate EA data.To address this gap,this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework:DoD Architectural Framework 2.0(DoDAF2.0).The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0.An illustrative ArchiSurance case is conducted to explain the measurement process.Systematically,this paper explores the process of BITA measurement from the viewpoint of EA,which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.展开更多
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 system...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.展开更多
Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fa...Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fail to fully leverage the structure information of KGs for entity alignment.Therefore,our goal is to thoroughly explore the features of entity neighbors and relationships to obtain better entity embeddings.In this work,we propose DCEA,an effective dual-context representation learning framework for entity alignment.Specifically,the neighbor-level embedding module introduces relation information to more accurately aggregate neighbor context.The relation-level embedding module utilizes neighbor context to enhance relation-level embeddings.To eliminate semantic gaps between neighbor-level and relation-level embeddings,and fully exploit their complementarity,we design a hybrid embedding fusion model that adaptively performs embedding fusion to obtain powerful joint entity embeddings.We also jointly optimize the contrastive loss of multi-level embeddings,enhancing their mutual reinforcement while preserving the characteristics of neighbor and relation embeddings.Additionally,the decision fusion module combines the similarity scores calculated between entities based on embeddings at different levels to make the final alignment decision.Extensive experimental results on public datasets indicate that our DCEA performs better than state-of-the-art baselines.展开更多
Enterprise architecture(EA) development is always a superior way to address business-IT alignment(BITA) issue.However, most EA design frameworks are inadequate to allocate IT resources, which is an important metric of...Enterprise architecture(EA) development is always a superior way to address business-IT alignment(BITA) issue.However, most EA design frameworks are inadequate to allocate IT resources, which is an important metric of BITA maturity. Under this situation, the idea of IT resource allocation is combined with the EA design process, in order to extend prior EA research on BITA and to demonstrate EA's capability of implementing IT governance. As an effective resource allocation method, portfolio decision analysis(PDA) is used to align business functions of business architecture and applications of system architecture. Furthermore, this paper exhibits an illustrative case with the proposed framework.展开更多
The complexity of business and information systems(IS)alignment is a growing concern for researchers and practitioners alike.The extant research on alignment architecture fails to consider the human viewpoint,which ma...The complexity of business and information systems(IS)alignment is a growing concern for researchers and practitioners alike.The extant research on alignment architecture fails to consider the human viewpoint,which makes it difficult to embrace emergent complexity.This paper contributes to the extant literature in the following ways.First,we combine an enterprise architecture(EA)framework with a human viewpoint to address alignment issues in the architecture design phase;second,we describe a dynamic alignment model by developing a humancentered meta-model that explains first-and second-order changes and their effects on alignment evolution.This paper provides better support for the theoretical research and the practical application of dynamic alignment.展开更多
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
基金supported by the National Natural Science Foundation of China(71571189)the State Key Laboratory of Air Traffic Management System and Technology(SKLATM201806)
文摘Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities of enterprise architecture(EA)in interrelating different business/IT viewpoints and elements,the development of EA is superior to support BITA measurement.Extant BITA measurement literature is sparse when it concerns EA.The literature tends to explain how EA viewpoints or models correlate with BITA,without discussing where to collect and integrate EA data.To address this gap,this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework:DoD Architectural Framework 2.0(DoDAF2.0).The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0.An illustrative ArchiSurance case is conducted to explain the measurement process.Systematically,this paper explores the process of BITA measurement from the viewpoint of EA,which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.
基金supported by the National Key R&D Program of China(2018AAA0101502)the Science and Technology Project of SGCC(State Grid Corporation of China):Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control。
文摘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.
基金supported by the“pioneer”and“Leading Goose”Key R&D Program of Zhejiang Province under Grant No.2022C03106the Zhejiang Provincial Natural Science Foundation of China under Grant No.LY23F020010the National Natural Science Foundation of China under Grant No.62077015.
文摘Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fail to fully leverage the structure information of KGs for entity alignment.Therefore,our goal is to thoroughly explore the features of entity neighbors and relationships to obtain better entity embeddings.In this work,we propose DCEA,an effective dual-context representation learning framework for entity alignment.Specifically,the neighbor-level embedding module introduces relation information to more accurately aggregate neighbor context.The relation-level embedding module utilizes neighbor context to enhance relation-level embeddings.To eliminate semantic gaps between neighbor-level and relation-level embeddings,and fully exploit their complementarity,we design a hybrid embedding fusion model that adaptively performs embedding fusion to obtain powerful joint entity embeddings.We also jointly optimize the contrastive loss of multi-level embeddings,enhancing their mutual reinforcement while preserving the characteristics of neighbor and relation embeddings.Additionally,the decision fusion module combines the similarity scores calculated between entities based on embeddings at different levels to make the final alignment decision.Extensive experimental results on public datasets indicate that our DCEA performs better than state-of-the-art baselines.
基金supported by the National Natural Science Foundation of China(71571189)
文摘Enterprise architecture(EA) development is always a superior way to address business-IT alignment(BITA) issue.However, most EA design frameworks are inadequate to allocate IT resources, which is an important metric of BITA maturity. Under this situation, the idea of IT resource allocation is combined with the EA design process, in order to extend prior EA research on BITA and to demonstrate EA's capability of implementing IT governance. As an effective resource allocation method, portfolio decision analysis(PDA) is used to align business functions of business architecture and applications of system architecture. Furthermore, this paper exhibits an illustrative case with the proposed framework.
文摘The complexity of business and information systems(IS)alignment is a growing concern for researchers and practitioners alike.The extant research on alignment architecture fails to consider the human viewpoint,which makes it difficult to embrace emergent complexity.This paper contributes to the extant literature in the following ways.First,we combine an enterprise architecture(EA)framework with a human viewpoint to address alignment issues in the architecture design phase;second,we describe a dynamic alignment model by developing a humancentered meta-model that explains first-and second-order changes and their effects on alignment evolution.This paper provides better support for the theoretical research and the practical application of dynamic alignment.
