A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement
Name disambiguation becomes increasingly important in information retrieval in the era of big data, while on how to further improve the accuracy of disambiguating duplicate names, the existing models are encountering many problems or facing many challenges, such as 1) how to capture and sufficiently...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8986661/ |
| _version_ | 1831679299944448000 |
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| author | Chunhui Deng Huifang Deng Chaoran Li |
| author_facet | Chunhui Deng Huifang Deng Chaoran Li |
| author_sort | Chunhui Deng |
| collection | DOAJ |
| description | Name disambiguation becomes increasingly important in information retrieval in the era of big data, while on how to further improve the accuracy of disambiguating duplicate names, the existing models are encountering many problems or facing many challenges, such as 1) how to capture and sufficiently use the global structure features of the network; 2) how to handle the complexity of feature data sets; 3) how to differentiate different types of feature relations, and 4) how to deal with the feature information missing etc. All these challenges are very important issues in further improving the accuracy. In order to address the above issues, this paper proposed a novel model which is called HRFAENE (Heterogeneous Relation Fusion and Attribute Enhanced Network Embedding Model). This model considers both feature network structure information (multiple relations) and document attribute features. The feature network structure information is represented by a scalable loss function which is designed based on pairwise constraints. The document attribute features are comprehensively extracted through multiple heterogeneous information networks which are constructed based on strong features. In order to better identify the disambiguation entity, this model uses weak features as node attributes in strong feature networks and iteratively learns network structure information and node attribute information. The experimental results show that the proposed model significantly outperforms the existing models in the disambiguation accuracy and has good stability, indicating that the model is very effective as expected and can be applied in reality. |
| first_indexed | 2024-12-20T05:18:15Z |
| format | Article |
| id | doaj.art-a5e22c0ad42f41cc82165453fe529c63 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:18:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a5e22c0ad42f41cc82165453fe529c632022-12-21T19:52:07ZengIEEEIEEE Access2169-35362020-01-018283752838410.1109/ACCESS.2020.29723728986661A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute EnhancementChunhui Deng0https://orcid.org/0000-0001-8297-9934Huifang Deng1https://orcid.org/0000-0001-6394-4496Chaoran Li2https://orcid.org/0000-0001-9546-2934School of Computer Engineering, Guangzhou College, South China University of Technology, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaName disambiguation becomes increasingly important in information retrieval in the era of big data, while on how to further improve the accuracy of disambiguating duplicate names, the existing models are encountering many problems or facing many challenges, such as 1) how to capture and sufficiently use the global structure features of the network; 2) how to handle the complexity of feature data sets; 3) how to differentiate different types of feature relations, and 4) how to deal with the feature information missing etc. All these challenges are very important issues in further improving the accuracy. In order to address the above issues, this paper proposed a novel model which is called HRFAENE (Heterogeneous Relation Fusion and Attribute Enhanced Network Embedding Model). This model considers both feature network structure information (multiple relations) and document attribute features. The feature network structure information is represented by a scalable loss function which is designed based on pairwise constraints. The document attribute features are comprehensively extracted through multiple heterogeneous information networks which are constructed based on strong features. In order to better identify the disambiguation entity, this model uses weak features as node attributes in strong feature networks and iteratively learns network structure information and node attribute information. The experimental results show that the proposed model significantly outperforms the existing models in the disambiguation accuracy and has good stability, indicating that the model is very effective as expected and can be applied in reality.https://ieeexplore.ieee.org/document/8986661/Name disambiguationscholar entity identificationnetwork representation modelheterogeneous relation fusionattribute enhanced network embeddingnode (document) attributes |
| spellingShingle | Chunhui Deng Huifang Deng Chaoran Li A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement IEEE Access Name disambiguation scholar entity identification network representation model heterogeneous relation fusion attribute enhanced network embedding node (document) attributes |
| title | A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement |
| title_full | A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement |
| title_fullStr | A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement |
| title_full_unstemmed | A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement |
| title_short | A Scholar Disambiguation Method Based on Heterogeneous Relation-Fusion and Attribute Enhancement |
| title_sort | scholar disambiguation method based on heterogeneous relation fusion and attribute enhancement |
| topic | Name disambiguation scholar entity identification network representation model heterogeneous relation fusion attribute enhanced network embedding node (document) attributes |
| url | https://ieeexplore.ieee.org/document/8986661/ |
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