The Experts below are selected from a list of 46215 Experts worldwide ranked by ideXlab platform
Alexander G. Hauptmann - One of the best experts on this subject based on the ideXlab platform.
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Learning Distilled Graph for Large-Scale Social Network Data Clustering
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Wenhe Liu, Dong Gong, Mingkui Tan, Javen Shi, Yi Yang, Alexander G. HauptmannAbstract:Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the Graph Construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting Graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for Graph Construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting Graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled Graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate Graph initialized with link data; in Graph distillation step, the proposed method updates and refines the Graph based on only the selected features. The final resulting Graph, which is referred to as the distilled Graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
Wenhe Liu - One of the best experts on this subject based on the ideXlab platform.
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Learning Distilled Graph for Large-Scale Social Network Data Clustering
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Wenhe Liu, Dong Gong, Mingkui Tan, Javen Shi, Yi Yang, Alexander G. HauptmannAbstract:Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the Graph Construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting Graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for Graph Construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting Graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled Graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate Graph initialized with link data; in Graph distillation step, the proposed method updates and refines the Graph based on only the selected features. The final resulting Graph, which is referred to as the distilled Graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
Diego Reforgiato Recupero - One of the best experts on this subject based on the ideXlab platform.
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mining scholarly publications for scientific knowledge Graph Construction
European Semantic Web Conference, 2019Co-Authors: Davide Buscaldi, Danilo Dessi, Enrico Motta, Francesco Osborne, Diego Reforgiato RecuperoAbstract:In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, and (iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships in the field of Semantic Web.
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mining scholarly data for fine grained knowledge Graph Construction
Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019) at ESWC2019, 2019Co-Authors: Davide Buscaldi, Danilo Dessi, Enrico Motta, Francesco Osborne, Diego Reforgiato RecuperoAbstract:Knowledge Graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques.
Haisheng Li - One of the best experts on this subject based on the ideXlab platform.
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a relationship extraction method for domain knowledge Graph Construction
World Wide Web, 2020Co-Authors: Haoze Yu, Haisheng LiAbstract:As a semantic knowledge base, knowledge Graph is a powerful tool for managing large-scale knowledge consists with instances, concepts and relationships between them. In view that the existing domain knowledge Graphs can not obtain relationships in various structures through targeted approaches in the process of Construction which resulting in insufficient knowledge utilization, this paper proposes a relationship extraction method for domain knowledge Graph Construction. We obtain upper and lower relationships from structured data in the classification system of network encyclopedia and semi-structured data in the classification labels of web pages, and non-superordinate relationships are extracted from unstructured text through the proposed convolution residual network based on improved cross-entropy loss function. We verify the effectiveness of the designed method by comparing with existing relationship extraction methods and constructing a food domain knowledge Graph.
Javen Shi - One of the best experts on this subject based on the ideXlab platform.
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Learning Distilled Graph for Large-Scale Social Network Data Clustering
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Wenhe Liu, Dong Gong, Mingkui Tan, Javen Shi, Yi Yang, Alexander G. HauptmannAbstract:Spectral analysis is critical in social network analysis. As a vital step of the spectral analysis, the Graph Construction in many existing works utilizes content data only. Unfortunately, the content data often consists of noisy, sparse, and redundant features, which makes the resulting Graph unstable and unreliable. In practice, besides the content data, social network data also contain link information, which provides additional information for Graph Construction. Some of previous works utilize the link data. However, the link data is often incomplete, which makes the resulting Graph incomplete. To address these issues, we propose a novel Distilled Graph Clustering (DGC) method. It pursuits a distilled Graph based on both the content data and the link data. The proposed algorithm alternates between two steps: in the feature selection step, it finds the most representative feature subset w.r.t. an intermediate Graph initialized with link data; in Graph distillation step, the proposed method updates and refines the Graph based on only the selected features. The final resulting Graph, which is referred to as the distilled Graph, is then utilized for spectral clustering on the large-scale social network data. Extensive experiments demonstrate the superiority of the proposed method.
