1,721,104 research outputs found
DFMKE: A dual fusion multi-modal knowledge graph embedding framework for entity alignment
No full textEntity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal knowledge graph embedding framework, to address entity alignment. We first devise an early fusion method for fusing features of multi-modal entity representations of a KG. Simultaneously, multiple representations of various types of knowledge are generated independently by various techniques and fused by a low-rank multi-modal late fusion method. Finally, the outputs of early and late fusion methods are combined using a dual fusion scheme. DFMKE provides an ultimate fusion solution by leveraging the advantages of early and late fusion methods. Extensive experiments on two public datasets show that the DFMKE outperforms state-of-the-art methods by a significant margin achieving at least 10% more regard to Hits@n and MRR metrics
A conceptual framework and an underlying model for community detection and management in a Social Internetworking scenario
No full textRecommendation of similar users, resources and social networks in a Social Internetworking Scenario
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Recommendation of reliable users, social networks and high-quality resources in a Social Internetworking System
No full textA conceptual framework for community detection, characterization and membership in a Social Internetworking scenario
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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