• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    p3130-pei.pdf
    Size:
    998.6Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Conference Paper
    Authors
    Pei, Shichao
    Yu, Lu
    Hoehndorf, Robert cc
    Zhang, Xiangliang cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    KAUST Grant Number
    FCC/1/1976-19-01
    Date
    2019-05-13
    Online Publication Date
    2019-05-13
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/656520
    
    Metadata
    Show full item record
    Abstract
    Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions: 1) labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used; and 2) knowledge graph embedding is affected by entity's degree difference, which brings challenges to align high frequent and low frequent entities. We propose a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment. Furthermore, we improve the knowledge graph embedding with awareness of the degree difference by performing the adversarial training. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy.
    Citation
    Pei, S., Yu, L., Hoehndorf, R., & Zhang, X. (2019). Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference. The World Wide Web Conference on - WWW ’19. doi:10.1145/3308558.3313646
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-19-01.
    Publisher
    Association for Computing Machinery, Inc
    Conference/Event name
    2019 World Wide Web Conference, WWW 2019
    DOI
    10.1145/3308558.3313646
    Additional Links
    http://dl.acm.org/citation.cfm?doid=3308558.3313646
    ae974a485f413a2113503eed53cd6c53
    10.1145/3308558.3313646
    Scopus Count
    Collections
    Conference Papers; Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.