IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v37y2019i1d10.1007_s10878-017-0225-y.html
   My bibliography  Save this article

Optimizing model parameter for entity summarization across knowledge graphs

Author

Listed:
  • Jihong Yan

    (Shanghai Polytechnic University)

  • Chen Xu

    (Shanghai Agricultural Committee)

  • Na Li

    (East China Normal University)

  • Ming Gao

    (East China Normal University)

  • Aoying Zhou

    (East China Normal University)

Abstract

Knowledge graphs, which belongs to the category of semantic networks, are considered as a new method of knowledge representation of health care data. It establishes a semantic explanation model for human perception and health care information processing. Each knowledge graph is composed of massive entities and relationships. However, it is an arduous work to search and visualize users’ interested entities and attributes since there are many attributes for an entity across different knowledge graphs. It is a natural problem how to summarize an entity based on multiple knowledge graphs. We propose a three-stage algorithm to solve the problem of entity summarization across knowledge graphs, including candidate generation, knowledge graph linkage, and entity summarization. We propose an unsupervised framework to link different knowledge graphs based on the semantic and structure information of entities. To further reduce the computational cost, we employ word embedding technique to find the similar entities in semantic, and filter some pairs of unmatched entities. Finally, we model entity summarization as personalized ranking problem in a knowledge graph. We conduct a set of experiments to evaluate our proposed method on four real datasets: historical data for user query, two English knowledge graphs (YAGO and DBpeida) and an English corpus. Experimental results demonstrate the effectiveness of our proposed method by comparing with baselines.

Suggested Citation

  • Jihong Yan & Chen Xu & Na Li & Ming Gao & Aoying Zhou, 2019. "Optimizing model parameter for entity summarization across knowledge graphs," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 293-318, January.
    • Handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0225-y
    DOI: 10.1007/s10878-017-0225-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-017-0225-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-017-0225-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jihong Yan & Wenliang Cheng & Chengyu Wang & Jun Liu & Ming Gao & Aoying Zhou, 2015. "Optimizing word set coverage for multi-event summarization," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 996-1015, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhihong Zhao & Beibei Li & Jun Liu, 2021. "Fitting PB1-p62 filaments model structure into its electron microscopy based on an improved genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 937-947, November.
    2. Wei Gao & Wuping Bao & Xin Zhou, 2019. "Analysis of cough detection index based on decision tree and support vector machine," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 375-384, January.
    3. Zhihong Zhao & Beibei Li & Jun Liu, 0. "Fitting PB1-p62 filaments model structure into its electron microscopy based on an improved genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0225-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.