research-article

Representation learning for entity type ranking

Authors:

Md Mostafizur Rahman,

Atsuhiro Takasu,

Gianluca DemartiniAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 2049 - 2056

https://doi.org/10.1145/3341105.3374029

Published: 30 March 2020 Publication History

Abstract

The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context.

In this paper we address the entity type ranking problem by means of KG embedding models. In our work, we show that entity type ranking can be seen as a special case of the KG completion problem. Embeddings can be learned from both the structural and probabilistic information of the entities. We propose a Representation Learning model for Type Ranking (RL-TRank) and the results of the structure embedding and the probabilistic embedding are combined to get the entity type ranking. Experimental results show that the accuracy of RL-TRank approaches outperform the state-of-the-art type ranking models while, at the same time, being more efficient and scalable.

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Cited By

Rahman MKikuta DHirate YSuzumura THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph-Based Audience Expansion Model for Marketing CampaignsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661363(2970-2975)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661363
Wu BSong YChu JCao J(2022)Information Extraction for Knowledge Graph of ISO 19650 StandardsAdvances in Wireless Communications and Applications10.1007/978-981-19-3486-5_20(162-172)Online publication date: 24-Jun-2022
https://doi.org/10.1007/978-981-19-3486-5_20
Rahman MTakasu A(2020)Exploiting knowledge graph and text for ranking entity typesACM SIGAPP Applied Computing Review10.1145/3429204.342920720:3(35-46)Online publication date: 8-Oct-2020
https://dl.acm.org/doi/10.1145/3429204.3429207

Index Terms

Representation learning for entity type ranking
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Cited By

Rahman MKikuta DHirate YSuzumura THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph-Based Audience Expansion Model for Marketing CampaignsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661363(2970-2975)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661363
Wu BSong YChu JCao J(2022)Information Extraction for Knowledge Graph of ISO 19650 StandardsAdvances in Wireless Communications and Applications10.1007/978-981-19-3486-5_20(162-172)Online publication date: 24-Jun-2022
https://doi.org/10.1007/978-981-19-3486-5_20
Rahman MTakasu A(2020)Exploiting knowledge graph and text for ranking entity typesACM SIGAPP Applied Computing Review10.1145/3429204.342920720:3(35-46)Online publication date: 8-Oct-2020
https://dl.acm.org/doi/10.1145/3429204.3429207

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