research-article

Constructing and Mining Web-scale Knowledge Graphs

Authors:

Evgeniy Gabrilovich,

Nicolas UsunierAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 1195 - 1197

https://doi.org/10.1145/2911451.2914807

Published: 07 July 2016 Publication History

Abstract

Recent years have witnessed a proliferation of large-scale knowledge graphs, from purely academic projects such as YAGO to major commercial projects such as Google's Knowledge Graph and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of those types (graph vertices and edges). In this tutorial, we present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, open research challenges, as well as pointers to available datasets and relevant literature. Knowledge graphs have become an enabling resource for a plethora of new knowledge-rich applications. Consequently, the tutorial will also discuss the role of knowledge bases in empowering a range of web applications, from web search to social networks to digital assistants. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.

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Cited By

Le TLe NLe B(2023)Knowledge graph embedding by relational rotation and complex convolution for link predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119122214:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119122
Zhang HZhong HBai WPan F(2019)Cross-platform rating prediction method based on review topicFuture Generation Computer Systems10.1016/j.future.2019.06.021101:C(236-245)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1016/j.future.2019.06.021
Charles VManguinhas HIsaac AFreire NGordea SBaptista AWalk P(2018)Designing a multilingual knowledge graph as a service for cultural heritageProceedings of the 2018 International Conference on Dublin Core and Metadata Applications10.5555/3308533.3308538(29-40)Online publication date: 10-Sep-2018
https://dl.acm.org/doi/10.5555/3308533.3308538
Show More Cited By

Index Terms

Constructing and Mining Web-scale Knowledge Graphs
1. Information systems
  1. Data management systems
    1. Information integration
  2. Information systems applications

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Published In

cover image ACM Conferences

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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knowledge graphs

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SIGIR '16

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Le TLe NLe B(2023)Knowledge graph embedding by relational rotation and complex convolution for link predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119122214:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119122
Zhang HZhong HBai WPan F(2019)Cross-platform rating prediction method based on review topicFuture Generation Computer Systems10.1016/j.future.2019.06.021101:C(236-245)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1016/j.future.2019.06.021
Charles VManguinhas HIsaac AFreire NGordea SBaptista AWalk P(2018)Designing a multilingual knowledge graph as a service for cultural heritageProceedings of the 2018 International Conference on Dublin Core and Metadata Applications10.5555/3308533.3308538(29-40)Online publication date: 10-Sep-2018
https://dl.acm.org/doi/10.5555/3308533.3308538
Brandizi MSingh ARawlings CHassani-Pak K(2018)Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database ApproachJournal of Integrative Bioinformatics10.1515/jib-2018-002315:3Online publication date: 7-Aug-2018
https://doi.org/10.1515/jib-2018-0023

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