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NewsNetExplorer: automatic construction and exploration of news information networks

Authors:

Heng Ji,

Yizhou SunAuthors Info & Claims

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

Pages 1091 - 1094

https://doi.org/10.1145/2588555.2594537

Published: 18 June 2014 Publication History

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Abstract

News data is one of the most abundant and familiar data sources. News data can be systematically utilized and ex- plored by database, data mining, NLP and information re- trieval researchers to demonstrate to the general public the power of advanced information technology. In our view, news data contains rich, inter-related and multi-typed data objects, forming one or a set of gigantic, interconnected, het- erogeneous information networks. Much knowledge can be derived and explored with such an information network if we systematically develop effective and scalable data-intensive information network analysis technologies. By further developing a set of information extraction, in- formation network construction, and information network mining methods, we extract types, topical hierarchies and other semantic structures from news data, construct a semi- structured news information network NewsNet. Further, we develop a set of news information network exploration and mining mechanisms that explore news in multi-dimensional space, which include (i) OLAP-based operations on the hierarchical dimensional and topical structures and rich-text, such as cell summary, single dimension analysis, and promo- tion analysis, (ii) a set of network-based operations, such as similarity search and ranking-based clustering, and (iii) a set of hybrid operations or network-OLAP operations, such as entity ranking at different granularity levels. These form the basis of our proposed NewsNetExplorer system. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses multiple challenging research problems. Moreover, some functions are our on-going research tasks. By integrating these functions, NewsNetExplorer not only provides with us insightful recommendations in NewsNet exploration system but also helps us gain insight on how to perform effective information extraction, integration and mining in large unstructured datasets.

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Cited By

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Kim HEl-Kishky ARen XHan J(2019)Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006049(105-114)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006049
Wu JYu LZhang QShi PLiu LDeng SHuang H(2018)Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor DecompositionComplexity10.1155/2018/96534042018(38)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/9653404
Patel HParaskevopoulos PRenz M(2018)Data Fusion of Diverse Data SourcesProceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3210272.3210275(13-18)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3210272.3210275
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Index Terms

NewsNetExplorer: automatic construction and exploration of news information networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

June 2014

1645 pages

ISBN:9781450323765

DOI:10.1145/2588555

General Chairs:
Curtis Dyreson
Utah State University, USA
,
Feifei Li
University of Utah, USA
,
Program Chair:
M. Tamer Özsu
University of Waterloo, Canada

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2014

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SIGMOD/PODS'14

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SIGMOD

SIGMOD/PODS'14: International Conference on Management of Data

June 22 - 27, 2014

Utah, Snowbird, USA

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Kim HEl-Kishky ARen XHan J(2019)Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006049(105-114)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006049
Wu JYu LZhang QShi PLiu LDeng SHuang H(2018)Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor DecompositionComplexity10.1155/2018/96534042018(38)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/9653404
Patel HParaskevopoulos PRenz M(2018)Data Fusion of Diverse Data SourcesProceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3210272.3210275(13-18)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3210272.3210275
Shi CLi YZhang JSun YYu P(2017)A Survey of Heterogeneous Information Network AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.259856129:1(17-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2598561
Shi CYu PShi CYu P(2017)Prototype System Based on Heterogeneous NetworkHeterogeneous Information Network Analysis and Applications10.1007/978-3-319-56212-4_8(201-217)Online publication date: 26-May-2017
https://doi.org/10.1007/978-3-319-56212-4_8
Niu XZhang YHuang TWu X(2016)OSim: An OLAP-Based Similarity Search Service Solver for Dynamic Information NetworksWireless Algorithms, Systems, and Applications10.1007/978-3-319-42836-9_47(536-547)Online publication date: 4-Aug-2016
https://doi.org/10.1007/978-3-319-42836-9_47
Wang CHan J(2015)Mining Latent Entity StructuresSynthesis Lectures on Data Mining and Knowledge Discovery10.2200/S00625ED1V01Y201502DMK0107:1(1-159)Online publication date: 31-Mar-2015
https://doi.org/10.2200/S00625ED1V01Y201502DMK010

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