Construction of structured heterogeneous networks from massive text data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Network data analytics is important, powerful, and exciting. How big role may network data analytics play in the real world? Much real-world data is unstructured, in the form of natural language text. A grand challenges on big data research is to develop effective and scalable methods to turn such massive text data into actionable knowledge. In order to turn such massive unstructured, text-rich, but interconnected data into knowledge, we propose a data-to-networkto-knowledge (D2N2K) paradigm, that is, first transform data into relatively structured heterogeneous information networks, and then mine such text-rich and structure-rich heterogeneous networks to generate useful knowledge. We argue that such a paradigm represents a promising direction and network data analytics will play an essential role in transforming data to knowledge. However, a critical bottleneck in this game is mining structures from text data. We present our recent progress on developing effective methods for mining structures from massive text data and constructing structured heterogeneous information networks.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd ACM SIGMOD Workshop on Network Data Analytics, NDA 2017
EditorsShourya Roy, Arnab Bhattacharya, Akhil Arora
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450349901
DOIs
StatePublished - May 14 2017
Event2nd ACM SIGMOD Workshop on Network Data Analytics, NDA 2017 - Chicago, United States
Duration: May 19 2017 → …

Publication series

NameProceedings of the 2nd ACM SIGMOD Workshop on Network Data Analytics, NDA 2017

Other

Other2nd ACM SIGMOD Workshop on Network Data Analytics, NDA 2017
CountryUnited States
CityChicago
Period5/19/17 → …

Keywords

  • Network mining
  • Structure mining from massive text data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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