Automatically Generating a Concept Hierarchy with Graphs

Pucktada Treeratpituk, Madian Khabsa, C. Lee Giles

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

Abstract

We propose a novel graph-based approach for constructing concept hierarchy from a large text corpus. Our algorithm incorporates both statistical co-occurrences and lexical similarity in optimizing the structure of the taxonomy. To automatically generate topic-dependent taxonomies from a large text corpus, we first extracts topical terms and their relationships from the corpus. The algorithm then constructs a weighted graph representing topics and their associations. A graph partitioning algorithm is then used to recursively partition the topic graph into a taxonomy. For evaluation, we apply our approach to articles, primarily computer science, in the CiteSeerX digital library and search engine.

Original languageEnglish (US)
Title of host publicationJCDL 2015 - Proceedings of the 15th ACM/IEEE-CE Joint Conference on Digital Libraries
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages265-266
Number of pages2
ISBN (Electronic)9781450335942
DOIs
StatePublished - Jun 21 2015
Event15th ACM/IEEE-CE Joint Conference on Digital Libraries, JCDL 2015 - Knoxville, United States
Duration: Jun 21 2015Jun 25 2015

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2015-June
ISSN (Print)1552-5996

Other

Other15th ACM/IEEE-CE Joint Conference on Digital Libraries, JCDL 2015
CountryUnited States
CityKnoxville
Period6/21/156/25/15

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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