Extracting Temporal Patterns From Large-Scale Text Corpus

Yu Liu, Wen Hua, Xiaofang Zhou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Knowledge, in practice, is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of designing temporal patterns, i.e., indicating phrases and their temporal meanings, for temporal knowledge harvesting. However, pattern design is extremely laborious and time consuming even for a single relation. Therefore, in this work, we study the problem of temporal pattern extraction by automatically analysing a large-scale text corpus with a small number of seed temporal facts. The problem is challenging considering the ambiguous nature of natural language and the huge amount of documents we need to analyse in order to obtain highly representative temporal patterns. To this end, we introduce various techniques, including corpus annotation, pattern generation, scoring and clustering, to reduce ambiguity in the text corpus and improve both accuracy and coverage of the extracted patterns. We conduct extensive experiments on real world datasets and the experimental results verify the effectiveness of our proposals.

Original languageEnglish
Title of host publicationDatabases Theory and Applications - 30th Australasian Database Conference, ADC 2019, Proceedings
EditorsLijun Chang, Xin Cao, Junhao Gan
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030120788
Publication statusPublished - 2019
Externally publishedYes
Event30th Australasian Database Conference, ADC 2019 - Sydney, Australia
Duration: 29 Jan 20191 Feb 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11393 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th Australasian Database Conference, ADC 2019


  • Temporal knowledge harvesting
  • Temporal patterns
  • Text mining

ASJC Scopus subject areas

  • Information Systems


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