One-class order embedding for dependency relation prediction

Meng Fen Chiang, Ee Peng Lim, Wang Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo

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

Abstract

Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.

Original languageEnglish (US)
Title of host publicationSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages205-214
Number of pages10
ISBN (Electronic)9781450361729
DOIs
StatePublished - Jul 18 2019
Event42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 - Paris, France
Duration: Jul 21 2019Jul 25 2019

Publication series

NameSIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019
CountryFrance
CityParis
Period7/21/197/25/19

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics
  • Software

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  • Cite this

    Chiang, M. F., Lim, E. P., Lee, W. C., Ashok, X. J. S., & Prasetyo, P. K. (2019). One-class order embedding for dependency relation prediction. In SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 205-214). (SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3331184.3331249