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Acquiring temporal constraints between relations

Published: 29 October 2012 Publication History

Abstract

We consider the problem of automatically acquiring knowledge about the typical temporal orderings among relations (e.g., actedIn(person, film) typically occurs before wonPrize (film, award)), given only a database of known facts (relation instances) without time information, and a large document collection. Our approach is based on the conjecture that the narrative order of verb mentions within documents correlates with the temporal order of the relations they represent. We propose a family of algorithms based on this conjecture, utilizing a corpus of 890m dependency parsed sentences to obtain verbs that represent relations of interest, and utilizing Wikipedia documents to gather statistics on narrative order of verb mentions. Our proposed algorithm, GraphOrder, is a novel and scalable graph-based label propagation algorithm that takes transitivity of temporal order into account, as well as these statistics on narrative order of verb mentions. This algorithm achieves as high as 38.4% absolute improvement in F1 over a random baseline. Finally, we demonstrate the utility of this learned general knowledge about typical temporal orderings among relations, by showing that these temporal constraints can be successfully used by a joint inference framework to assign specific temporal scopes to individual facts.

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    cover image ACM Conferences
    CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
    October 2012
    2840 pages
    ISBN:9781450311564
    DOI:10.1145/2396761
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    Published: 29 October 2012

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    Author Tags

    1. graph-based semi-supervised learning
    2. knowledge bases
    3. label propagation
    4. narrative ordering
    5. temporal ordering
    6. temporal scoping

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    • (2023)Dynamic Controllability of Parameterized CSTNUsProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577618(965-973)Online publication date: 27-Mar-2023
    • (2022)Learning Temporal Task Models from Human Bimanual Demonstrations2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS47612.2022.9981068(7664-7671)Online publication date: 23-Oct-2022
    • (2019)Time-Aware Knowledge Graphs for Decision Making in the Building IndustryDecision Support Systems IX: Main Developments and Future Trends10.1007/978-3-030-18819-1_5(57-69)Online publication date: 14-Apr-2019
    • (2018)Temporal Evolution Data Model for Heterogeneous Entities: Modeling with Temporal and Evolution InformationCloud Computing and Security10.1007/978-3-030-00009-7_19(202-211)Online publication date: 21-Sep-2018
    • (2015)VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2015.7298752(1456-1464)Online publication date: Jun-2015
    • (2015)DeFacto-Temporal and multilingual Deep Fact ValidationWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2015.08.00135:P2(85-101)Online publication date: 1-Dec-2015
    • (2014)Graph-Based Semi-Supervised LearningSynthesis Lectures on Artificial Intelligence and Machine Learning10.2200/S00590ED1V01Y201408AIM0298:4(1-125)Online publication date: 31-Jul-2014
    • (2014)Semantic culturomicsProceedings of the VLDB Endowment10.14778/2732977.27329947:12(1215-1218)Online publication date: 1-Aug-2014
    • (2014)Predicting Object Dynamics in ScenesProceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2014.260(2027-2034)Online publication date: 23-Jun-2014
    • (2014)Tackling representation, annotation and classification challenges for temporal knowledge base populationKnowledge and Information Systems10.1007/s10115-013-0675-141:3(611-646)Online publication date: 1-Dec-2014
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