Online probabilistic theory revision from examples with ProPPR
Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examp...
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| Published in: | Machine learning Vol. 108; no. 7; pp. 1165 - 1189 |
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| Main Authors: | , , |
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| Language: | English |
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01.07.2019
Springer Nature B.V |
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examples are independent along the iterations. Thus, most relational learning systems are still designed to learn only from closed batches of data. Furthermore, in case there is a previously acquired model, these systems either would discard it or assuming it as correct. In this work, we propose an online relational learning algorithm that can handle continuous, open-ended streams of relational examples as they arrive. We employ techniques of theory revision to take advantage of the previously acquired model as a starting point, by finding where it should be modified to cope with the new examples, and automatically update it. We rely on the Hoeffding’s bound statistical theory to decide if the model must, in fact, be updated in accordance with the new examples. The proposed algorithm is built upon ProPPR statistical relational language, aiming at contemplating the uncertainty inherent to real data. Experimental results in social networks and entity co-reference datasets show the potential of the proposed approach compared to other relational learners. |
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| AbstractList | Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examples are independent along the iterations. Thus, most relational learning systems are still designed to learn only from closed batches of data. Furthermore, in case there is a previously acquired model, these systems either would discard it or assuming it as correct. In this work, we propose an online relational learning algorithm that can handle continuous, open-ended streams of relational examples as they arrive. We employ techniques of theory revision to take advantage of the previously acquired model as a starting point, by finding where it should be modified to cope with the new examples, and automatically update it. We rely on the Hoeffding’s bound statistical theory to decide if the model must, in fact, be updated in accordance with the new examples. The proposed algorithm is built upon ProPPR statistical relational language, aiming at contemplating the uncertainty inherent to real data. Experimental results in social networks and entity co-reference datasets show the potential of the proposed approach compared to other relational learners. |
| Author | Guimarães, Victor Paes, Aline Zaverucha, Gerson |
| Author_xml | – sequence: 1 givenname: Victor surname: Guimarães fullname: Guimarães, Victor organization: Department of Systems Engineering and Computer Science, COPPE, Universidade Federal do Rio de Janeiro (UFRJ) – sequence: 2 givenname: Aline orcidid: 0000-0002-9089-7303 surname: Paes fullname: Paes, Aline email: alinepaes@ic.uff.br organization: Department of Computer Science, Universidade Federal Fluminense (UFF) – sequence: 3 givenname: Gerson surname: Zaverucha fullname: Zaverucha, Gerson organization: Department of Systems Engineering and Computer Science, COPPE, Universidade Federal do Rio de Janeiro (UFRJ) |
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| Cites_doi | 10.1007/s10994-016-5595-3 10.1007/s10994-016-5596-2 10.2307/2981683 10.1021/ci700418f 10.1145/347090.347107 10.7551/mitpress/1192.001.0001 10.1007/11536314_18 10.1023/A:1009867806624 10.1007/978-3-540-68856-3 10.1016/S0004-3702(00)00004-7 10.1145/1529282.1529610 10.1109/ICDM.2006.70 10.1007/s10994-011-5244-9 10.1007/s10994-015-5481-4 10.1007/BF03037227 10.1007/s10994-009-5116-8 10.1093/logcom/exx015 10.1080/01621459.1963.10500830 10.1007/s10994-006-5833-1 10.2200/S00692ED1V01Y201601AIM032 10.1016/0743-1066(94)90035-3 10.1145/872734.806939 10.1145/1117454.1117456 10.1016/S0004-3702(98)00034-4 10.1109/FOCS.2006.44 10.1007/s10994-015-5488-x |
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| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 Machine Learning is a copyright of Springer, (2019). All Rights Reserved. |
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| Keywords | Mining data streams Statistical relational learning Inductive logic programming Online learning Theory revision from examples |
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| References_xml | – reference: MuggletonSDe RaedtLInductive logic programming: Theory and methodsJournal of Logic Programming1994192062967912799360816.6804310.1016/0743-1066(94)90035-3 – reference: BlockeelHDe RaedtLJacobsNDemoenBScaling up inductive logic programming by learning from interpretationsData Mining and Knowledge Discovery199931599310.1023/A:1009867806624 – reference: PaesAZaveruchaGCostaVSOn the use of stochastic local search techniques to revise first-order logic theories from examplesMachine Learning2017106219724135968780673782010.1007/s10994-016-5595-3 – reference: DriesADe RaedtLTowards clausal discovery for stream mining2010BerlinSpringer916 – reference: MurphyKPMachine learning: A probabilistic perspective2012CambridgeMIT Press1295.68003 – reference: DubocALPaesAZaveruchaGUsing the bottom clause and modes declarations on FOL theory revision from examplesMachine Learning2009761731071156.6852610.1007/s10994-009-5116-8 – reference: RichardsonMDomingosPMarkov logic networksMachine Learning200662110713610.1007/s10994-006-5833-1 – reference: DawidAPPresent position and potential developments: Some personal views: Statistical theory: The prequential approachJournal of the Royal Statistical Society Series A (General)1984147227829276381110.2307/2981683 – reference: Andersen, R., Chung, F., & Lang, K. 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