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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Veröffentlicht in:Machine learning Jg. 108; H. 7; S. 1165 - 1189
Hauptverfasser: Guimarães, Victor, Paes, Aline, Zaverucha, Gerson
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2019
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Science
Control
Data transmission
Digital media
Distance learning
Knowledge management
Machine learning
Mechatronics
Natural Language Processing (NLP)
Robotics
Simulation and Modeling
Social networks
Special Issue of the Inductive Logic Programming (ILP) 2017-2018
Statistical analysis
Mining data streams
Statistical relational learning
Inductive logic programming
Online learning
Theory revision from examples
ISSN:0885-6125, 1573-0565
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Zusammenfassung: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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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-019-05798-y