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Using neural networks to aggregate Linked Data rules

Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico (2014). Using neural networks to aggregate Linked Data rules. In: Knowledge Engineering and Knowledge Management, Lecture Notes in Computer Science, Springer International Publishing, pp. 547–562.

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Two typical problems are encountered after obtaining a set of rules from a data mining process: (i) their number can be extremely large and (ii) not all of them are interesting to be considered. Both manual and automatic strategies trying to overcome those problems have to deal with technical issues such as time costs and computational complexity. This work is an attempt to address the quantity and quality issues through using a Neural Network model for predicting the quality of Linked Data rules. Our motivation comes from our previous work, in which we obtained large sets of atomic rules through an inductive logic inspired process traversing Linked Data. Assuming a limited amount of resources, and therefore the impossibility of trying every possible combination to obtain a better rule representing a subset of items, the major issue becomes detecting the combinations that will produce the best rule in the shortest time. Therefore, we propose to use a Neural Network to learn directly from the rules how to recognise a promising aggregation. Our experiments show that including a Neural Network-based prediction model in a rule aggregation process significantly reduces the amount of resources (time and space) required to produce high-quality rules.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 Springer International Publishing Switzerland
ISBN: 3-319-13703-4, 978-3-319-13703-2
ISSN: 0302-9743
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
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Item ID: 41687
Depositing User: Kay Dave
Date Deposited: 09 Jan 2015 09:35
Last Modified: 09 May 2019 16:56
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