| 1. |
- Frasconi, Paolo, et al.
(författare)
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kLog : A language for logical and relational learning with kernels
- 2014
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Ingår i: Artificial Intelligence. - Amsterdam : Elsevier. - 0004-3702 .- 1872-7921. ; 217, s. 117-143
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Tidskriftsartikel (refereegranskat)abstract
- We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph — in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.
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| 2. |
- Frasconi, Paolo, et al.
(författare)
-
kLog : A language for logical and relational learning with kernels
- 2015
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Ingår i: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015). - : AAAI Press. - 9781577357384 ; , s. 4183-4187
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Konferensbidrag (refereegranskat)abstract
- We introduce kLog, a novel language for kernel-based learning on expressive logical and relational representations. kLog allows users to specify logical and relational learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, and logic programming. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph - in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. An empirical evaluation shows that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it.db.ub.oru.se along with tutorials.
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| 3. |
- Verbeke, Mathias, et al.
(författare)
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kLogNLP : Graph Kernel–based Relational Learning of Natural Language
- 2014
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Ingår i: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. - : Association for Computational Linguistics. - 9781941643006 ; , s. 85-90
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Konferensbidrag (refereegranskat)abstract
- kLog is a framework for kernel-basedlearning that has already proven success-ful in solving a number of relational tasksin natural language processing. In this pa-per, we presentkLogNLP, a natural lan-guage processing module for kLog. Thismodule enriches kLog with NLP-specificpreprocessors, enabling the use of exist-ing libraries and toolkits within an elegantand powerful declarative machine learn-ing framework. The resulting relationalmodel of the domain can be extended byspecifying additional relational features ina declarative way using a logic program-ming language. This declarative approachoffers a flexible way of experimentationand a way to insert domain knowledge.
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