Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
The Description Logic Handbook
The Description Logic Handbook
Variable-strength conditional preferences for ranking objects in ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
Scalable querying services over fuzzy ontologies
Proceedings of the 17th international conference on World Wide Web
Semantic matchmaking as non-monotonic reasoning: a description logic approach
Journal of Artificial Intelligence Research
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Kernel methods for mining instance data in ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Query answering and ontology population: an inductive approach
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Relational kernel machines for learning from graph-structured RDF data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Towards top-k query answering in description logics: the case of DL-Lite
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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In the context of semantic knowledge bases, we tackle the problem of ranking resources w.r.t. some criterion. The proposed solution is a method for learning functions that can approximately predict the correct ranking. Differently from other related methods proposed, that assume the ranking criteria to be explicitly expressed (e.g. as a query or a function), our approach is data-driven, being able to produce a predictor detecting the implicit underlying criteria from assertions regarding the resources in the knowledge base. The usage of specific kernel functions encoding the similarity between individuals in the context of knowledge bases allows the application of the method to ontologies in the standard representations for the Semantic Web. The method is based on a kernelized version of the Perceptron Ranking algorithm which is suitable for batch but also online problem settings. Moreover, differently from other approaches based on regression, the method takes advantage from the underlying ordering on the ranking labels. The reported empirical evaluation proves the effectiveness of the method at the task of predicting the rankings of single users in the Linked User Feedback dataset, by integrating knowledge from the Linked Open Data cloud during the learning process.