On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ontology acquisition and semantic retrieval from semantic annotated chinese poetry
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Scientific research ontology to support systematic review in software engineering
Advanced Engineering Informatics
From artifacts to aggregations: Modeling scientific life cycles on the semantic Web
Journal of the American Society for Information Science and Technology
The anatomy of a nanopublication
Information Services and Use - Selected Papers from the ICSTI Interactive Publications Conference 2010
An ontological representation of the digital library evaluation domain
Journal of the American Society for Information Science and Technology
GoNTogle: a tool for semantic annotation and search
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
An exploration of the research trends in the digital library evaluation domain
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Ontological formalization of scientific experiments based on core scientific metadata model
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
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The digital library evaluation field has an evolving nature and it is characterized by a noteworthy proclivity to enfold various methodological orientations. Given the fact that the scientific literature in the specific domain is vast, researchers require tools that will exhibit either commonly acceptable practices, or areas for further investigation. In this paper, a data mining methodology is proposed to identify prominent patterns in the evaluation of digital libraries. Using Machine Learning techniques, all papers presented in the ECDL and JCDL conferences between the years 2001 and 2011 were categorized as relevant or non-relevant to the DL evaluation domain. Then, the relevant papers were semantically annotated according to the Digital Library Evaluation Ontology (DiLEO) vocabulary. The produced set of annotations was clustered to evaluation patterns for the most frequently used tools, methods and goals of the domain. Our findings highlight the expressive nature of DiLEO, place emphasis on semantic annotation as a necessary step in handling domain-centric corpora and underline the potential of the proposed methodology in the profiling of evaluation activities.