Class-based n-gram models of natural language
Computational Linguistics
S-CREAM - Semi-automatic CREAtion of Metadata
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Combining concept hierarchies and statistical topic models
Proceedings of the 17th ACM conference on Information and knowledge management
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Combining concept hierarchies and statistical topic models
Proceedings of the 17th ACM conference on Information and knowledge management
Learning Semantic Query Suggestions
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Latent topic feedback for information retrieval
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Getting the meaning right: a complementary distributional layer for the web semantics
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Mapping queries to the Linking Open Data cloud: A case study using DBpedia
Web Semantics: Science, Services and Agents on the World Wide Web
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
SSHLDA: a semi-supervised hierarchical topic model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hierarchical topic integration through semi-supervised hierarchical topic modeling
Proceedings of the 21st ACM international conference on Information and knowledge management
Collaborative filtering by analyzing dynamic user interests modeled by taxonomy
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
DASISH: An Initiative for a European Data Humanities Infrastructure
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Context-dependent conceptualization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. The methodology we propose is based on applications of statistical topic models (also known as latent Dirichlet allocation models). We demonstrate the utility of this general framework in two ways. We first illustrate how the methodology can be used to automatically tag Web pages with concepts from a known set of concepts without any need for labeled documents. We then perform a series of experiments that quantify how combining human-defined semantic knowledge with data-driven techniques leads to better language models than can be obtained with either alone.