Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Embedding knowledge in Web documents
WWW '99 Proceedings of the eighth international conference on World Wide Web
Annotea: an open RDF infrastructure for shared Web annotations
Proceedings of the 10th international conference on World Wide Web
Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor
Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor
Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Relational learning techniques for natural language information extraction
Relational learning techniques for natural language information extraction
Active learning with multiple views
Active learning with multiple views
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Focused named entity recognition using machine learning
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Logical structure based semantic relationship extraction from semi-structured documents
Proceedings of the 15th international conference on World Wide Web
EOS: expertise oriented search using social networks
Proceedings of the 16th international conference on World Wide Web
Table detection from plain text using machine learning and document structure
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Semantic annotation using horizontal and vertical contexts
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
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With the advent of the Semantic Web, there is a great need to upgrade existing web content to semantic web content. This can be accomplished through semantic annotations. Unfortunately, manual annotation is tedious, time consuming and error-prone. In this paper, we propose a tool, called iASA, that learns to automatically annotate web documents according to an ontology. iASA is based on the combination of information extraction (specifically, the Similarity-based Rule Learner—SRL) and machine learning techniques. Using linguistic knowledge and optimal dynamic window size, SRL produces annotation rules of better quality than comparable semantic annotation systems. Similarity-based learning efficiently reduces the search space by avoiding pseudo rule generalization. In the annotation phase, iASA exploits ontology knowledge to refine the annotation it proposes. Moreover, our annotation algorithm exploits machine learning methods to correctly select instances and to predict missing instances. Finally, iASA provides an explanation component that explains the nature of the learner and annotator to the user. Explanations can greatly help users understand the rule induction and annotation process, so that they can focus on correcting rules and annotations quickly. Experimental results show that iASA can reach high accuracy quickly.