Integrity = validity + completeness
ACM Transactions on Database Systems (TODS)
Not all answers are equally good: estimating the quality of database answers
Flexible query answering systems
Database techniques for the World-Wide Web: a survey
ACM SIGMOD Record
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Conceptual-model-based data extraction from multiple-record Web pages
Data & Knowledge Engineering
Formal Ontology in Information Systems: Proceedings of the 1st International Conference June 6-8, 1998, Trento, Italy
A brief survey of web data extraction tools
ACM SIGMOD Record
Approximate Query Processing with Summary Tables in Statistical Databases
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
A Mutually Beneficial Integration of Data Mining and Information Extraction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Relational Learning Techniques for Natural Language Extraction
Relational Learning Techniques for Natural Language Extraction
Toward semantic understanding: an approach based on information extraction ontologies
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
A framework for analysis of data freshness
Proceedings of the 2004 international workshop on Information quality in information systems
Evaluating machine learning for information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
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We present an architecture for structuring and querying the contents of a set of documents which belong to an organization. The structure is a database which is semi-automatically populated using information extraction techniques. We provide an ontology-based language to interrogate the contents of the documents. The processing of queries in this language can give approximate answers and triggers a mechanism for improving the answers by doing additional information extraction of the textual sources. Individual database items have associated quality metadata which can be used when evaluating the quality of answers. The interaction between information extraction and query processing is a pivotal aspect of this research.