Conceptual-model-based data extraction from multiple-record Web pages
Data & Knowledge Engineering
Creating Semantic Web Contents with Protégé-2000
IEEE Intelligent Systems
OntoWeb - A Semantic Web Community Portal
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
Towards Ontology Generation from Tables
World Wide Web
Thesis: automatic ontology generation from web tabular structures
AI Communications
Quantitative and qualitative evaluation of the OntoLearn ontology learning system
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automatic hidden-web table interpretation by sibling page comparison
ER'07 Proceedings of the 26th international conference on Conceptual modeling
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Generating ontologies via language components and ontology reuse
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Conceptual Modeling Meets the Human Genome
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Automatic hidden-web table interpretation, conceptualization, and semantic annotation
Data & Knowledge Engineering
Ontology consolidation in bioinformatics
APCCM '10 Proceedings of the Seventh Asia-Pacific Conference on Conceptual Modelling - Volume 110
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Biologists usually focus on only a small, individualized, subdomain of the huge domain of biology. With respect to their sub-domain, they often need data collected from various different web resources. In this research, we provide a tool with which biologists can generate a sub-domain-size, user-specific ontology that can extract data from web resources. The central idea is to let a user provide a seed, which consists of a single data instance embedded within the concepts of interest. Given a seed, the system can generate an extraction ontology, match information with the user's view based on the seed, and collect information from online repositories. Our initial experimentations indicate that our prototype system can successfully match source data with an ontology seed and gather information from different sources with respect to user-specific, personalized views.