CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Converting the syntactic structures of hierarchical data to their semantic structures
Information organization and databases
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Fundamentals of Database Systems
Fundamentals of Database Systems
Modern Information Retrieval
Schema versioning and database conversion techniques for bi-temporal databases
Annals of Mathematics and Artificial Intelligence
The Semantic Web: The Roles of XML and RDF
IEEE Internet Computing
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Semi-Automatic Wrapper Generation for Internet Information Sources
COOPIS '97 Proceedings of the Second IFCIS International Conference on Cooperative Information Systems
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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A lot of work has been done in the area of extracting data content from the Web, but less attention has been given to extracting the conceptual schemas or ontologies of underlying Web pages. The goal of the WebOntEx (Web ontology extraction) project is to make progress toward semiautomatically extracting Web ontologies by analyzing a set of Web pages that are in the same application domain. The ontology is considered a complete schema of the domain concepts. Our ontology metaconcepts are based on the extended entity-relationship (EER) model. The concepts are classified into entity types, relationships, attributes, and superclass/ subclass hierarchies. WebOntEx attempts to extract ontology concepts by analyzing the use of HTML tags and by utilizing Part-of-Speech tagging. WebOntEx applies heuristic rules and machine learning techniques, in particular, inductive logic programming (ILP).