The Utility of Knowledge in Inductive Learning
Machine Learning
Efficient top-down induction of logic programs
ACM SIGART Bulletin
Answering queries using views (extended abstract)
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Query reformulation for dynamic information integration
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
Learning to Understand Information on the Internet: AnExample-Based Approach
Journal of Intelligent Information Systems - Special issue: next generation information technologies and systems
Data-driven understanding and refinement of schema mappings
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Logic-based techniques in data integration
Logic-based artificial intelligence
ECML '93 Proceedings of the European Conference on Machine Learning
Language Series Revisited: The Complexity of Hypothesis Spaces in ILP (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
MiniCon: A scalable algorithm for answering queries using views
The VLDB Journal — The International Journal on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Query planning and optimization in information integration
Query planning and optimization in information integration
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Applied Ontology
Learning semantic descriptions of web information sources
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Category translation: learning to understand information on the internet
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Bringing semantics to web services: the OWL-S approach
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
Efficient learning of action schemas and web-service descriptions
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Automatically Constructing Semantic Web Services from Online Sources
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Web database schema identification through simple query interface
RED'09 Proceedings of the 2nd international conference on Resource discovery
Extraction and integration of partially overlapping web sources
Proceedings of the VLDB Endowment
Learning web-service task descriptions from traces
Web Intelligence and Agent Systems
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The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter.