Algorithms for string searching
ACM SIGIR Forum
A hierarchical approach to wrapper induction
Proceedings of the third annual conference on Autonomous Agents
Record-boundary discovery in Web documents
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Generating finite-state transducers for semi-structured data extraction from the Web
Information Systems - Special issue on semistructured data
IEPAD: information extraction based on pattern discovery
Proceedings of the 10th international conference on World Wide Web
A flexible learning system for wrapping tables and lists in HTML documents
Proceedings of the 11th international conference on World Wide Web
RoadRunner: Towards Automatic Data Extraction from Large Web Sites
Proceedings of the 27th International Conference on Very Large Data Bases
A Fully Automated Object Extraction System for the World Wide Web
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
RENS --- Enabling a Robot to Identify a Person
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Extraction of user-defined data blocks using the regularity of dynamic web pages
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Automated extraction of hit numbers from search result pages
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
The HiLeX system for semantic information extraction
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
TEX: An efficient and effective unsupervised Web information extractor
Knowledge-Based Systems
| Hi-index | 0.00 |
Much information on the Web is contained in regularly structured objects, or data records. Data records often present their host pages' essential information, such as lists of products and services. Mining data records to extract this information can help you provide value-added services. Existing approaches to data extraction on the Web include supervised learning and automatic techniques. Supervised learning requires substantial human effort, and current automatic techniques provide poor results. To solve this problem, the MDR (mining data records) system exploits two key observations about the layout of data records in Web pages and employs a string-matching algorithm. Experiments show that this new automatic technique significantly outperforms existing methods. In addition, it mines both contiguous and noncontiguous data records.