Integrating Unstructured Data into Relational Databases Export

@inproceedings{citeulike:1901135, abstract = {In this paper we present a system for automatically integrating unstructured text into a multi-relational database using state-of-the-art statistical models for structure extraction and matching. We show how to extend current highperforming models, Conditional Random Fields and their semi-markov counterparts, to effectively exploit a variety of recognition clues available in a database of entities, thereby significantly reducing the dependence on manually labeled training data. Our system is designed to load unstructured records into columns spread across multiple tables in the database while resolving the relationship of the extracted text with existing column values, and preserving the cardinality and link constraints of the database. We show how to combine the inference algorithms of statistical models with the database imposed constraints for optimal data integration.}, address = {Washington, DC, USA}, author = {Mansuri, Imran R. and Sarawagi, Sunita}, booktitle = {ICDE '06: Proceedings of the 22nd International Conference on Data Engineering (ICDE'06)}, citeulike-article-id = {1901135}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1129889}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/ICDE.2006.83}, citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1617397}, citeulike-linkout-3 = {http://www.it.iitb.ac.in/~sunita/papers/icde06a.pdf}, citeulike-linkout-4 = {http://crf.sourceforge.net/}, doi = {10.1109/ICDE.2006.83}, isbn = {0769525709}, keywords = {crf, document\_db\_integration}, posted-at = {2007-11-12 04:03:07}, priority = {2}, publisher = {IEEE Computer Society}, title = {Integrating Unstructured Data into Relational Databases}, url = {http://dx.doi.org/10.1109/ICDE.2006.83}, year = {2006} }

TY - CONF ID - citeulike:1901135 L3 - citeulike-article-id:1901135 N2 - In this paper we present a system for automatically integrating unstructured text into a multi-relational database using state-of-the-art statistical models for structure extraction and matching. We show how to extend current highperforming models, Conditional Random Fields and their semi-markov counterparts, to effectively exploit a variety of recognition clues available in a database of entities, thereby significantly reducing the dependence on manually labeled training data. Our system is designed to load unstructured records into columns spread across multiple tables in the database while resolving the relationship of the extracted text with existing column values, and preserving the cardinality and link constraints of the database. We show how to combine the inference algorithms of statistical models with the database imposed constraints for optimal data integration. SN - 0769525709 AD - Washington, DC, USA TI - Integrating Unstructured Data into Relational Databases PB - IEEE Computer Society T2 - ICDE '06: Proceedings of the 22nd International Conference on Data Engineering (ICDE'06) KW - crf KW - document_db_integration AU - Mansuri, Imran AU - Sarawagi, Sunita PY - 2006/// UR - http://dx.doi.org/10.1109/ICDE.2006.83 ER -