By Topic

Robust recognition of complex entities in text exploiting enterprise data and NLP-techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

Data transactions between business partners often include unstructured data such as invoices or purchase orders. In order to process such automatically, complex business entities need to be identified. Examples for complex entities are products, business partners and purchase orders which are stored in a supplier relationship management system. Both, structured records in the enterprise system and text data, describe these complex entities. A major challenge is to correctly associate entities recognized in unstructured data with entities stored in structured data, e.g. enterprise databases. We address that problem and propose a robust process methodology which includes three phases: candidate extraction from unstructured text, generation of initial mappings with structured data and disambiguation of the mappings exploiting relationships among the entities in the enterprise data and the documentspsila structure. We describe each step in detail, propose a common architecture and introduce to our data model and algorithms.

Published in:

Digital Information Management, 2008. ICDIM 2008. Third International Conference on

Date of Conference:

13-16 Nov. 2008