Mapping enterprise entities to text segments

Today, valuable business information is increasingly stored as unstructured data (documents, emails, etc.). For example, documents exchanged between business partners capture information on transactions between them like purchases or invoices. A major challenge is to correctly recognize and associate real-world entities in unstructured data, e.g. documents, with those stored in structured data e.g., enterprise databases. To address this, we propose in this paper a robust process methodology consisting of three phases: entity extraction from documents, generation of mapping of recognized entities with structured data, and disambiguation of mappings exploiting relationships from the enterprise data and the documents' structure. Categories and Subject Descriptors ontent Analysis and Indexing]: [Abstracting methods, Linguistic processing, Dictionaries] General Terms Algorithms Keywords Information Extraction, Named Entity Recognition

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Falk Brauer, Alexander Löser, Hong-Hai Do
• Keywords: CIKM 2008, Information Management, Named Entity Recognition, Structured Data E.g, Unstructured Data