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Linked Enterprise Data for Fine Grained Named Entity Linking and Web Intelligence

Weichselbraun, Albert and Streiff, Daniel and Scharl, Arno (2014) Linked Enterprise Data for Fine Grained Named Entity Linking and Web Intelligence. In: 4th International Conference on Web Intelligence, Mining and Semantics (WIMS 2014), 2 - 4 June 2014, Thessaloniki, Greece.

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Abstract

To identify trends and assign metadata elements such as location and sentiment to the correct entities, Web intelligence applications require methods for linking named entities and revealing relations between organizations, persons and products. For this purpose we introduce Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories. This paper outlines the underlying methods, provides insights into the migration of proprietary knowledge sources to linked enterprise data, and discusses the lessons learned from adapting linked data for named entity linking. A large dataset obtained from Orell Füssli, the largest Swiss business information provider, serves as the main showcase. This dataset includes more than nine million triples on companies, their contact information, management, products and brands. We identify major challenges towards applying this data for named entity linking and conduct a comprehensive evaluation based on several news corpora to illustrate how Recognyze helps address them, and how it improves the performance of named entity linking components drawing upon linked data rather than machine learning techniques.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:linked open data, linked enterprise data, named entity linking, business news, Web intelligence
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
ID Code:77
Deposited By: Dr Albert Weichselbraun
Deposited On:26 Apr 2014 05:28
Last Modified:10 Jun 2014 06:04

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