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Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Weichselbraun, Albert and Kuntschik, Philipp and Brasoveanu, Adrian M. P. (2018) Mining and Leveraging Background Knowledge for Improving Named Entity Linking. In: WIMS 2018 - 8th International Conference on Web Intelligence, Mining and Semantics, June 25-27 2018, Novi Sad, Serbia. (In Press)

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Abstract

Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.

Item Type:Conference or Workshop Item (Paper)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
ID Code:110
Deposited By: Brasoveanu Adrian M.P.
Deposited On:30 May 2018 11:18
Last Modified:04 Oct 2018 06:57

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