Mining and Leveraging Background Knowledge for Improving Named Entity Linking
| Autor: | Philipp Kuntschik, Adrian M. P. Brasoveanu, Albert Weichselbraun |
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| Rok vydání: | 2018 |
| Předmět: |
Correctness
Computer science 02 engineering and technology Linked data Named Entity Linking ENCODE computer.software_genre Data science Knowledge-rich Information Extraction Entity linking Information extraction Semantic Technologies 020204 information systems Data quality Linked Data Quality 0202 electrical engineering electronic engineering information engineering Semantic technology Graph (abstract data type) 020201 artificial intelligence & image processing Information Extraction computer Natural Language Processing |
| Zdroj: | WIMS Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics-WIMS 18 Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics -WIMS '18 |
| DOI: | 10.1145/3227609.3227670 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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