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Mining and Leveraging Background Knowledge for Improving Named Entity Linking
- Source :
- 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
- Publication Year :
- 2018
- Publisher :
- ACM, 2018.
-
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.
- Subjects :
- 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
Subjects
Details
- ISBN :
- 978-1-4503-5489-9
- ISBNs :
- 9781450354899
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics
- Accession number :
- edsair.doi.dedup.....ad95a0dddbd50a8936d2deb141b12a46