A graph-based approach for ontology population with named entities
|A graph-based approach for ontology population with named entities|
|Author(s)||Shen W., Wang J., Luo P., Wang M.|
|Published in||ACM International Conference Proceeding Series|
|Keyword(s)||entity linking, label propagation, named entity classification, ontology population (Extra: Accuracy Improvement, Baseline methods, entity linking, Experimental studies, Graph-based, Label propagation, Management techniques, Named entities, Named entity classification, Ontology Population, Random Walk, Real-world entities, Semantic knowledge, Subtasks, Wikipedia, Knowledge management, Mapping, Natural language processing systems)|
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A graph-based approach for ontology population with named entities is a 2012 conference paper written in English by Shen W., Wang J., Luo P., Wang M. and published in ACM International Conference Proceeding Series.
Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques. This issue naturally consists of two subtasks: (1) for the entity mention whose mapping entity does not exist in the ontology, attach it to the right category in the ontology (i.e., fine-grained named entity classification), and (2) for the entity mention whose mapping entity is contained in the ontology, link it with its mapping real world entity in the ontology (i.e., entity linking). Previous studies only focus on one of the two subtasks and cannot solve this task of populating ontology with named entities integrally. This paper proposes APOLLO, a grAph-based aPproach for pOpuLating ontoLOgy with named entities. APOLLO leverages the rich semantic knowledge embedded in the Wikipedia to resolve this task via random walks on graphs. Meanwhile, APOLLO can be directly applied to either of the two subtasks with minimal revision. We have conducted a thorough experimental study to evaluate the performance of APOLLO. The experimental results show that APOLLO achieves significant accuracy improvement for the task of ontology population with named entities, and outperforms the baseline methods for both subtasks.
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