Paper Info
Title
Entity Resolution in Texts Using Statistical Learning and Ontologies
Abstract
Ambiguities, which are inherently present in natural languages represent a challenge of determining the actual identities of entities mentioned in a document (e.g., Paris can refer to a city in France but it can also refer to a small city in Texas, USA or to a 1984 film directed by Wim Wenders having title Paris, Texas). Disambiguation is a problem that can be successfully solved by entity resolution methods.This paper studies various methods for estimating relatedness between entities, used in collective entity resolution. We define a unified entity resolution approach, capable of using implicit as well as explicit relatedness for collectively identifying in-text entities. As a relatedness measure, we propose a method, which expresses relatedness using the heterogeneous relations of a domain ontology. We also experiment with other relatedness measures, such as using statistical learning of co-occurrences of two entities or using content similarity between them. Evaluation on real data shows that the new methods for relatedness estimation give good results.
Year
DOI
Venue
2009
10.1007/978-3-642-10871-6_7
ASWC
Keywords
Field
DocType
natural language,entity resolution
Ontology,Semantic integration,Data mining,Semantic annotation,Computer science,Natural language processing,Statistical learning,Artificial intelligence,Entity linking,Ontology (information science),Name resolution,Information retrieval,Natural language
Conference
Volume
ISSN
Citations 
5926
0302-9743
7
PageRank 
References 
Authors
0.53
30
2
Name
Order
Citations
PageRank
Tadej Stajner1324.78
Dunja Mladenic21484170.14