Name
Abstract | ||
|---|---|---|
The Information Content IC of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some other authors tried to overcome previous approaches, estimating IC from the knowledge modeled in an ontology. In this paper, the authors develop this idea, by proposing a new model to compute the IC of a concept exploiting the taxonomic knowledge modeled in an ontology. In comparison with related works, their proposal aims to better capture semantic evidences found in the ontology. To test the authors' approach, they have applied it to well-known semantic similarity measures, which were evaluated using standard benchmarks. Results show that the use of the authors' model produces, in most cases, more accurate similarity estimations than related works. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4018/jswis.2012040102 | Int. J. Semantic Web Inf. Syst. |
Keywords | Field | DocType |
standard benchmarks,well-known semantic similarity measure,semantic evidence,new model,taxonomic knowledge,concept appearance probability,related work,previous approach,accurate similarity estimation,information content ic,semantic similarity,information content,ontologies,knowledge management,computational linguistics | Semantic similarity,Ontology (information science),Ontology,Data mining,Information retrieval,Computer science,Computational linguistics,Artificial intelligence,Natural language processing | Journal |
Volume | Issue | ISSN |
8 | 2 | 1552-6283 |
Citations | PageRank | References |
19 | 0.64 | 40 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Sánchez | 1 | 395 | 32.93 |
| Montserrat Batet | 2 | 899 | 37.20 |