Skip to Main content Skip to Navigation
Journal articles

Using semantic perimeters with ontologies to evaluate the semantic similarity of scientific papers

Abstract : The work presented in this paper deals with the use of ontologies to compare scientific texts. It particularly deals with scientific papers, specifically their abstracts, short texts that are relatively well structured and normally provide enough knowledge to allow a community of readers to assess the content of the associated scientific papers. The problem is, therefore, to determine how to assess the semantic proximity/similarity of two papers by examining their respective abstracts. Given that a domain ontology provides a useful way to represent knowledge relative to a given domain, this work considers ontologies relative to scientific domains. Our process begins by defining the relevant domain for an abstract through an automatic classification that makes it possible to associate this abstract to its relevant scientific domain, chosen from several candidate domains. The content of an abstract is represented in the form of a conceptual graph which is enriched to construct its semantic perimeter. As presented below, this notion of semantic perimeter usefully allows us to assess the similarity between the texts by matching their graphs. Detecting plagiarism is the main application field addressed in this paper, among the many possible application fields of our approach.
Document type :
Journal articles
Complete list of metadatas

Cited literature [71 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02348268
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Tuesday, November 5, 2019 - 1:01:42 PM
Last modification on : Saturday, June 20, 2020 - 3:38:39 AM
Document(s) archivé(s) le : Friday, February 7, 2020 - 11:32:22 AM

File

iltache_22328.pdf
Files produced by the author(s)

Identifiers

Citation

Samia Iltache, Catherine Comparot, Malik Si-Mohammed, Pierre - Jean Charrel. Using semantic perimeters with ontologies to evaluate the semantic similarity of scientific papers. Informatica, 2018, 42 (3), pp.375-399. ⟨10.31449/inf.v42i3.1559⟩. ⟨hal-02348268⟩

Share

Metrics

Record views

203

Files downloads

540