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Open AccessArticle

Semantic Search Enhanced with Rating Scores

National Research Council (CNR), Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”, Via dei Taurini 19, I-00185 Rome, Italy
Author to whom correspondence should be addressed.
Future Internet 2020, 12(4), 67;
Received: 11 March 2020 / Revised: 10 April 2020 / Accepted: 13 April 2020 / Published: 15 April 2020
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSime has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as S e m S i m R V . View Full-Text
Keywords: similarity reasoning; semantic search; reference ontology; semantic annotation similarity reasoning; semantic search; reference ontology; semantic annotation
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Formica, A.; Pourabbas, E.; Taglino, F. Semantic Search Enhanced with Rating Scores. Future Internet 2020, 12, 67.

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