Semantic Search Enhanced with Rating Scores

This paper presents SemSim(e), 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. SemSim(e) 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 SemSim(e) has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as SemSimRV.