Semantic Search: Document Ranking and Clustering Using Computer Science Ontology and N-Grams

Semantic similarity has become an important tool and widely been used to solve traditional Information Retrieval problems. This study adopts ontology of computer science and proposes an ontology indexing weight based on Wu and Palmer’s edge counting measure and uses the N-grams method for computing a family of word similarity. The study also compares the subsumption weight between Hliaoutakis and Nicola’s weight and query keywords (Decision Making, Genetic Algorithm, Machine Learning, Heuristic ). A probability value (p-values) from the t-test (p = 0.105) is higher 0.05, which indicates the evidence of no of no significant differences between the two weights methods. The experimental results show the new keyword-keyword similarity matrix scores that compute from hierarchical relationship weight based on Computer Science ontology and string matching (tri-grams) for computing of string of keyword. We computed the document-document similarity matrix scores using our keyword similarity matrix scores and compared them with the keyword matching weights using Dice coefficient method. In addition, this paper, we presented a new document semantic ranking process for the semantic ranking that proposes a new weight of query term in the document based on Computer Science Ontology weight. The experimental results show that the new document similarity score between a user’s query and the paper suggests that the new measures were effectively ranked.