![Ιδρυματικό Αποθετήριο [SANDBOX]](images/dias.logo.png "Ιδρυματικό Αποθετήριο [SANDBOX]"){kind=logo}
Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/B720F853-8E86-49BB-8093-74494F81E209 | ||
| Έτος | 2006 | ||
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | ||
| Άδεια Χρήσης |
|
||
| Βιβλιογραφική Αναφορά | Angelos Hliaoutakis, Giannis Varelas, Epimenidis Voutsakis, Euripides G.M. Petrakis, Evangelos Milios, "Information Retrieval by Semantic Similarity" , International Journal on Semantic Web and Information Systems (IJSWIS), Vol. 2, no. 3, pp. 55-73, Jul. 2006. DOI: 10.4018/jswis.2006070104 https://doi.org/10.4018/jswis.2006070104 | ||
| Εμφανίζεται στις Συλλογές |
Semantic Similarity relates to computing the similarity between conceptually similar but not nec- essarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic sim- ilarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capa- ble for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising perfor- mance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies
Copyright © DIAS 2013
