PMRF: Parameterized Matching-Ranking Framework
| Autor: | Salem Chakhar, Alessio Ishizaka, Nadia Yacoubi-Ayadi, Fatma Ezzahra Gmati, Afef Bahri |
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| Rok vydání: | 2016 |
| Předmět: |
Matching (statistics)
Computer science Parameterized complexity 02 engineering and technology computer.software_genre Management information systems Semantic similarity Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Learning to rank Data mining Web service computer |
| Zdroj: | Software Engineering Research, Management and Applications ISBN: 9783319339023 |
| DOI: | 10.1007/978-3-319-33903-0_13 |
| Popis: | The PMRF (Parameterized Matching-Ranking Framework) is a highly configurable framework supporting a parameterized matching and ranking of Web services. This paper first introduces the matching and ranking algorithms supported by the PMRF. Next, it presents the architecture of the developed system and discusses some implementation issues. Then, it provides the results of performance evaluation of the PMRF. It also compares PMRF to two exiting frameworks, namely iSeM-logic-based and SPARQLent. The different matching and ranking algorithms have been evaluated using the OWLS-TC4 datasets. The evaluation has been conducted employing the SME2 (Semantic Matchmaker Evaluation Environment) tool. The results show that the algorithms behave globally well in comparison to iSeM-logic-based and SPARQLent. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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