Document Type : Original/Review Paper

Authors

Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran.

Abstract

Abstract: Web service is a technology for defining self-describing objects, structural-based, and loosely coupled applications. They are accessible all over the web and provide a flexible platform. Although service registries such as Universal Description, Discovery, and Integration (UDDI) provide facilities for users to search requirements, retrieving the exact results that satisfy users’ need is still a difficult task since providers and requesters have various views about descriptions with different explanations. Consequently, one of the most challenging obstacles in the discovery task would be how to understand both sides, which is called knowledge-based understanding. This is of immense value for search engines, information retrieval tasks, and even NPL-based various tasks. The goal is to help recognize matching degrees precisely and retrieve the most relevant services more straightforward. In this research, we introduce a conceptual similarity method as a new way that facilitates discovery procedure with less dependency on the provider and user descriptions to reduce the manual intervention of both sides and being more explicit for the machines. We provide a comprehensive knowledge-based approach by applying the Latent Semantic Analysis (LSA) model to the ontology scheme - WordNet and domain-specific in-sense context-based similarity algorithm. The evaluation of our similarity method, done on OWL-S test collection, shows that a sense-context similarity algorithm can boost the disambiguation procedure of descriptions, which leads to conceptual clarity. The proposed method improves the performance of service discovery in comparison with the novel keyword-based and semantic-based methods.

Keywords

[1] F. Devin, Web oriented architecture–How to design a RESTFull API. TORUS 1–Toward an Open Resource using Services: Cloud Computing for Environmental Data, pp.191-206, 2020.
 
[2] S. Adeli, and P. Moradi, QoS-based Web Service Recommendation using Popular-dependent Collaborative Filtering. Journal of AI and Data Mining, 8(1), pp.83-93, 2020.
 
[3] M. Klusch, P. Kapahnke, S. Schulte, F. Lecue, and A. Bernstein, “Semantic Web Service Search: A Brief Survey,” KI-Künstl. Intell., pp. 1–9, 2015.
 
[4] K.-H. Lee, M. Lee, Y.-Y. Hwang, and K.-C. Lee, “A framework for xml web services retrieval with ranking,” in 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE’07), 2007, pp. 773–778.
 
[5] N. E. Evangelopoulos, “Latent semantic analysis,” Wiley Interdiscip. Rev. Cogn. Sci., Vol. 4, No. 6, pp. 683–692, 2013.
 
[6] T. K. Landauer, “LSA as a theory of meaning,” in Handbook of latent semantic analysis, Psychology Press, 2007, pp. 15–46.
 
[7] I. Lizarralde, C. Mateos, A. Zunino, T. A. Majchrzak, and T. M. Grønli, Discovering web services in social web service repositories using deep variational autoencoders. Information Processing and Management, 57(4), p.102231, 2020.
 
[8] M. Fariss, N. El Allali, H. Asaidi, and M. Bellouki, October. Review of ontology based approaches for web service discovery. In International Conference on Advanced Information Technology, Services and Systems (pp. 78-87). Springer, Cham, 2018.
 
[9] O. Sharifi, Z. Bayram, A critical evaluation of web service modeling ontology and web service modeling language. In International Symposium on Computer and Information Sciences (pp. 97-105). Springer, Cham, 2016.
 
[10] C. Mateos, M. Crasso, A. Zunino, and M. Campo, “Supporting ontology-based semantic matching of web services in movilog,” in Advances in Artificial Intelligence-IBERAMIA-SBIA 2006, Springer, 2006, pp. 390–399.
 
[11] M. Shamsfard and A. A. Barforoush, “Learning ontologies from natural language texts,” Int. J. Hum.-Comput. Stud., Vol. 60, No. 1, pp. 17–63, 2004.
 
[12] G. Salton, A. Wong, and C.-S. Yang, “A vector space model for automatic indexing,” Commun. ACM, Vol. 18, No. 11, pp. 613–620, 1975.
 
[13] M. Paolucci, T. Kawamura, T. R. Payne, and K. Sycara, “Semantic matching of web services capabilities,” in International Semantic Web Conference, 2002, pp. 333–347.
 
[14] W. Jiang, J. Lin, H. Wang, and S. Zou, Hybrid semantic service matchmaking method based on a random forest. Tsinghua Science and Technology, 25(6), pp.798-812, 2020.
 
[15] S. Pakari, E. Kheirkhah, and M. Jalali, “A Novel Approach: A Hybrid Semantic Matchmaker For Service Discovery In Service Oriented Architecture,” Int. J. Netw. Secur. Its Appl., Vol. 6, No. 1, p. 37, 2014.
 
[16] V. Oleshchuk, “Ontology-based service matching and discovery,” in Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011 IEEE 6th International Conference on, 2011, Vol. 2, pp. 609–612.
 
[17] A. V. Paliwal, B. Shafiq, J. Vaidya, H. Xiong, and N. Adam, “Semantics-based automated service discovery,” Serv. Comput. IEEE Trans. On, Vol. 5, No. 2, pp. 260–275, 2012.
 
[18] Y. Shi, G. Li, J. Li, and Y. Li, “Framework of semantic web service discovery based on ontology mapping,” in Research Challenges in Computer Science, 2009. ICRCCS’09. International Conference on, 2009, pp. 77–80.
 
[19] S. Pakari, E. Kheirkhah, and M. Jalali, “Web service discovery methods and techniques: A review,” Int. J. Comput. Sci. Eng. Inf. Technol., Vol. 4, No. 1, 2014.
 
[20] B. Di Martino, “Semantic web services discovery based on structural ontology matching,” Int. J. Web Grid Serv., Vol. 5, No. 1, pp. 46–65, 2009.
 
[21] L. Zhou, “An approach of semantic web service discovery,” in Communications and Mobile Computing (CMC), 2010 International Conference on, 2010, Vol. 1, pp. 537–540.
 
[22] A. B. Bener, V. Ozadali, and E. S. Ilhan, “Semantic matchmaker with precondition and effect matching using SWRL,” Expert Syst. Appl., Vol. 36, No. 5, pp. 9371–9377, 2009.
 
[23] C. Ke and Z. Huang, “Self-adaptive semantic web service matching method,” Knowl.-Based Syst., Vol. 35, pp. 41–48, 2012.
 
[24] A. Adala, N. Tabbane, and S. Tabbane, “A framework for automatic web service discovery based on semantics and NLP techniques,” Adv. Multimed., Vol. 2011, p. 1, 2011.
 
[25] M. D. Lakshmi and J. P. M. Dhas, “A Hybrid Approach for Discovery of OWL-S Services Based on Functional and Non-Functional Properties,” WSEAS Trans Comput, Vol. 14, pp. 62–71, 2015.
 
[26] R. Karimpour and F. Taghiyareh, “conceptual discovery of web services using WordNet,” in Services Computing Conference, 2009. APSCC 2009. IEEE Asia-Pacific, 2009, pp. 440–444.
 
[27] G. Ganapathy and C. Surianarayanan, “An approach to identify candidate services for semantic web service discovery,” in 2010 IEEE international conference on service-oriented computing and applications (SOCA), 2010, pp. 1–4.
 
[28] Y. Peng and C. Wu, “Automatic semantic web service discovery based on assignment algorithm,” 2010.
 
[29] F. Chen, M. Li, H. Wu, and L. Xie, “Web service discovery among large service pools utilising semantic similarity and clustering,” Enterp. Inf. Syst., Vol. 11, No. 3, pp. 452–469, 2017.
 
[30] D. Lin and others, “An information-theoretic definition of similarity.,” in Icml, 1998, Vol. 98, pp. 296–304.
 
[31] C. N. Pushpa, G. Deepak, A. Kumar, J. Thriveni, and K. R. Venugopal, “OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics,” in 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–5, 2020.
 
[32] C. B. Merla, “Context-aware match-making in semantic web service discovery,” IJAEST-Int. J. Adv. Eng. Sci. Technol., Vol. 1, No. 9, pp. 243–247, 2010.
 
[33] M. Klusch, P. Kapahnke, and B. Fries, “Hybrid semantic web service retrieval: A case study with OWLS-MX,” in Semantic Computing, 2008 IEEE International Conference on, 2008, pp. 323–330.
 
[34] J. R. Raj and T. Sasipraba, “web service discovery based on computation of semantic similarity distance and Qos normalization,” Indian J. Comput. Sci. Eng., Vol. 3, No. 2, pp. 235–239, 2012.
 
[35] A. Farooq and R. Arshad, “An Efficient Technique for Web Services Identification,” Int J Multidiscip Sci Eng, Vol. 2, No. 1, pp. 26–30, 2011.
 
[36] N. Kokash, “A comparison of web service interface similarity measures,” Front. Artif. Intell. Appl., Vol. 142, p. 220, 2006.
 
[37] “SemWebCentral: OWL-S Service Retrieval Test Collection: File Release Notes and Changelog.” http://projects.semwebcentral.org/frs/shownotes.php?release_id=369.
 
[38] “Porter-Stemmer,” Drupal.org. https://www.drupal.org/project/porterstemmer.
 
[39] C. D. Manning, M. Surdeanu, J. Bauer, J. R. Finkel, S. Bethard, and D. McClosky, “The Stanford CoreNLP Natural Language Processing Toolkit.,” in ACL (System Demonstrations), 2014, pp. 55–60.
 
[40] K. Clark and C. D. Manning, “Improving coreference resolution by learning entity-level distributed representations,” ArXiv Prepr. ArXiv160601323, 2016.
 
[41] C. Leacock and M. Chodorow, “Combining local context and WordNet similarity for word sense identification,” WordNet Electron. Lex. Database, Vol. 49, No. 2, pp. 265–283, 1998.
 
[42] X. Zhu, X. Yang, Y. Huang, Q. Guo, and B. Zhang, Measuring similarity and relatedness using multiple semantic relations in WordNet. Knowledge and Information Systems, 62(4), 1539-1569, 2020.
 
[43] C. Corley and R. Mihalcea, “Measuring the semantic similarity of texts,” in Proceedings of the ACL workshop on empirical modeling of semantic equivalence and entailment, 2005, pp. 13–18.
 
[44] M. C. Lintean and V. Rus, “Measuring Semantic Similarity in Short Texts through Greedy Pairing and Word Semantics.,” 2012.
 
[45] J. J. Jiang and D. W. Conrath, “Semantic similarity based on corpus statistics and lexical taxonomy,” ArXiv Prepr. Cmp-Lg9709008, 1997.
 
[46] Y. Peng, “Two levels semantic web service discovery,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, 2010, Vol. 6, pp. 2523–2526.
 
[47] M. Lintean and V. Rus, “An Optimal Quadratic Approach to Monolingual Paraphrase Alignment,” in Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania, 2015, pp. 127–134.
 
[48] S. Xu, Bayesian Naïve Bayes classifiers to text classification. Journal of Information Science, 44(1), pp. 48-59, 2018.
 
[49] B. T. Pham, D. Bui, I. Prakash, and M. Dholakia, “Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS,” J Geomat., Vol. 10, pp. 71–79, 2016.
 
[50] D. Ștefănescu, R. Banjade, and V. Rus, “Latent semantic analysis models on wikipedia and tasa,” 2014.
[51] T. K. Landauer, P. W. Foltz, and D. Laham, “An introduction to latent semantic analysis,” Discourse Process., Vol. 25, No. 2–3, pp. 259–284, 1998.
 
[52] M. C. Lintean, C. Moldovan, V. Rus, and D. S. McNamara, “The Role of Local and Global Weighting in Assessing the Semantic Similarity of Texts Using Latent Semantic Analysis.,” in FLAIRS Conference, 2010, pp. 235–240.
 
[53] P. Farzi, R. Akbari, and O. Bushehrian, “Improving semantic web service discovery method based on QoS ontology,” in 2017 2nd conference on swarm intelligence and evolutionary computation (csiec), 2017, pp. 72–76.
 
[54] M. Klusch, B. Fries, and K. Sycara, “OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services,” Web Semant. Sci. Serv. Agents World Wide Web, Vol. 7, No. 2, pp. 121–133, 2009.