Abstract
Ontologies support the intelligent systems in which information needs to be handled, reused and transferred. This paper is about a weighted graph-oriented ontology matching algorithm, namely the WeGO++ algorithm that helps ontology matching in a pragmatic manner. It considers predominantly the graph-structured nature of ontology models. The methods and approaches in obtaining matching across ontologies use the basic principles of similarity identifications among graph structures. WeGO++ ontology matching frame work is able to successfully match all the data sets. The algorithmic process that is contained in the work looks for nine types of Similarity Scores between members of the ontologies to be matched. Each score is given a specific weight for arriving at the final Similarity Score for each element, and the overall Similarity Score for the ontology is computed using these values. The pragmatic nature of this matching solution is an advantage which approaches the task in a simplistic and efficient manner. The computations are not very complicated and do not use large computing power. It is possible to add more features such as incorporating a sophisticated GUI, changing the matching threshold at runtime and adding a new module to introduce a graphical representation of Similarity Scores of the ontology elements. Threshold values can be fixed to accept levels of matching between elements and classify the quality of match that exists between the ontologies concerned. This provides some room for the manual decision making which can be efficient in deciding matching levels in certain hesitant matches. The algorithms have been tested with OAEI test data sets and some popular ontology library data sets apart from ontologies built from conventional database data sets.
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References
Al-Namiy AQ (2013) Algorithm to match ontologies on the semantic web. Editorial Preface, vol 4, no 3, pp 221–227
Bechhofer S, van Harmelen F, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein BLA, Franklin W (2004) OWL web ontology language reference. W3C Recommendation
Bollegala D, Matsuo Y, Ishizuka M (2007) Measuring semantic similarity between words using web search engines. WWW 7:757–766
Bryl V, Sergienya I, Tonelli S, Giuliano C (2007) Role repository linking framenet and wordnet 10(5):237–241
Cruz F, Xiao H (2005) The Role of Ontologies in Data Integration. J Eng Intell Syst 13(4):245–252
Cruz F, Xiao H, Hsu F (2004) An Ontology-based framework for semantic interoperability between XML sources. In: International database engineering and applications symposium (IDEAS), pp 217–226
Dean M, Connolly D, van Harmelen F, Hendler J, Horrocks I, McGuinness DL, Patel-Schneider PF, Stein LA (2002) OWL web ontology language 1.0 reference
Decker S, Melnik S, van Harmelen F, Fensel D, Klein MCA, Broekstra J, Erdmann M, Horrocks I (2000) The semantic web. The roles of XML and RDF. IEEE Internet Comput 4(5):63–74
Didion J, Walenz B (2004) Jwnl (java wordnet library)
Doan AH, Madhavan J, Dhamankar R, Domingos P, Halevy A (2003) Learning to match ontologies on the semantic web. VLDB J 12:303–319
Fenza G, Loia V, Senatore S (2009) Local semantic context analysis for automatic ontology matching. In: IFSA/EUSFLAT Conference, pp 1315–1320
Gan M, Dou X, Wang D, Jiang R (2011) DOPCA: a new method for calculating ontology based semantic similarity. In: 2011 10th IEEE/ACIS international conference on computer and information science
Garcia-solaco M, Saltor F, Castellanos M (2006) Semantic heterogeneity in multidatabase systems. In: Bukhres O, Elmagarmid A (eds) Object-oriented multidatabase systems. Prentice-Hall, Englewood Cliffs, pp 129–202
Hendler J, Berners-Lee T, Miller E (2002) Integrating Applications on the Semantic Web. J Inst Electron Eng Jpn 122(10):676–680
Hoffart J, Suchanek FM, Berberich K, Weikum G (2013) YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif Intell 194:28–61
Jorge G, Bernad J, Mena E (2011) Ontology matching with CIDER: evaluation report for OAEI. In: Proceedings of the sixth international workshop on ontology matching
Koumenides CL, Shadbolt NR (2014) Ranking methods for entity‐oriented semantic web search. J Assoc Inf Sci Technol 65(6):1091–1106
Knoblock CA, Szekely P, Ambite JL, Goel A, Gupta S, Lerman K, Muslea M, Taheriyan M, Mallick P (2012) Semi-automatically mapping structured sources into the semantic web. In: Extended semantic web conference. Springer, Berlin, Heidelberg, pp 375–390
Leacock C, Chodorow M (1998) Combining local context and wordnet similarity for word sense identification. In: Fellbaum C (ed) WordNet: an electronic lexical database. MIT Press, Cambridge, pp 265–283
Mathur I, Joshi N, Darbari H, Kumar A (2014) Shiva: a framework for graph based ontology matching. Int J Comput Appl Vol 87
Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38:39–41
Nagvenkar A, Pawar JD, Bhattacharyya P (2016) Indowordnet conversion to web ontology language (OWL)
Patel-Schneider PF, Hayes P, Horrocks I (2004) OWL web ontology language: semantics and abstract syntax. W3C Recommendation
Pedersen T, Banerjee S, Patwardhan S (2005) Maximizing semantic relatedness to perform word sense disambiguation. Research Report UMSI 2005/25, University of Minnesota Supercomputing Institute
Rajeswari V, Selvan A, Varughese DK (2013) A novel method for ontology integration by evaluating member equivalence. In: 2013 IEEE seventh international conference on semantic computing
Sagi E, Kaufmann S, Clark B (2009) Semantic density analysis: comparing word meaning across time and phonetic space. In: Proceedings of the workshop on geometrical models of natural language semantics. Association for Computational Linguistics, pp 104–111
Sanfilippo AP, Tratz SC, Gregory ML, Chappell AR, Whitney PD, Posse C, Paulson PR, Baddeley B, Hohimer RE, White AM (2006) Ontological annotation with wordnet. No. PNNL-SA-54089. Pacific Northwest National Lab (PNNL), Richland, WA (United States)
Shekarpour SA, Katebi M, Katebi SD (2009) A trust model for semantic web. IJSSST 10(2):13–24
Shiva, Kumar A, Joshi N (2014) An enhanced graph based ontology matcher. Int J Comput Appl (0975–8887) 92(16)
Simon J, Raja K, Arasu GT (2012) Relevant pages in semantic web search engines using. Int J Electron Comput Sci Eng 1(2):578–584
Snow R, Prakash S, Jurafsky D, Ng AY (2007) Learning to merge word senses. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (emnlp-conll), pp 1005–1014
Spagnola S, Lagoze C (2011) Edge dependent pathway scoring for calculating semantic similarity in ConceptNet. In: Proceedings of the ninth international conference on computational semantics. Association for Computational Linguistics, pp 385–389
Supekar K, Patel C, Lee Y (2004) Characterizing quality of knowledge on semantic web. In: FLAIRS conference, pp 472–478
Tayal MA, Raghuwanshi MM, Malik L (2014) Word net based method for determining semantic sentence similarity through various word senses. In: Proceedings of the 11th international conference on natural language processing, pp 139–145
Taye MM, Alalwan N (2010) Ontology alignment technique for improving semantic integration. In: The fourth international conference on advances in semantic processing
Tsang V, Stevenson S (2004) Calculating semantic distance between word sense probability distributions. In: Proceedings of the eighth conference on computational natural language learning (CoNLL-2004) at HLT-NAACL
Weikum G, Graupmann J, Schenkel R, Theobald M (2004) Towards a statistically semantic web. In: International conference on conceptual modeling. Springer, Berlin, Heidelberg, pp 3–17
Xiao H, Cruz IF (2006) Integrating and exchanging XML data using ontologies’. In: Journal on data semantics VI, volume 4090 of Lecture Notes in Computer Science. Springer, pp 67–89
Yang C-Y, Wu S-J (2012) Semantic web information retrieval based on the Wordnet. Int J Digit Content Technol Appl. https://doi.org/10.4156/jdcta.vol6.issue6
Zhang R, Wang Y, Wang J (2008) Research on ontology matching approach in semantic web. In: International conference on internet computing in science and engineering
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Rajeswari, V., Kavitha, M. & Varughese, D.K. A weighted graph-oriented ontology matching algorithm for enhancing ontology mapping and alignment in Semantic Web. Soft Comput 23, 8661–8676 (2019). https://doi.org/10.1007/s00500-019-04148-3
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DOI: https://doi.org/10.1007/s00500-019-04148-3