Research Article
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Similarity Matching of Ontology in Semantic Web

Year 2022, Volume: 12 Issue: 2, 263 - 267, 24.12.2022

Abstract

Matching Ontologies becomes an important task for many applications in Semantic Web. This paper investigates effective similarity match between ontologies by considering similarity on two levels. We first consider the similarity of the linguistic properties of the ontology entities which takes in consideration both morphological and semantics of the entities. This is then combined with measuring the similarity of the ontology structure as represented by RDF graph. This similarity is derived by constructing a graph from the
matched nodes and use it to calculate the measure of structure similarity.

References

  • Algergawy, A., Nayak, R., & Saake, G. (2010). Element similarity measures in XML schema matching. Information Sciences, 180(24), 4975-4998.
  • Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american, 284(5), 34-43.
  • Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. “ O’Reilly Media, Inc.”.
  • Blanchard, E., Harzallah, M., Briand, H., & Kuntz, P. (2005). A typology of ontology-based semantic measures. EMOIINTEROP, 160, 3-11.
  • Budanitsky, A., & Hirst, G. (2006). Evaluating wordnet-based measures of lexical semantic relatedness. Computational linguistics, 32(1), 13-47.
  • Cyganiak, R., Wood, D., Lanthaler, M., Klyne, G., Carroll, J. J., & McBride, B. (2014). RDF 1.1 concepts and abstract syntax. W3C recommendation, 25(02), 1-22.
  • Ding, L., Pan, R., Finin, T., Joshi, A., Peng, Y., & Kolari, P. (2005). Finding and ranking knowledge on the semantic web. In International Semantic Web Conference (pp. 156-170). Springer, Berlin, Heidelberg.
  • Graves, A., Adali, S., & Hendler, J. (2008). A Method to Rank Nodes in an RDF Graph. In International Semantic Web Conference (Posters & Demos) (Vol. 401).
  • Jeh, G., & Widom, J. (2002, July). Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538-543).
  • Klyne, G. (2004). RDF Concepts and Abstract Syntax W3C Recommendation. http://www. w3. org/TR/rdf-concepts/. Lin, D. (1998). An information-theoretic definition of similarity. In Icml (Vol. 98, No. 1998, pp. 296-304).
  • Liu, X., Tong, Q., Liu, X., & Qin, Z. (2021). Ontology matching: state of the art, future challenges and thinking based on utilized information. IEEE Access.
  • Lv, Q., Jiang, C., & Li, H. (2020). Solving ontology metamatching problem through an evolutionary algorithm with approximate evaluation indicators and adaptive selection pressure. IEEE Access, 9, 3046-3064.
  • Melnik, S., Garcia-Molina, H., & Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proceedings 18th international conference on data engineering (pp. 117-128). IEEE.
  • Miller, G. A., Fellbaum, C., Tengi, R., Wolff, S., Wakefield, P., Langone, H., & Haskell, B. (2006). WordNet: A lexical database for the English language. Cognitive Science Lab, Princeton University, http://www. cogsci. princeton. edu/wn.
  • Motik, B., & Patel-Schneider, P. (2012). OWL 2 Web Ontology Language Mapping to RDF Graphs.
  • Nayak, R., & Tran, T. (2007). A progressive clustering algorithm to group the XML data by structural and semantic similarity. International Journal of Pattern Recognition and Artificial Intelligence, 21(04), 723-743.
  • Qin, P., Lu, Z., Yan, Y., & Wu, F. (2009). A new measure of word semantic similarity based on wordnet hierarchy and dag theory. In 2009 International Conference on Web Information Systems and Mining (pp. 181-185). IEEE.
  • Ramasubramanian, C., & Ramya, R. (2013). Effective preprocessing activities in text mining using improved porter’s stemming algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 2(12), 4536-4538.
  • Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007.
  • Rice, S. V., Bunke, H., & Nartker, T. A. (1997). Classes of cost functions for string edit distance. Algorithmica, 18(2), 271-280.
  • Salman, A. (2020). Similarity matching of XML schema. Karaelmas Fen ve Mühendislik Dergisi, 10(1), 121-129.
  • Schneider, P., Hayes, P., & Horrocks, I. (2004). OWL Web Ontology Language Semantics and Abstract Syntax. W3C Recommendation. World Wide Web Consortium (W3C).
  • Uschold, M. (2003). Where are the semantics in the semantic web?. Ai Magazine, 24(3), 25-25. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E. G., &
  • Milios, E. E. (2005). Semantic similarity methods in wordnet and their application to information retrieval on the web. In Proceedings of the 7th annual ACM international workshop on Web information and data management (pp. 10-16).
  • Wang, Y., Li, Y., Fan, J., Ye, C., & Chai, M. (2021). A survey of typical attributed graph queries. World Wide Web, 24(1), 297- 346.
  • Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. arXiv preprint cmp-lg/9406033.
  • Zhang, D., Song, T., He, J., Shi, X., & Dong, Y. (2012). A similarity-oriented RDF graph matching algorithm for ranking linked data. In 2012 IEEE 12th International Conference on Computer and Information Technology (pp. 427-434). IEEE.
  • Zhang, R., Wang, Y., & Wang, J. (2008). Research on ontology matching approach in semantic web. In 2008 International Conference on Internet Computing in Science and Engineering (pp. 254-257). IEEE.
  • Zhu, H., Zhong, J., Li, J., & Yu, Y. (2002). An approach for semantic search by matching RDF graphs. In FLAIRS Conference (pp. 450-454).

Semantik Web’de Ontoloji Benzerliklerinin Eşleşmesi

Year 2022, Volume: 12 Issue: 2, 263 - 267, 24.12.2022

Abstract

Ontolojileri eşleştirmek birçok Semantik Web uygulaması için önemli bir görev haline gelmiştir. Bu makale, iki benzerlik seviyesi kullanarak ontolojiler arasındaki etkili benzerlik eşleşmesini araştırmaktadır. Bu çalışmada, ilk olarak varlıkların hem morfolojisini hem de semantiğini hesaba katarak, ontoloji varlıklarının dilsel özelliklerinin benzerliklerinin incelenmesi ile başlamaktadır. Bu daha sonra bir RDF grafiği ile temsil edilen ontoloji yapısının bir karşılaştırması ile birleştirilmektedir. Bu benzerlik, eşleşen düğümlerden bir grafik oluşturularak ve yapı benzerliğinin ölçüsünü hesaplamak için kullanılarak hesaplanmaktadır.

References

  • Algergawy, A., Nayak, R., & Saake, G. (2010). Element similarity measures in XML schema matching. Information Sciences, 180(24), 4975-4998.
  • Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american, 284(5), 34-43.
  • Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. “ O’Reilly Media, Inc.”.
  • Blanchard, E., Harzallah, M., Briand, H., & Kuntz, P. (2005). A typology of ontology-based semantic measures. EMOIINTEROP, 160, 3-11.
  • Budanitsky, A., & Hirst, G. (2006). Evaluating wordnet-based measures of lexical semantic relatedness. Computational linguistics, 32(1), 13-47.
  • Cyganiak, R., Wood, D., Lanthaler, M., Klyne, G., Carroll, J. J., & McBride, B. (2014). RDF 1.1 concepts and abstract syntax. W3C recommendation, 25(02), 1-22.
  • Ding, L., Pan, R., Finin, T., Joshi, A., Peng, Y., & Kolari, P. (2005). Finding and ranking knowledge on the semantic web. In International Semantic Web Conference (pp. 156-170). Springer, Berlin, Heidelberg.
  • Graves, A., Adali, S., & Hendler, J. (2008). A Method to Rank Nodes in an RDF Graph. In International Semantic Web Conference (Posters & Demos) (Vol. 401).
  • Jeh, G., & Widom, J. (2002, July). Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538-543).
  • Klyne, G. (2004). RDF Concepts and Abstract Syntax W3C Recommendation. http://www. w3. org/TR/rdf-concepts/. Lin, D. (1998). An information-theoretic definition of similarity. In Icml (Vol. 98, No. 1998, pp. 296-304).
  • Liu, X., Tong, Q., Liu, X., & Qin, Z. (2021). Ontology matching: state of the art, future challenges and thinking based on utilized information. IEEE Access.
  • Lv, Q., Jiang, C., & Li, H. (2020). Solving ontology metamatching problem through an evolutionary algorithm with approximate evaluation indicators and adaptive selection pressure. IEEE Access, 9, 3046-3064.
  • Melnik, S., Garcia-Molina, H., & Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proceedings 18th international conference on data engineering (pp. 117-128). IEEE.
  • Miller, G. A., Fellbaum, C., Tengi, R., Wolff, S., Wakefield, P., Langone, H., & Haskell, B. (2006). WordNet: A lexical database for the English language. Cognitive Science Lab, Princeton University, http://www. cogsci. princeton. edu/wn.
  • Motik, B., & Patel-Schneider, P. (2012). OWL 2 Web Ontology Language Mapping to RDF Graphs.
  • Nayak, R., & Tran, T. (2007). A progressive clustering algorithm to group the XML data by structural and semantic similarity. International Journal of Pattern Recognition and Artificial Intelligence, 21(04), 723-743.
  • Qin, P., Lu, Z., Yan, Y., & Wu, F. (2009). A new measure of word semantic similarity based on wordnet hierarchy and dag theory. In 2009 International Conference on Web Information Systems and Mining (pp. 181-185). IEEE.
  • Ramasubramanian, C., & Ramya, R. (2013). Effective preprocessing activities in text mining using improved porter’s stemming algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 2(12), 4536-4538.
  • Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007.
  • Rice, S. V., Bunke, H., & Nartker, T. A. (1997). Classes of cost functions for string edit distance. Algorithmica, 18(2), 271-280.
  • Salman, A. (2020). Similarity matching of XML schema. Karaelmas Fen ve Mühendislik Dergisi, 10(1), 121-129.
  • Schneider, P., Hayes, P., & Horrocks, I. (2004). OWL Web Ontology Language Semantics and Abstract Syntax. W3C Recommendation. World Wide Web Consortium (W3C).
  • Uschold, M. (2003). Where are the semantics in the semantic web?. Ai Magazine, 24(3), 25-25. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E. G., &
  • Milios, E. E. (2005). Semantic similarity methods in wordnet and their application to information retrieval on the web. In Proceedings of the 7th annual ACM international workshop on Web information and data management (pp. 10-16).
  • Wang, Y., Li, Y., Fan, J., Ye, C., & Chai, M. (2021). A survey of typical attributed graph queries. World Wide Web, 24(1), 297- 346.
  • Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. arXiv preprint cmp-lg/9406033.
  • Zhang, D., Song, T., He, J., Shi, X., & Dong, Y. (2012). A similarity-oriented RDF graph matching algorithm for ranking linked data. In 2012 IEEE 12th International Conference on Computer and Information Technology (pp. 427-434). IEEE.
  • Zhang, R., Wang, Y., & Wang, J. (2008). Research on ontology matching approach in semantic web. In 2008 International Conference on Internet Computing in Science and Engineering (pp. 254-257). IEEE.
  • Zhu, H., Zhong, J., Li, J., & Yu, Y. (2002). An approach for semantic search by matching RDF graphs. In FLAIRS Conference (pp. 450-454).
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ayşe Salman 0000-0003-2649-3061

Publication Date December 24, 2022
Published in Issue Year 2022 Volume: 12 Issue: 2

Cite

APA Salman, A. (2022). Similarity Matching of Ontology in Semantic Web. Karaelmas Fen Ve Mühendislik Dergisi, 12(2), 263-267. https://doi.org/10.7212/karaelmasfen.1122025
AMA Salman A. Similarity Matching of Ontology in Semantic Web. Karaelmas Fen ve Mühendislik Dergisi. December 2022;12(2):263-267. doi:10.7212/karaelmasfen.1122025
Chicago Salman, Ayşe. “Similarity Matching of Ontology in Semantic Web”. Karaelmas Fen Ve Mühendislik Dergisi 12, no. 2 (December 2022): 263-67. https://doi.org/10.7212/karaelmasfen.1122025.
EndNote Salman A (December 1, 2022) Similarity Matching of Ontology in Semantic Web. Karaelmas Fen ve Mühendislik Dergisi 12 2 263–267.
IEEE A. Salman, “Similarity Matching of Ontology in Semantic Web”, Karaelmas Fen ve Mühendislik Dergisi, vol. 12, no. 2, pp. 263–267, 2022, doi: 10.7212/karaelmasfen.1122025.
ISNAD Salman, Ayşe. “Similarity Matching of Ontology in Semantic Web”. Karaelmas Fen ve Mühendislik Dergisi 12/2 (December 2022), 263-267. https://doi.org/10.7212/karaelmasfen.1122025.
JAMA Salman A. Similarity Matching of Ontology in Semantic Web. Karaelmas Fen ve Mühendislik Dergisi. 2022;12:263–267.
MLA Salman, Ayşe. “Similarity Matching of Ontology in Semantic Web”. Karaelmas Fen Ve Mühendislik Dergisi, vol. 12, no. 2, 2022, pp. 263-7, doi:10.7212/karaelmasfen.1122025.
Vancouver Salman A. Similarity Matching of Ontology in Semantic Web. Karaelmas Fen ve Mühendislik Dergisi. 2022;12(2):263-7.