Semantik Web’de Ontoloji Benzerliklerinin Eşleşmesi
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.
Similarity Matching of Ontology in Semantic Web
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.
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- 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).