An Effective XML Documents Clustering Method Using Word Embeddings for Heterogeneous Collections
Abstract
As the size of XML repositories is growing, XML data management becomes challenging as how these documents can be stored and retrieved. One way of resolving such issues is to group the documents into clusters so that documents within the same cluster are more related than documents in different clusters. This became necessary in order to aid indexing and retrieval of XML documents. Traditional documents clustering methods represents documents with models that fails to consider the semantic relation between words. In this paper, WEClusterX is proposed to semantically cluster XML documents. The idea behind WEClusterX is to pinpoint which concept is represented by a particular context. Firstly, a pre-trained Bidirectional Encoder Representations from Transformers (BERT) is used to extract and cluster embeddings. Then, a Context-Document matrix is generated from the cluster of embeddings. Finally, clusters were formed using the famous k-means algorithm. The method combines the statistical importance of words with their contextualized representation in documents in order to forms meaningful clusters. The proposed WEClusterX is evaluated using extensive experiments. Experimental results have demonstrated that our proposed clustering solution achieved better performance in terms of purity and entropy.
Keywords
XML documentDocuments clusteringBERTEmbeddingsHeterogeneous
References
[1]. Aggarwal, C.C., Ta, N., Wang, J., Feng, J. and Zaki, M.J., (2007). XProj: A framework for projected structural clustering of XML documents. Proceedings of the 13th ACM SIGKDD International Conference on knowledge discovery and data mining, San Jose, Califomia, USA, pp 46-55 [2]. Aggarwal, C.C. and Zhai, C. (2012). A survey of text clustering Algorithms. In: Aggarwal, C.C. and Zhai, C. (eds) Mining Text data. Springer, Boston, MA. http://doi.org/10.1007/978-1- 4614-3223-4_4. [3]. Alishahi,M., Naghibzadeh, M., and Aski, B.S., (2010). Tag name structure-based clustering of XML documents. International Journal of Computer and Electrical Engeneerining, Vol. 2(1). [4]. Antonellis, P., Makris, C., and Tsirakis, N., (2008). XEdge: clustering homogeneous and heterogeneous XML documents using edge summaries. Proceedings of the 2008 ACM Symposium on Applied Computing, New York, USA, pp. 1082-1088. [5]. Altingövde, I.S., Atilgan, D., and Ulusoy, O., (2010). Exploiting Index Pruning Methods for Clustering XML Collections. Proceedings of the 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, December 7-9, 2009, pp. 379-386. [6]. Bessine, K., Nehew, A., Cherroun, H., and Moussaoui, A., (2015). XCLSC: Structure and content-based clustering of XML documents. Proceedings of the 2015 12th International Symposium on Programming and Systems (ISPS), 28-30 April 2015. [7]. Costa, G., and Ortale, R., (2013). A latent semantic approach to xml clustering by content and structure based on non-negative matrix factorization. Proceedings of the 12th International Conference on Machine Learning and Applications, Miami, pp. 179-184. [8]. Costa, G., and Ortale, R., (2014). Xml document co-clustering via non-negative matrix tri- factorization. Proceedings of the IEEE 26th International conference on Tools with Artificial Intelligence (ICTAI), Cyprus, pp. 607-614. [9]. Costa, G., and Ortale, R., (2015). Fully-automatic xml clustering by structure-constrained phrases. Proceedings of the IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 146-153. [10]. Costa, G., and Ortale, R., (2017). XML Clustering by Structure-Constrained Phrases: A Fully-Automatic Approach Using Contextualized N-Grams. International Journal on Artificial Intelligence Tools, Vol. 26(1). [11]. Costa, G., and Ortale, R., (2018). Machine learning techniques for XML (co-) clustering by structure-constrained phrases. Information Retrieval Journal, Vol. 21(1), pp. 24-55. [12]. Dalamagas, T., Cheng, T., Winkel, K.J., Sellis, T., (2006). A methodology for clustering XML documents by structure. Information Systems, vol. 31(3) pp. 187-228. [13]. Devlin, J., Chang, M., Lee, K., and Toutanova, M., (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pp. 4171–4186, Minneapolis, Minnesota. [14]. Dongo, I., Ticona-Herrera, R., Cadinalle, Y., and Guzman, R., (2020). Semantic Similarity of XML Documents Based on Structural and Content Analysis. Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent Control, November 2020. [15]. Greco, S., Gullo, F., Ponti, G., and Tagarelli, A., (2011). Collaborative clustering of XML documents. Journal of Computer and System Sciences, Vol. 77 (6) pp. 988-1008. [16]. Jianwu, Y., Cheung, W.K., and Chen, X., (2005). Integrating element and term semantics for similarity-based XML document clustering. Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), Compiegne, France, 2005, pp. 222-228 [17]. Joulin, A., Edouard G., Piotr B.,& Mikolov, T. (2016). Bag of Tricks for Efficient Text Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 427–431, Valencia, Spain. [18]. Lalmas, M. (2009). XML RETRIEVAL. (G. Marchionini, Ed.). Morgan & Claypool http://doi.org/10.2200/S00203ED1V01Y200907ICR007 [19]. Lee, L.M., Yang, L.H., Hsu, W., and Yang, X., (2002). XClust: Clustering XML schemas for effective integration. Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 4-9, 2002. [20]. Leung, H.P., Chung, F.L., Chan, S.C.F., and Luk, R. (2005). XML document clustering using common XPath. Proceedings of the 2005 International Workshop on Challenges in Web Information Retrieval and Integration (WIRI 2005), 8-9 April 2005, Tokyo, Japan [21]. Mikolov, T., Sutskever, I., and Chen, K., et al. (2013) Distributed Representations of Words and Phrases and Their Compositionality. Proc of the 26th International Conference on Neural Information Processing Systems, Curran Associates Inc., USA, 3111-3119. [22]. Nayak, R.,and Xia, F.B.,(2004) Automatic Integration of Heterogenous XML-schemas. In Kotsis, G, Taniar, D, Bressan, S, & Ibrahim, I K (Eds.) The Sixth International Conference on Information Integration and Web-based Applications and Services. Oesterreichische Computer Gesellschaft, Bandung, Indonesia, pp. 427-436. [23]. Nayak, R., and Tran, T., (2007). A progressive clustering algorithm to group XML data by structural and semantic similarity. International Journal of Pattern Recognition and Artificial Intelligence. Vol. 21(4). Pp. 723-743. [24]. Nearman, A., and Jagadish, H.V., (2002). Evaluating Structural Similarity in XML Documents. Proceedings of the 5th international conference on computational science (ICCS), Wisconsin, USA. [25]. Pennington,J., Socher,R., & Manning C.( 2014). GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics. [26]. Rezk, N.G., Sarhan,A., and Algergawy, A., (016). Clustering of XML documents based on structure and aggregated content, Proceedings of the 11th International Conference on Computer Engineering & Systems (ICCES), Egypt, pp. 93-102. [27]. Samadi, N., and Ravana, S.D., (2023). XML Clustering Framework based on document content and structure in a heterogenous digital library. Malaysian Journal of Computer Science, Vol. 36 (3), pp. 124-147. [28]. Tagarelli, A., amnd Greco, S., (2006). Towards Semantic XML Clustering. In the proceedings of the SIAM International conference on data mining, pp. 188-198. [29]. Tran, T., Nayak, R. (2007). Evaluating the Performance of XML Document Clustering by Structure Only. In: Fuhr, N., Lalmas, M., Trotman, A. (eds) Comparative Evaluation of XML Information Retrieval Systems. INEX 2006. Lecture Notes in Computer Science, vol Springer, Berlin, Heidelberg. [30]. Tran, T., Nayak, R., Bruza, P. (2008). Document Clustering Using Incremental and Pairwise Approaches. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds) Focused Access to XML Documents. INEX 2007. Lecture Notes in Computer Science, vol 4862. Springer, Berlin, Heidelberg. [31]. Tran, T., Kutty, S., Nayak, R. (2009). Utilizing the Structure and Content Information for XML Document Clustering. In: Geva, S., Kamps, J., Trotman, A. (eds) Advances in Focused Retrieval. INEX 2008. Lecture Notes in Computer Science, vol 5631., pp. 460-468, Springer, Berlin, Heidelberg [32]. Vercoustre, AM., Fegas, M., Gul, S., Lechevallier, Y. (2006). A Flexible Structured-Based Representation for XML Document Mining. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds) Advances in XML Information Retrieval and Evaluation. INEX 2005. Lecture Notes in Computer Science, vol 3977. Springer, Berlin, Heidelberg. [33]. W
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