Ralph Peeters
Abstract
An increasing number of data providers have adopted shared numbering schemes such as GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This means for data integration that shared identifiers are often available for a subset of the entity descriptions to be integrated while such identifiers are not available for others. The challenge in these settings is to learn a matcher for entity descriptions without identifiers using the entity descriptions containing identifiers as training data. The task can be approached by learning a binary classifier which distinguishes pairs of entity descriptions for the same real-world entity from descriptions of different entities. The task can also be modeled as a multi-class classification problem by learning classifiers for identifying descriptions of individual entities. We present a dual-objective training method for BERT, called JointBERT, which combines binary matching and multi-class classification, forcing the model to predict the entity identifier for each entity description in a training pair in addition to the match/non-match decision. Our evaluation across five entity matching benchmark datasets shows that dual-objective training can increase the matching performance for seen products by 1% to 5% F1 compared to single-objective Transformer-based methods, given that enough training data is available for both objectives. In order to gain a deeper understanding of the strengths and weaknesses of the proposed method, we compare JointBERT to several other BERT-based matching methods as well as baseline systems along a set of specific matching challenges. This evaluation shows that JointBERT, given enough training data for both objectives, outperforms the other methods on tasks involving seen products, while it underperforms for unseen products. Using a combination of LIME explanations and domain-specific word classes, we analyze the matching decisions of the different deep learning models and conclude that BERT-based models are better at focusing on relevant word classes compared to RNN-based models.
- Nils Barlaug and Jon Atle Gulla. 2021. Neural Networks for Entity Matching: A Survey. ACM Transactions on Knowledge Discovery from Data 15, 3 (2021), 52:1--52:37.Google Scholar
- Alexander Brinkmann and Christian Bizer. 2021. Improving Hierarchical Product Classification using Domain-specific Language Modelling. In Proceedings of Workshop on Knowledge Management in e-Commerce.Google Scholar
- Ursin Brunner and Kurt Stockinger. 2020. Entity Matching with Transformer Architectures - a Step Forward in Data Integration. In Proceedings of the International Conference on Extending Database Technology. 463--473.Google Scholar
- Rich Caruana. 1997. Multitask Learning. Machine Learning 28 (1997), 41--75. Google ScholarDigital Library
- Peter Christen. 2012. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer-Verlag, Berlin Heidelberg. Google ScholarDigital Library
- Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2020. An Overview of End-to-End Entity Resolution for Big Data. Comput. Surveys 53, 6 (2020), 127:1--127:42. Google ScholarDigital Library
- Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. ELECTRA:Pre-Training Text Encoders as Discriminators Rather Than Generators. In 8th International Conference on Learning Representations.Google Scholar
- Valter Crescenzi, Andrea De Angelis, Donatella Firmani, Maurizio Mazzei, Paolo Merialdo, et al. 2021. Alaska: A Flexible Benchmark for Data Integration Tasks. arXiv:2101.11259 [cs] (Feb. 2021).Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1. 4171--4186.Google Scholar
- Vincenzo Di Cicco, Donatella Firmani, Nick Koudas, Paolo Merialdo, and Divesh Srivastava. 2019. Interpreting Deep Learning Models for Entity Resolution: An Experience Report Using LIME. In Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. 8:1--8:4. Google ScholarDigital Library
- William B Dolan and Chris Brockett. 2005. Automatically constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing. 9--16.Google Scholar
- Amr Ebaid, Saravanan Thirumuruganathan, Walid G. Aref, Ahmed Elmagarmid, and Mourad Ouzzani. 2019. EXPLAINER: Entity Resolution Explanations. In 2019 IEEE 35th International Conference on Data Engineering. 2000--2003.Google Scholar
- Muhammad Ebraheem, Saravanan Thirumuruganathan, Shafiq Joty, Mourad Ouzzani, and Nan Tang. 2018. Distributed Representations of Tuples for Entity Resolution. Proceedings of the VLDB Endowment 11, 11 (2018), 1454--1467. Google ScholarDigital Library
- Ahmed K. Elmagarmid, Panagiotis G. Ipeirotis, and Vassilios S. Verykios. 2007. Duplicate Record Detection: A Survey. IEEE Transactions on Knowledge and Data Engineering 19, 1 (2007), 1--16. Google ScholarDigital Library
- Ivan P. Fellegi and Alan B. Sunter. 1969. A Theory for Record Linkage. J. Amer. Statist. Assoc. 64, 328 (1969), 1183--1210.Google ScholarCross Ref
- Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, et al. 2020. XTREME: A Massively Multilingual Multi-Task Benchmark for Evaluating Cross-Lingual Generalisation. In International Conference on Machine Learning. 4411--4421.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations.Google Scholar
- Pradap Konda, Sanjib Das, Paul Suganthan G. C., AnHai Doan, Adel Ardalan, et al. 2016. Magellan: Toward Building Entity Matching Management Systems. Proceedings of the VLDB Endowment 9, 12 (2016), 1197--1208. Google ScholarDigital Library
- Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, et al. 2020. ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations. In 8th International Conference on Learning Representations.Google Scholar
- Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020. Deep Entity Matching with Pre-Trained Language Models. Proceedings of the VLDB Endowment 14, 1 (2020), 50--60. Google ScholarDigital Library
- Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. Multi-Task Deep Neural Networks for Natural Language Understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 4487--4496.Google ScholarCross Ref
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, et al. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 [cs] (July 2019).Google Scholar
- Sidharth Mudgal, Han Li, Theodoros Rekatsinas, AnHai Doan, Youngchoon Park, et al. 2018. Deep Learning for Entity Matching: A Design Space Exploration. In Proceedings of the 2018 International Conference on Management of Data. 19--34. Google ScholarDigital Library
- Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, et al. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. 8024--8035. Google ScholarDigital Library
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, et al. 2011. Scikit-Learn: Machine Learning in Python. Journal of machine learning research 12 (2011), 2825--2830. Google ScholarDigital Library
- Ralph Peeters, Anna Primpeli, Benedikt Wichtlhuber, and Christian Bizer. 2020. Using Schema.Org Annotations for Training and Maintaining Product Matchers. In Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. 195--204.Google ScholarDigital Library
- Anna Primpeli, Ralph Peeters, and Christian Bizer. 2019. The WDC Training Dataset and Gold Standard for Large-Scale Product Matching. In Companion Proceedings ofThe 2019 World Wide Web Conference. 381--386. Google ScholarDigital Library
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135--1144. Google ScholarDigital Library
- Sebastian Ruder. 2017. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv:1706.05098 [cs, stat] (June 2017).Google Scholar
- Kashif Shah, Selcuk Kopru, and Jean David Ruvini. 2018. Neural Network Based Extreme Classification and Similarity Models for Product Matching. In Proceedings of the 2018 Conference of the Association for Computational Linguistics, Volume 3. 8--15.Google ScholarCross Ref
- Kai-Sheng Teong, Lay-Ki Soon, and Tin Tin Su. 2020. Schema-Agnostic Entity Matching Using Pre-Trained Language Models. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2241--2244. Google ScholarDigital Library
- Saravanan Thirumuruganathan, Mourad Ouzzani, and Nan Tang. 2019. Explaining Entity Resolution Predictions: Where Are We and What Needs to Be Done?. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics. 1--6. Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, et al. 2017. Attention Is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 6000--6010. Google ScholarDigital Library
- Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R Thomas McCoy, et al. 2019. Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 4465--4476.Google ScholarCross Ref
- Thomas Wolf, Lysandre Debut,Victor Sanh,Julien Chaumond,Clement Delangue, et al. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 38--45.Google ScholarCross Ref
- Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020. TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 8413--8426.Google ScholarCross Ref
- Ziqi Zhang, Christian Bizer, Ralph Peeters, and Anna Primpeli. 2020. MWPD2020: Semantic Web Challenge on Mining the Web of HTML-Embedded Product Data. In CEUR Workshop Proceedings, Vol. 2720. 2--18.Google Scholar
Index Terms
- Dual-objective fine-tuning of BERT for entity matching
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