Abstract
Deep neural networks are increasingly used for tasks such as entity resolution, sentiment analysis, and information extraction. As the methods are rather training data hungry, it is necessary to use large training sets in order to enable the methods to play their strengths. Millions of websites have started to annotate structured data within HTML pages using the schema.org vocabulary. Popular types of entities that are annotated are products, reviews, events, people, hotels, and other local businesses [12]. These semantic annotations are used by all major search engines to display rich snippets in search results. This is also the main driver behind the wide-scale adoption of the annotation techniques.
This article explores the potential of using semantic annotations from large numbers of websites as training data for supervised entity resolution, sentiment analysis, and information extraction methods. After giving an overview of the types of structured data that are available on the Semantic Web, we focus on the task of product matching in e‑commerce and explain how semantic annotations can be used to gather a large training dataset for product matching. The dataset consists of more than 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e‑shops, that provide schema.org annotations including some form of product identifiers, such as manufacturer part numbers (MPNs), global trade item numbers (GTINs), or stock keeping units (SKUs). The dataset, which we offer for public download, is orders of magnitude larger than the Walmart-Amazon [7], Amazon-Google [10], and Abt-Buy [10] datasets that are widely used to evaluate product matching methods. We verify the utility of the dataset as training data by using it to replicate the recent result of Mudgal et al. [15] stating that embeddings and RNNs outperform traditional symbolic matching methods on tasks involving less structured data. After the case study on product data matching, we focus on sentiment analysis and information extraction and discuss how semantic annotations from the Web can be used as training data within both tasks.
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Notes
Examples of pay level domains are for instance amazon.de or https://www.ebay.co.uk/.
Regex applied to each predicate URI for capturing identity revealing properties: .*/(gtin8|gtin12|gtin13|gtin14|sku|mpn|identifier|productID).
The review data can be downloaded from: http://webdatacommons.org/structureddata/2018-12/stats/schema_org_subsets.html.
References
Achichi M, Cheatham M, Dragisic Z et al (2017) Results of the ontology alignment evaluation initiative 2017. In: Proceedings of the 12th ISWC Workshop on Ontology Matching, pp 61–113
Das S et al The magellan data repository. https://sites.google.com/site/anhaidgroup/useful-stuff/data. Accessed on 31.01.2019.
Daskalaki E, Flouris G, Fundulaki I, Tzanina S (2016) Instance matching benchmarks in the era of Linked Data. J Web Semant 39C:1–14
Deriu J et al (2017) Leveraging large amounts of weakly supervised data for multi-language sentiment classification. In: Proceedings of the 26th International Conference on World Wide Web – WWW ’17. ACM Press, Perth, pp 1045–1052
Ebraheem M, Thirumuruganathan S, Joty S, Ouzzani M, Tang N (2018) Distributed representations of tuples for entity resolution. Proc VLDB Endow 11:1454–1467
Foley J, Bendersky M, Josifovski V (2015) Learning to extract local events from the web. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 423–432
Gokhale C et al (2014) Corleone: hands-off crowdsourcing for entity matching. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data – SIGMOD ’14. ACM Press, Snowbird, pp 601–612
Kärle E, Fensel A, Toma I, Fensel D (2016) Why are there more hotels in Tyrol than in Austria? Analyzing schema.org usage in the hotel domain. In: Proceedings of the International Conference on Information and Communication Technologies in Tourism 2016. Springer, Cham, pp 99–112
Konda P et al (2016) Magellan: toward building entity matching management systems over data science stacks. Proc VLDB Endow 9(13):1581–1584
Köpcke H, Thor A, Rahm E (2010) Evaluation of entity resolution approaches on real-world match problems. Proc VLDB Endow 3(1–2):484–493
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167
Meusel et al (2014) The webdatacommons microdata, RDFa and microformat dataset series. In: Proceedings of the International Semantic Web Conference, pp 277–292
Meusel R, Paulheim H (2015) Heuristics for fixing common errors in deployed schema.org microdata. In: The semantic web. Latest advances and new domains. Springer, Cham, pp 152–168
Meusel R, Paulheim H (2015) Creating large-scale training and test corpora for extracting structured data from the web. In: Proceedings of third workshop on linked data for information extraction
Mudgal S et al (2018) Deep learning for entity matching: a design space exploration. In: Proceedings of the 2018 International Conference on Management of Data – SIGMOD ’18. ACM Press, Houston, pp 19–34
Petrovski P, Bizer C (2017) Extracting attribute-value pairs from product specifications on the web. In: Proceedings of the International Conference on Web Intelligence – WI ’17. ACM Press, Leipzig, pp 558–565
Petrovski P, Bryl V, Bizer C (2014) Integrating product data from websites offering microdata markup. In: Proceedings of the 23rd International Conference on World Wide Web – WWW ’14 Companion. ACM Press, Seoul, pp 1299–1304
Petrovski P, Primpeli A, Meusel R, Bizer C (2017) The WDC gold standards for product feature extraction and product matching. In: Proceedings of the International Conference on E‑Commerce and Web Technologies. Springer, Cham, pp 73–86
Qiu D, Barbosa L, Dong XL, Shen Y, Srivastava D (2015) Dexter: large-scale discovery and extraction of product specifications on the web. Proc VLDB Endow 8(13):2194–2205
Ristoski P, Petrovski P, Mika P, Paulheim H (2018) A machine learning approach for product matching and categorization. Semant Web 9(5):707–728
Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp 502–518
Severyn A, Moschitti A (2015) Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval – SIGIR ’15. ACM Press, Santiago, pp 959–962
Shah K, Kopru S, Ruvini JD (2018) Neural network based extreme classification and similarity models for product matching. In: Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: human language technologies. Industry papers, vol 3. Association for Computational Linguistics, New Orleans, pp 8–15
Suganthan P, Doan A et al (2017) Falcon: scaling up hands-off crowdsourced entity matching to build cloud services. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp 1431–1446
Tang D et al (2016) Sentiment embeddings with applications to sentiment analysis. IEEE Trans Knowl Data Eng 28(2):496–509
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Bizer, C., Primpeli, A. & Peeters, R. Using the Semantic Web as a Source of Training Data. Datenbank Spektrum 19, 127–135 (2019). https://doi.org/10.1007/s13222-019-00313-y
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DOI: https://doi.org/10.1007/s13222-019-00313-y