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检索条件"任意字段=1st Semantic Web Challenge on Mining the Web of HTML-Embedded Product Data, MWPD 2020"

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mwpd 2020 - Proceedings of the semantic web challenge on mining the web of html-embedded product data, co-located with the 19th International semantic web Conference, ISWC 2020

MWPD 2020 - Proceedings of the Semantic Web Challenge on Min...

引用

1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

The proceedings contain 7 papers. The topics discussed include: mwpd2020: semantic web challenge on mining the web of html-embedded product data;PMap: ensemble pre-training models for product matching;a pre-trained matching model based on Selfand inter-ensemble for product matching task;ISCAS_ICIP at mwpd-2020 task 1: product matching based on deep entity matching frameworks;language model CNN-driven similarity matching and classification for html-embedded product data;BERT with dynamic masked Softmax and pseudo labeling for hierarchical product classification;and ProBERT: product data classification with fine-tuning BERT model.

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mwpd2020: semantic web challenge on mining the web of html-embedded product data 1

MWPD2020: Semantic web challenge on mining the web of html-e...

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1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Zhang, Ziqi Bizer, Christian Peeters, Ralph Primpeli, Anna University of Sheffield Broomhall SheffieldS10 2TG United Kingdom Universität Mannheim Schloss Mannheim68131 Germany

This paper gives an overview of the semantic web challenge on mining the web of html-embedded product data (mwpd2020) which has been conducted as part of the International semantic web Conference (ISWC2020). The challenge consists of two tasks: product matching and product classification. In the first task, participants need to identify offers for the same product originating from different websites. The goal of the second task is to categorize offers from different websites into the GS1 GPC product hierarchy. Six teams from the USA, China, Japan, and Germany participated in the challenge. The winning system in Task 1, PMap, achieved an F1 score of 86.05 using an ensemble of transformer-based language models. Task 2 was won by team Rhinobird achieving a weighted average F1 score of 88.62 using a BERT-based ensemble which considers the dependencies among different category levels. Copyright © 2020 for this paper by its authors.

关键词： websites

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PMap: Ensemble pre-training models for product matching 1

PMap: Ensemble pre-training models for product matching

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1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Kertkeidkachorn, Natthawut Ichise, Ryutaro National Institute of Advanced Industrial Science and Technology Tokyo135-0064 Japan National Institute of Informatics Tokyo101-8430 Japan

mining theweb of html-embedded product data (mwpd) challenge aims to benchmark methods dealing with two e-commerce data integration tasks: 1 ) product Matching and 2) product Classification. In this paper, we present the design of our system, namely PMap, for the mwpd challenge on the product Matching task. PMap aggregates the results of the various state of the art pretraining models to resolve the identical products. Results on MWHPD show that PMap outperforms the baseline and obtains the promising performance for the product matching task. The code and the system's outputs are available. Copyright © 2020 for this paper by its authors.

关键词： data integration

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Language model CNN-driven similarity matching and classification for html-embedded product data 1

Language model CNN-driven similarity matching and classifica...

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1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Borst, Janos Krner, Erik Opasjumruskit, Kobkaew Niekler, Andreas Leipzig University Faculty of Mathematics and Computer Science Institute of Computer Science Augustusplatz 10 Leipzig04109 Germany Institute of Data Science Mlzerstrae 3 Jena07745 Germany

The semantic web challenge mining the web of html- embedded product data aims to benchmark current technologies on the data integration tasks (1) product matching and (2) product classification, as recent years have seen significant use of semantic annotations in the e-commerce domain, but often with inconsistencies, no complete coverage or con icting information. We introduce a transformer-based approach for textual product matching and extend it with an CNN for product *** compare the in uence of different input feature combinations against prediction performance and introduce a technique to augment the classification task with additional information. We are able to outperform baseline results using text-only approaches. Copyright © 2020 for this paper by its authors.

关键词： Computational linguistics

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ISCAS_ICIP at mwpd-2020 task 1: product matching based on deep entity matching frameworks 1

ISCAS_ICIP at MWPD-2020 task 1: Product matching based on de...

引用

1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Fu, Cheng Wang, Tianshu Nie, Hao Han, Xianpei Sun, Le Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

This paper describes our product matching system developed for semantic web challenge on mining html-embedded product data 2020 (Task 1). product matching is the task of identifying product offers deriving from different websites that refer to the same real-world product, which is a typical scenario of entity matching (EM). In our system, we implement four state-of-theart deep learning-based entity matching models and integrate their results to get the final product matching predictions. Competition results show that, our system obtains promising performance on this task. Copyright © 2020 for this paper by its authors.

关键词： semantic web

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ProBERT: product data classification with fine-tuning BERT model 1

ProBERT: Product data classification with fine-tuning BERT m...

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1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Zahera, Hamada M. Sherif, Mohamed A. Data Science Group Paderborn University Germany

In this paper, we describe our submission to the semantic web challenge on mining the product data in websites (mwpd2020). The dataset provided 19K instances of product data collected from various websites. The task is to predict the category, defined as hierarchical taxonomy as provided in the training set, of the product titles in the test set. In our approach, we present a simple BERT-based model (dubbed ProBERT) for classifying product data into one or more *** trained our system on products titles and descriptions to learn semantic representation. The participated systems are evaluated using weighted-average precision, recall and F1-score. Copyright © 2020 for this paper by its authors.

关键词： websites

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A pre-trained matching model based on self- And inter-ensemble for product matching task 1

A pre-trained matching model based on self- And inter-ensemb...

引用

1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Xu, Shiyao Shijia, E. Yang, Li Xiang, Yang Tongji University Shanghai China Tencent Shanghai China

The product matching task aims to identify that if a pair of product deriving from different websites refer to the same product or not. While the accumulated semantic annotations of products make it possible to study deep neural network-based matching methods, product matching is still a challenging task due to suffering from the class imbalance and heterogeneity of textual descriptions. In this paper, we directly regard product matching as a semantic text matching problem and propose a pre-trained matching model based on both self- and inter-ensemble. BERT is the main module in our approach for binary classification of product pairs. We perform two types of ensemble methods: self-ensemble using stochastic weight averaging (SWA) for the same model, and inter-ensemble combing the prediction of different models. Additionally, the focal loss is adopted to alleviate the imbalance problem of positive and negative samples. Experimental results show that our model outperforms existing deep learning matching approaches. The proposed model achieves an F1-score of 85.94% on the test data which ranks second in the SWC2020 on mining the web of html-embedded product data Task One. Our implementation has been released. Copyright © 2020 for this paper by its authors.

关键词： stochastic systems

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Bert with dynamic masked softmax and pseudo labeling for hierarchical product classification 1

Bert with dynamic masked softmax and pseudo labeling for hie...

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1st semantic web challenge on mining the web of html-embedded product data, mwpd 2020

作者： Yang, Li Shijia, E. Xu, Shiyao Xiang, Yang Tongji University Shanghai China Tencent Shanghai China

ierarchical product classification (HPC) aims to assign prede fined product categories stored in a hierarchical structure to product instances. Categories at different levels of a product tend to have dependencies. However, most previous studies either decompose the original problem into a set of at classification sub-problems or deal with all categories simultaneously, lacking a focus on the dependencies among different category levels. In this paper, we propose a BERT-based ensemble model to address the HPC challenge in SWC2020mwpd Task 2. We devise a masked matrix for each category level based on the hierarchical category structure, which can dynamically filter out the child categories unrelated to the current parent category and eliminate the negative effect of category inconsistency. Further through a two-level ensemble strategy and pseudo labeling, our team Rhinobird wins first place with a weighted-average macro-F1 score of 88.62 on the testing dataset. Our source code is publicly available on Github. Copyright © 2020 for this paper by its authors.

关键词： BERT Dynamic Masked Softmax Hierarchical product Classification Pseudo Labeling

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