The WDC Gold Standards for Product Feature Extraction and Product Matching

Petrovski, Petar; Primpeli, Anna; Meusel, Robert; Bizer, Christian

doi:10.1007/978-3-319-53676-7_6

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…Baselines. As baselines for the WDC dataset, we repeat TF-IDF cosine similarity and Paragrph2Vec experiments presented in [40], additionally we learn a decision tree and a random forest as baselines. The first baseline, considers pair-wise matching of product descriptions for which TF-IDF vectors are calculated using the bag-of-word feature extraction method.…”

Section: Methodsmentioning

confidence: 99%

“…Table 5 gives an overview of the matching results on the WDC Product Matching Gold Standard dataset. As baselines, we take TF-IDF cosine similarity and Para-grph2Vec experiments presented in [40], and decision tree and random forest explained above. Moreover, we compare results from: (i) handwritten matching rules;, (ii) the GenLink algorithm, (iii) GenLinkGL, (iv) Gen-LinkSA and (v) GenLinkComb.…”

Section: Methodsmentioning

confidence: 99%

“…The Amazon-Google data set has also been widely used as benchmark data set [23]. WDC Product Matching Gold Standard: This gold standard [40] for product matching contains correspondences between 1500 products (500 each from the categories headphones, mobile phones, and TVs), collected from 32 different websites and a unified product catalog containing 150 products with the following distribution: (1) Headphones -50, (2) Phones -50, and (3) TVs -50. The data in the catalog has been scraped from leading shopping services, like Google Shopping, or directly from the vendor's website.…”

Section: Datasetsmentioning

confidence: 99%

“…It is worth to note that the product name property in these datasets is actually a short description of the product mentioning different properties rather than the actual product name. WDC Product Matching Gold Standard contains a small set of properties with a density above 90% while most properties belong to the long tail of rather sparse properties [40].…”

Section: Datasetsmentioning

confidence: 99%

“…Amazon and eBay) is difficult as most websites publish heterogeneous product descriptions using proprietary schemata which vary widely concerning their level of detail [31]. For instance in [40], we analyzed product data from 32 popular e-shops. The shops use within each product category (mobile phones, headphones, TVs) approximately 30 different attributes to describe items.…”

Section: Introductionmentioning

confidence: 99%

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Learning expressive linkage rules from sparse data

Petrovski

Bizer

2020

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A central problem in the context of the Web of Data, as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. There exists a large body of research on entity resolution. Interestingly, most of the existing research focuses on entity resolution on dense data, meaning data that does not contain too many missing values. This paper sets a different focus and explores learning expressive linkage rules from as well as applying these rules to sparse web data, i.e. data exhibiting a large amount of missing values. Such data is a common challenge in various application domains including e-commerce, online hotel booking, or online recruiting. We propose and compare three entity resolution methods that employ genetic programming to learn expressive linkage rules from sparse data. First, we introduce the GenLinkGL algorithm which learns groups of matching rules and applies specific rules out of these groups depending on which values are missing from a pair of records. Next, we propose GenLinkSA, which employs selective aggregation operators within rules. These operators exclude misleading similarity scores (which result from missing values) from the aggregations, but on the other hand also penalize the uncertainty that results from missing values. Finally, we introduce GenLinkComb, a method which combines the central ideas of the previous two into one integrated method. We evaluate all methods using six benchmark datasets: three of them are e-commerce product datasets, the other datasets describe restaurants, movies, and drugs. We show improvements of up to 16% F-measure compared to handwritten rules, on average 12% F-measure improvement compared to the original GenLink algorithm, 15% compared to EAGLE, 8% compared to FEBRL, and 5% compared to CoSum-P.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning expressive linkage rules from sparse data

Petrovski

Bizer

2020

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Unsupervised Bootstrapping of Active Learning for Entity Resolution

2020

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Entity resolution is one of the central challenges when integrating data from large numbers of data sources. Active learning for entity resolution aims to learn high-quality matching models while minimizing the human labeling effort by selecting only the most informative record pairs for labeling. Most active learning methods proposed so far, start with an empty set of labeled record pairs and iteratively improve the prediction quality of a classification model by asking for new labels. The absence of adequate labeled data in the early active learning iterations leads to unstable models of low quality which is known as the cold start problem. In our work we solve the cold start problem using an unsupervised matching method to bootstrap active learning. We implement a thresholding heuristic that considers pre-calculated similarity scores and assigns matching labels with some degree of noise at no manual labeling cost. The noisy labels are used for initializing the active learning process and throughout the whole active learning cycle for model learning and query selection. We evaluate our pipeline with six datasets from three different entity resolution settings using active learning with a committee-based query strategy and show it successfully deals with the cold start problem. Comparing our method against two active learning baselines without bootstrapping, we show that it can additionally lead to overall improved learned models in terms of F1 score and stability.

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Web Mining

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2023

Machine Learning for Data Science Handbook

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The WDC Gold Standards for Product Feature Extraction and Product Matching

Cited by 9 publications

References 12 publications

Learning expressive linkage rules from sparse data

Learning expressive linkage rules from sparse data

Unsupervised Bootstrapping of Active Learning for Entity Resolution

Web Mining

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