Exploiting Microdata Annotations to Consistently Categorize Product Offers at Web Scale

Meusel, Robert; Primpeli, Anna; Meilicke, Christian; Paulheim, Heiko; Bizer, Christian

doi:10.1007/978-3-319-27729-5_7

Cited by 12 publications

(26 citation statements)

References 13 publications

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“…[11] is the most recent approach for exploiting Microdata annotations for categorization of products data. In this approach the authors exploit the already assigned s:Category property to develop distantly supervised approaches to map the products to set of target categories from an existing product catalog.…”

Section: Related Workmentioning

confidence: 99%

“…Following Meusel et al [11], in order to weigh the different features for each of the elements in the two input sets, we apply two different strategies, Binary Term Occurrence (BTO) and TF-IDF. In the end we use the feature vectors to build a classification model.…”

Section: Product Categorizationmentioning

confidence: 99%

“…The first level contains 38 different categories, the second level 113 categories and the third level 783 categories. To evaluate the proposed approach we use the Microdata products gold standard developed in [11]. We removed non-English instances from the dataset, resulting in 8,362 products.…”

Section: Product Categorizationmentioning

confidence: 99%

“…In this work we focus on data annotated with the Microdata markup format using the schema.org vocabulary. Recent works [10,11] have shown that the Microdata format is the most commonly used markup format, with highest domain and entity coverage. Also, schema.org is the most frequently used vocabulary to describe products.…”

Section: Introductionmentioning

confidence: 99%

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Enriching Product Ads with Metadata from HTML Annotations

Ristoski

Mika

2016

The Semantic Web. Latest Advances and New Domains

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Abstract. Product ads are a popular form of search advertizing offered by major search engines, including Yahoo, Google and Bing. Unlike traditional search ads, product ads include structured product specifications, which allow search engine providers to perform better keyword-based ad retrieval. However, the level of completeness of the product specifications varies and strongly influences the performance of ad retrieval. On the other hand, online shops are increasing adopting semantic markup languages such as Microformats, RDFa and Microdata, to annotate their content, making large amounts of product description data publicly available. In this paper, we present an approach for enriching product ads with structured data extracted from thousands of online shops offering Microdata annotations. In our approach we use structured product ads as supervision for training feature extraction models able to extract attribute-value pairs from unstructured product descriptions. We use these features to identify matching products across different online shops and enrich product ads with the extracted data. Our evaluation on three product categories related to electronics show promising results in terms of enriching product ads with useful product data.

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Section: Related Workmentioning

confidence: 99%

Section: Product Categorizationmentioning

confidence: 99%

Section: Product Categorizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enriching Product Ads with Metadata from HTML Annotations

Ristoski

Mika

2016

The Semantic Web. Latest Advances and New Domains

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show abstract

“…A significant amount of structured data is embedded in Web pages today [2]. While this limits the opportunities for data publishers to trace the usage of that embedded data directly, it plays a crucial role in modern eCommerce when it is extracted [13] and thus may be indirectly tracked when consumers are driven to Web sites as a result of improved ranking in search engines.…”

Section: How To Interact With the Web Of Data?mentioning

confidence: 99%