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Erschienen in:
Detecting Similar Linked Datasets Using Topic Modelling Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Enriching Product Ads with Metadata from HTML Annotations

verfasst von : Petar Ristoski, Peter Mika

Erschienen in: The Semantic Web. Latest Advances and New Domains

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Fast Approximate A-Box Consistency Checking Using Machine Learning
Nächstes Kapitel Iterative Entity Navigation via Co-clustering Semantic Links and Entity Classes
Fußnoten
1
Search advertising is a method of placing online advertisements on web pages that show results from search engine queries.
 
2
We need to note that we apply the same approach for identifying duplicates within the dataset of unstructured product descriptions, and for identifying matches between the unstructured product descriptions and the structured product ads.
 
6
We tried calculating the similarity based on different combination of title and description, but the best results were delivered when using only the product title.
 
7
For example, two laptops might share the same brand, same CPU, and same HDD, but if the memory differs, then the laptops are not the same.
 
8
We were not able to build a sufficiently good CRF model that is able to annotate text with high precision because of the many possible attributes across all categories. A separate CRF model for each category in the structured database of product ads should be trained.
 
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Meusel, R., Primpeli, A., Meilicke, C., Paulheim, H., Bizer, C.: Exploiting microdata annotations to consistently categorize product offers at web scale. In: Stuckenschmidt, H., Jannach, D. (eds.) EC-Web 2015. LNBIP, vol. 239, pp. 83–93. Springer, Heidelberg (2015)CrossRef
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Petrovski, P., Bryl, V., Bizer, C.: Integrating product data from websites offering microdata markup. In: Proceedings of the 23rd International Conference on World Wide Web Companion, pp. 1299–1304 (2014)
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Petrovski, P., Bryl, V., Bizer, C.: Learning regular expressions for the extraction of product attributes from e-commerce microdata (2014)
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Metadaten
Titel
Enriching Product Ads with Metadata from HTML Annotations
verfasst von
Petar Ristoski
Peter Mika
Copyright-Jahr
2016
Buch
The Semantic Web. Latest Advances and New Domains

Print ISBN: 978-3-319-34128-6

Electronic ISBN: 978-3-319-34129-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-34129-3_10

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