Learning expressive linkage rules using genetic programming

Isele, Robert; Bizer, Christian

doi:10.14778/2350229.2350276

Cited by 124 publications

(130 citation statements)

References 30 publications

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…The approach is based on the Silk rule learning framework [7], which is able to identify matching products based on their attributes. To do so, different combination of features from the product descriptions are used, e.g., bag of words, attribute-value pairs extracted using a dictionary, features extracted using manually written regular expressions, and combination of all.…”

Section: Related Workmentioning

confidence: 99%

“…Unstructured Product Offers -WDC Microdata Dataset The latest extraction of WebDataCommons includes over 5 billion entities marked up by one of the three main HTML markup languages (i.e., Microdata, Microformats and RDFa) and has been retrieved from the CommonCrawl 2014 corpus 7 . From this dataset we focus on product entities annotated with Microdata using the schema.org vocabulary.…”

Section: Datasetsmentioning

confidence: 99%

“…As a second baseline we use the Silk Link Discovery Framework [7], an opensource tool for discovering links between data items within different data sources. The tool uses genetic algorithm to learn linkage rules based on the extracted attributes.…”

Section: Experiments Setupmentioning

confidence: 99%

See 2 more Smart Citations

Enriching Product Ads with Metadata from HTML Annotations

Ristoski

Mika

2016

The Semantic Web. Latest Advances and New Domains

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Enriching Product Ads with Metadata from HTML Annotations

Ristoski

Mika

2016

The Semantic Web. Latest Advances and New Domains

View full text Add to dashboard Cite

show abstract

“…-Fitness evaluation assigns a fitness to each element in the population (lines [13][14][15][16]. In our case, the fitness of an individual (i.e.…”

Section: Evolutionary Searchmentioning

confidence: 99%

Pay-as-you-go Configuration of Entity Resolution

Maskat

Paton

Embury

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Entity resolution, which seeks to identify records that represent the same entity, is an important step in many data integration and data cleaning applications. However, entity resolution is challenging both in terms of scalability (all-against-all comparisons are computationally impractical) and result quality (syntactic evidence on record equivalence is often equivocal). As a result, end-to-end entity resolution proposals involve several stages, including blocking to efficiently identify candidate duplicates, detailed comparison to refine the conclusions from blocking, and clustering to identify the sets of records that may represent the same entity. However, the quality of the result is often crucially dependent on configuration parameters in all of these stages, for which it may be difficult for a human expert to provide suitable values. This paper describes an approach in which a complete entity resolution process is optimized, on the basis of feedback (such as might be obtained from crowds) on candidate duplicates. Given such feedback, an evolutionary search of the space of configuration parameters is carried out, with a view to maximizing the fitness of the resulting clusters. The approach is payas-you-go in that more feedback can be expected to give rise to better outcomes. An empirical evaluation shows that the co-optimization of the different stages in entity resolution can yield significant improvements over default parameters, even with small amounts of feedback.

show abstract

“…It is used to find out the potential comparison pairs, as shown in [5][6][7]. That is, from which classes and properties values should be compared.…”

Section: State Of the Artmentioning

confidence: 99%

Data Linking with Ontology Alignment

Fan

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Learning expressive linkage rules using genetic programming

Cited by 124 publications

References 30 publications

Enriching Product Ads with Metadata from HTML Annotations

Enriching Product Ads with Metadata from HTML Annotations

Pay-as-you-go Configuration of Entity Resolution

Data Linking with Ontology Alignment

Contact Info

Product

Resources

About