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Auflistung nach Schlagwort "Entity resolution"

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  • Textdokument
    The Best of Both Worlds: Combining Hand-Tuned and Word-Embedding-Based Similarity Measures for Entity Resolution
    (BTW 2019, 2019) Chen, Xiao; Campero Durand, Gabriel; Zoun, Roman; Broneske, David; Li, Yang; Saake, Gunter
    Recently word embedding has become a beneficial technique for diverse natural language processing tasks, especially after the successful introduction of several popular neural word embedding models, such as word2vec, GloVe, and FastText. Also entity resolution, i.e., the task of identifying digital records that refer to the same real-world entity, has been shown to benefit from word embedding. However, the use of word embeddings does not lead to a one-size-fits-all solution, because it cannot provide an accurate result for those values without any semantic meaning, such as numerical values. In this paper, we propose to use the combination of general word embedding with traditional hand-picked similarity measures for solving ER tasks, which aims to select the most suitable similarity measure for each attribute based on its property. We provide some guidelines on how to choose suitable similarity measures for different types of attributes and evaluate our proposed hybrid method on both synthetic and real datasets. Experiments show that a hybrid method reliant on correctly selecting required similarity measures can outperform the method of purely adopting traditional or word-embedding-based similarity measures.
  • Zeitschriftenartikel
    Parallel Entity Resolution with Dedoop
    (Datenbank-Spektrum: Vol. 13, No. 1, 2013) Kolb, Lars; Rahm, Erhard
    We provide an overview of Dedoop (Deduplication with Hadoop), a new tool for parallel entity resolution (ER) on cloud infrastructures. Dedoop supports a browser-based specification of complex ER strategies and provides a large library of blocking and matching approaches. To simplify the configuration of ER strategies with several similarity metrics, training-based machine learning approaches can be employed with Dedoop. Specified ER strategies are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. For improved performance, Dedoop supports redundancy-free multi-pass blocking as well as advanced load balancing approaches. To illustrate the usefulness of Dedoop, we present the results of a comparative evaluation of different ER strategies on a challenging real-world dataset.
  • Zeitschriftenartikel
    Using the Semantic Web as a Source of Training Data
    (Datenbank-Spektrum: Vol. 19, No. 2, 2019) Bizer, Christian; Primpeli, Anna; Peeters, Ralph
    Deep neural networks are increasingly used for tasks such as entity resolution, sentiment analysis, and information extraction. As the methods are rather training data hungry, it is necessary to use large training sets in order to enable the methods to play their strengths. Millions of websites have started to annotate structured data within HTML pages using the schema.org vocabulary. Popular types of entities that are annotated are products, reviews, events, people, hotels, and other local businesses [ 12 ]. These semantic annotations are used by all major search engines to display rich snippets in search results. This is also the main driver behind the wide-scale adoption of the annotation techniques. This article explores the potential of using semantic annotations from large numbers of websites as training data for supervised entity resolution, sentiment analysis, and information extraction methods. After giving an overview of the types of structured data that are available on the Semantic Web, we focus on the task of product matching in e‑commerce and explain how semantic annotations can be used to gather a large training dataset for product matching. The dataset consists of more than 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e‑shops, that provide schema.org annotations including some form of product identifiers, such as manufacturer part numbers (MPNs), global trade item numbers (GTINs), or stock keeping units (SKUs). The dataset, which we offer for public download, is orders of magnitude larger than the Walmart-Amazon [ 7 ], Amazon-Google [ 10 ], and Abt-Buy [ 10 ] datasets that are widely used to evaluate product matching methods. We verify the utility of the dataset as training data by using it to replicate the recent result of Mudgal et al. [ 15 ] stating that embeddings and RNNs outperform traditional symbolic matching methods on tasks involving less structured data. After the case study on product data matching, we focus on sentiment analysis and information extraction and discuss how semantic annotations from the Web can be used as training data within both tasks.