Data Mining - Concepts and Techniques.

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

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Linked Data - the story so far

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

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DBpedia: a nucleus for a web of open data

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

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Character-level convolutional networks for text classification

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

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DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

Editorial: The Semantic Web Challenge, 2011

In order to deliver a coherent user experience, product aggregators such as market places or price portals integrate product offers from many web shops into a single product categorization hierarchy. Recently, transformer models have shown remarkable performance on various NLP tasks. These models are 
pre-trained on huge cross-domain text corpora using self-supervised learning and fine-tuned afterwards for specific downstream tasks. Research from other application domains indicates that additional selfsupervised pre-training using domain-specific text corpora can further increase downstream performance without requiring additional task-specific training data. In this paper, we first show that transformers outperform a more traditional fastText-based classification technique on the task of assigning product offers from different web shops into a product hierarchy. Afterwards, we investigate whether it is possible to improve the performance of the transformer models by performing additional self-supervised pretraining using different corpora of product offers, which were extracted from the Common Crawl. Our experiments show that by using large numbers of related product offers for masked language modelling, it is possible to increase the performance of the transformer models by 1.22% in wF1 and 1.36% in hF1 reaching a performance of nearly 89% wF1.

Improving hierarchical product classification using domain-specific language modelling

Microformat, Microdata and RDFa data from the 2009 Common Crawl web corpus. We found structured data within 147 million HTML pages out of the 2 billion pages contained in the crawl (5%). These pages originate from 19 million different pay-level-domains. Altogether, the extracted data sets consist of 5 billion RDF quads.

Web Data Commons - RDFa, Microdata, and Microformat 2009/2010 Data Set

Das World Wide Web wandelt sich von einem Medium zur Veroffentlichung von Texten zu einem Medium zur Veroffentlichung von strukturierten Daten. Neben Web-2.0-APIs spielen bei dieser Entwicklung zunehmend Linked-Data-Technologien eine zentrale Rolle. Linked-Data-Technologien ermoglichen die Vernetzung von Datenbanken mittels Datenlinks auf Basis der Webstandards HTTP-URIs und RDF (Resource Description Framework). Das Linked Data Web deckt ein breites Themenspektrum ab, unter anderem beinhaltet es Informationen zu Orten, Personen, Ereignissen, Publikationen, Musik, Filmen sowie biowissenschaftliche Daten. Semantische Mashups sind Anwendungen, die diesen Datenraum nutzen. Der Artikel erlautert die technologischen Grundlagen von Linked Data und gibt anhand von Beispielen einen uberblick uber den derzeitigen Entwicklungsstand semantischer Mashups.

Semantische Mashups auf Basis des Linked Data Web

Semantische Mashups sind Anwendungen, die vernetzte Daten aus mehreren Web-Datenquellen mittels standardisierter Datenformate und Zugriffsmechanismen nutzen. Der Artikel gibt einen Uberblick uber die Idee und Motivation der Vernetzung von Daten. Es werden verschiedene Architekturen und Ansatze zur Generierung von RDF-Daten aus bestehenden Web 2.0- Datenquellen, zur Vernetzung der extrahierten Daten sowie zur Veroffentlichung der Daten im Web anhand konkreter Beispiele diskutiert. Hierbei wird insbesondere auf Datenquellen, die aus sozialen Interaktionen hervorgegangen sind eingegangen. Anschliesend wird ein Uberblick uber verschiedene, im Web frei zugangliche semantische Mashups gegeben und auf leichtgewichtige Inferenzansatze eingegangen, mittels derer sich die Funktionalitat von semantischen Mashups weiter verbessern lasst.

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Christian Bizer

Papers

Editorial: The Semantic Web Challenge, 2011

Improving hierarchical product classification using domain-specific language modelling

Web Data Commons - RDFa, Microdata, and Microformat 2009/2010 Data Set

Semantische Mashups auf Basis des Linked Data Web

Semantische Mashups auf Basis Vernetzter Daten