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This talk was given at the IIPC General Assembly in Paris in May 2014. It introduces the distributed, parallel extraction framework provided by the Web Data Commons project. The framework is public accessible and tailored for the Amazon Web Service Stack. Besides the presentation includes an excerpt of datasets which were extracted from over 100 TB of crawling data and are as well available at http://webdatacommons.org.

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CORE hosts the world’s largest collection of open access full texts, offering seamless, unrestricted access to research for citizens, researchers, libraries, software developers, funders and others. CORE’s aggregated content comes from thousands of institutional and subject repositories as well as journals and covers all research disciplines. In January 2019, CORE has hit the mark of 10 million monthly active users (10.41 million users). In September 2019, core.ac.uk has made it to the top 5k websites globally by user engagement as measured by the independent Alexa Rank, making it clearly one of the world’s most widely used Open Access services. In this talk, Petr and Nancy will explain the role of CORE in the open science ecosystem. They will introduce the solutions CORE offers for improving the delivery of research literature, including tools for discovering freely available copies of papers that might be behind publishers’ paywalls as well as a recommender system for open access literature. The use of CORE data to monitor compliance with open access policies has also recently received attention. The presenters will then reflect on the challenges in the sector and share their experience of building value-added services for the society on top of open content offered by libraries and their affiliated institutional repositories and open access journals.

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The slideset describes the Mannheim Search Join Engine and was used to present our submission to the Semantic Web Challenge 2014. More information about the Semantic Web Challenge: http://challenge.semanticweb.org/ Paper about the Mannheim Search Join Engine: http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/ki/pub/Lehmberg-Ritze-Ristoski-Eckert-Paulheim-Bizer-TableExtension-SemanticWebChallenge-ISWC2014-Paper.pdf Abstract: This Big Data Track submission demonstrates how the BTC 2014 dataset, Microdata annotations from thousands of websites, as well as millions of HTML tables are used to extend local tables with additional columns. Table extension is a useful operation within a wide range of application scenarios: Image you are an analyst having a local table describing companies and you want to extend this table with the headquarter of each company. Or imagine you are a film lover and want to extend a table describing films with attributes like director, genre, and release date of each film. The Mannheim SearchJoin Engine automatically performs such table extension operations based on a large data corpus gathered from over a million websites that publish structured data in various formats. Given a local table, the SearchJoin Engine searches the corpus for additional data describing the entities of the input table. The discovered data are then joined with the local table and their content is consolidated using schema matching and data fusion methods. As result, the user is presented with an extended table and given the opportunity to examine the provenance of the added data. Our experiments show that the Mannheim SearchJoin Engine achieves a coverage close to 100% and a precision of around 90% within different application scenarios.

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The Web contains vast amounts of structured data in the form of HTML tables, schema.org annotations, as well as datasets accessible via data repositories. The automated integration of data from larger numbers of Web data sources is a long-standing research challenge as the integration requires dealing with several tricky tasks such as schema matching, entity matching, and data indexing for retrieval. Most state-of-the-art methods for these tasks rely on variants of the BERT transformer model fine-tuned using significant amounts of task-specific training data. In the talk, Christian Bizer will critically review BERT-based data integration methods and question their robustness concerning out-of-distribution entities. He will compare the performance of BERT-based methods with results of GPT-4-based data integration methods and will argue that GPT-4-based methods are more training data efficient and more robust concerning unseen entities.

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The adoption of schema.org annotations on the Web has sharply increased over the last years with hundreds of thousands of websites annotating information about products, events, local businesses, reviews, and job postings within their pages. In the talk, Christian Bizer will discuss the integration of schema.org product data from large numbers of websites for the use cases of building product knowledge graphs as well as comparing product prizes across e-shops. The key challenge for this integration is to determine which webpages describe the same product. Christian Bizer will demonstrate how this challenge can be handled by deriving a large pool of training data from schema.org annotations and using this data to train transformer-based product matchers. He will discuss how the matchers exploit the richness of the training data available for widely sold head products using multi-task learning but can also excel on matching long-tail products using contrastive pre-training as well as cross-language learning.

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Current research on knowledge graph completion focuses on employing graph embeddings for the task of link prediction. But knowledge graph completion is more than link prediction and tasks such as adding formerly unknown long-tail entities to the graph, extending the schema of the graph with additional properties, and completing and updating numeric values are equally important tasks. In the talk, Christian Bizer will review recent results on using data from large numbers of independent websites to accomplish these tasks. He will focus on two types of web content – relational HTML tables and semantic annotations within HTML pages – and will discuss the potential of these types of content for set completion, schema extension, and fact checking, as well as their utility as training data for matching textual entity descriptions.

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http://iswc2016.semanticweb.org/pages/program/keynote-bizer.html Semantic Web technologies, such as Linked Data and Schema.org, are used by a significant number of websites to support the automated processing of their content. In the talk, I will contrast the original vision of the Semantic Web with empirical findings about the adoption of Semantic Web technologies on the Web. The analysis will show areas in which data providers behave as envisioned by the Semantic Web community but will also reveal areas in which real-world adoption patterns strongly deviate. Afterwards, I will discuss the challenges that result from the current adoption situation. To address these challenges, I will exemplify entity reconciliation, vocabulary matching, and data quality assessment techniques which exploit all semantic clues that are provided while being tolerant to noise and lazy data providers.

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The amount of structured data that is published on the Web has increased sharply over the last years. The deluge of available data calls for new search techniques which support users in finding and integrating data from large numbers of data sources. In his talk, Christian Bizer will give an overview of the different types of data search that have been proposed so far: Entity search, table search, constraint and unconstraint search joins. As an example of a system from the last category, he will introduce the Mannheim Search Join Engine which provides for executing unconstraint search joins over different types of Web data including Linked Data, Microdata, Web tables and Wikipedia tables.

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The Web contains large amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities. Relational web tables cover a wide range of topics and there is a growing body of research investigating the utility of web table data for applications such as complementing cross-domain knowledge bases, extending arbitrary tables with additional attributes, and translating data values. Until recently, most of the research around web tables originated from the large search engine companies as they were the only ones having access to large web crawls and thus were able to extract web table corpora from the crawls. This situation has changed in 2012 with the University of Mannheim and in 2014 with the Dresden University of Technology starting to extract web table corpora from the CommonCrawl, a large public web corpus. In the talk, I will introduce the 2015 version of the Web Data Commons - Web Table Corpus. Afterward, I will give an overview of the different efforts that are currently conducted by my group on exploring the application potential of relational web tables. These efforts include profiling the content of web tables by matching them to cross-domain knowledge bases such as DBpedia, fusing web table data in order to complement cross-domain knowledge bases, and performing SearchJoins between a local table and a web table corpus in order to extend the local table with additional attributes.

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