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

An increasing number of data providers have adopted shared numbering schemes such as GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This means for data integr...

/pdf/dual-objective-fine-tuning-of-bert-for-entity-matching-59wqpb9dz3.pdf

Dual-objective fine-tuning of BERT for entity matching

A recurrent problem with the way research data is stored, processed, and accessed is that scientific work environments, as well as research data infrastructures, remain isolated solutions that focus on data from specific disciplines or data produced within specific geographic regions, such as the European Union or the United States. Moreover, scientific work environments and research data infrastructures are based on a wide range of different technical architectures; this hampers the exchange of data among systems. In most cases, the research data infrastructures that are currently in operation remain isolated data silos that supply scientists with the data the systems were designed to deliver but do not allow scientists to discover all data that is available world-wide on a topic or to discover data on related topics provided by a different research data infrastructure.

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Interlinking Scientific Data on a Global Scale

Comparison shopping portals integrate product offers from large numbers of e-shops in order to support consumers in their buying decisions. Product offers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the specific vendor. In addition, product offers might contain structured or semi-structured product specifications in the form of HTML tables and HTML lists. As product specifications often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these specifications is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attribute-value pairs from product specifications on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product specification or not. In order to extract attribute-value pairs from the HTML fragments identified by the specification detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attribute-value pairs from product specifications, we introduce several new features for specification detection and support the extraction of attribute-value pairs from specifications having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 different e-shops. This experiment confirms the suitability of duplicate-based schema matching for product data integration.

Extracting attribute-value pairs from product specifications on the web

LDOW 2014 : Proceedings of the Workshop on Linked Data on the Web co-located with the 23rd International World Wide Web Conference (WWW 2014); Seoul, Korea, April 8, 2014

The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of Data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.

Christian Bizer

Papers

Dual-objective fine-tuning of BERT for entity matching

Interlinking Scientific Data on a Global Scale

Extracting attribute-value pairs from product specifications on the web

LDOW 2014 : Proceedings of the Workshop on Linked Data on the Web co-located with the 23rd International World Wide Web Conference (WWW 2014); Seoul, Korea, April 8, 2014

DBpedia - A Crystallization Point for the Web of Data