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

Previous research on the overall graph structure of the World Wide Web mostly focused on the page level, meaning that the graph that directly results from hyperlinks between individual web pages was analyzed. This paper aims to provide additional insights about the macroscopic structure of the World Web Web by analyzing an aggregated version of a recent web graph. The graph covers over 3.5 billion web pages and 128 billion hyperlinks between pages. It was crawled in the first half of 2012. We aggregate this graph by pay-level domain (PLD), meaning that all pages that belong to the same pay-level domain are represented by a single node and that an arc exists between two nodes if there is at least one hyperlink between pages of the corresponding pay-level domains. The resulting PLD graph covers 43 million PLDs and contains 623 million arcs between PLDs. Analyzing this aggregated graph allows us to present findings about linkage patterns between complete websites and not only individual HTML pages. In this paper, we present basic statistics about the PLD graph, such as degree distributions, top-ranked PLDs, distances and diameter. We analyze whether the bow-tie structure introduced by Broder et al. can also be identified in our PLD graph and reveal a backbone of highly interlinked websites within the graph. We group the websites by top-level domain and report findings about the overall linkage within and between different top-level domains. In a last experiment, we use data from the Open Directory Project (DMOZ) to categorize websites by topic and report findings about linkage patterns between websites belonging to different topical categories.

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Graph structure in the web: aggregated by pay-level domain

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).

The Berlin SPARQL Benchmark.

Many data mining problems can be solved better if more background knowledge is added: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is tedious manual work. In this paper, we introduce the RapidMiner Linked Open Data Extension, which can extend a dataset at hand with additional attributes drawn from the Linked Open Data (LOD) cloud, a large collection of publicly available datasets on various topics. The extension contains operators for linking local data to open data in the LOD cloud, and for augmenting it with additional attributes. In a case study, we show that the prediction error of car fuel consumption can be reduced by 50% by adding additional attributes, e.g., describing the automobile layout and the car body configuration, from Linked Open Data.

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Data Mining with Background Knowledge from the Web

Large numbers of websites have started to markup their content using standards such as Microdata, Microformats, and RDFa. The marked-up content elements comprise descriptions of people, organizations, places, events, products, ratings, and reviews. This development has accelerated in last years as major search engines such as Google, Bing and Yahoo! use the markup to improve their search results. Embedding semantic markup facilitates identifying content elements on webpages. However, the markup is mostly not as fine-grained as desirable for applications that aim to integrate data from large numbers of websites. This paper discusses the challenges that arise in the task of integrating descriptions of electronic products from several thousand e-shops that offer Microdata markup. We present a solution for each step of the data integration process including Microdata extraction, product classification, product feature extraction, identity resolution, and data fusion. We evaluate our processing pipeline using 1.9 million product offers from 9240 e-shops which we extracted from the Common Crawl 2012, a large public Web corpus.

Integrating product data from websites offering microdata markup

Linked Data sources on the Web use a wide range of different vocabularies to represent data describing the same type of entity. For some types of entities, like people or bibliographic record, common vocabularies have emerged that are used by multiple data sources. But even for representing data of these common types, different user communities use different competing common vocabularies. Linked Data applications that want to understand as much data from the Web as possible, thus need to overcome vocabulary heterogeneity and translate the original data into a single target vocabulary. To support application developers with this integration task, several Linked Data translation systems have been developed. These systems provide languages to express declarative mappings that are used to translate heterogeneous Web data into a single target vocabulary. In this paper, we present a benchmark for comparing the expressivity as well as the runtime performance of data translation systems. Based on a set of examples from the LOD Cloud, we developed a catalog of fifteen data translation patterns and survey how often these patterns occur in the example set. Based on these statistics, we designed the LODIB (Linked Open Data Integration Benchmark) that aims to reflect the real-world heterogeneities that exist on the Web of Data. We apply the benchmark to test the performance of two data translation systems, Mosto and LDIF, and compare the performance of the systems with the SPARQL 1.1 CONSTRUCT query performance of the Jena TDB RDF store.

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Christian Bizer

Papers

Graph structure in the web: aggregated by pay-level domain

The Berlin SPARQL Benchmark.

Data Mining with Background Knowledge from the Web

Integrating product data from websites offering microdata markup

Benchmarking the Performance of Linked Data Translation Systems