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

Over the last years, an increasing number of web sites have started to embed structured data into HTML documents as well as to publish structured data in addition to HTML documents directly on the Web. This trend has led to the extension of the Web with a global data space—the Web of Data. As the classic document Web, the Web of Data covers a wide variety of topics ranging from data describing people, organizations, and events over products and reviews to statistical data provided by governments as well as research data from various scientific disciplines. This chapter gives an overview of the topology of the Web of Data. We discuss the different techniques that are used to publish structured data on the Web and provide statistics about the amount and topics of the data currently published using each technique.

Topology of the Web of Data

The emerging Semantic Web technologies have the potential to deeply influence the further development of the Internet Economy. In this paper we propose a scenario based research approach to predict the effects of these new technologies on electronic markets and business models of traders and intermediaries. We develop a concrete scenario for the domain of business to consumer electronic commerce and explain its technical realization. Furthermore, we analyse the potential impacts on market participants and highlight a range of technical and organizational obstacles which have to be solved before semantic technologies can be deployed in an e-business context.

http://wifo5-03.informatik.uni-mannheim.de/bizer/pub/otm2003_Semmarkets.pdf

Business to Consumer Markets on the Semantic Web

Revision with unchanged content. Web-based information systems, such as search engines, news portals, elec­tronic markets and community sites, provide access to information origi­nating from numerous information providers. The quality of provided infor­mation varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore confron­ted with the increasingly difficult task to select high quality information from the vast amount of Web-accessible information. How can information sys­tems support users to distinguish high quality from low quality information? Which filtering mechanisms can be applied? How can filtering decisions be explained to the user? The book gives an overview about information quality assessment in context of web-based systems. Afterwards, a quality-driven information filtering framework is developed. The framework allows infor­mation consumers to apply a wide range of different filtering policies. In order to facilitate the information consumers' understanding of filtering decisions, the framework generates explanations why information satisfies a specific policy. The book targets Web developers who need to handle infor­mation quality problems within their applications as well as researchers wor­king on the topic.

Quality-Driven Information Filtering

A central problem in the context of the Web of Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. There exists a large body of research on entity resolution. Interestingly, most of the existing research focuses on entity resolution on dense data, meaning data that does not contain too many missing values. This paper sets a different focus and explores learning expressive linkage rules from as well as applying these rules to sparse data, i.e. data exhibiting a large amount of missing values. Sparse data is a common challenge in application domains such as e-commerce, online hotel booking, or online recruiting. We propose and compare three entity resolution methods that employ genetic programming to learn expressive linkage rules from sparse data. First, we introduce the GenLinkGL algorithm which learns groups of matching rules and applies specific rules out of these groups depending on which values are missing from a pair of records. Next, we propose GenLinkSA, which employs selective aggregation operators within rules. These operators exclude misleading similarity scores (which result from missing values) from the aggregations, but on the other hand also penalize the uncertainty that results from missing values. Finally, we introduce GenLinkComb, an algorithm which combines the central ideas of the previous two into one integrated method. We evaluate all methods using six benchmark datasets: three of them are e-commerce product datasets, the other datasets describe restaurants, movies, and drugs. We show improvements of up to 16% F-measure compared to handwritten rules, on average 12% F-measure improvement compared to the original GenLink algorithm, 15% compared to EAGLE, 8% compared to FEBRL, and 5% compared to CoSum-P.

Learning expressive linkage rules from sparse data

The Web contains a large number of relational HTML tables, which cover a multitude of different, often very specific topics. This rich pool of data has motivated a growing body of research on methods that use web table data to extend local tables with additional attributes or add missing facts to knowledge bases. Nearly all existing approaches for these tasks are limited to the extraction of binary relations from web tables, e.g. an unemployment number may only depend on the state. Inspecting randomly chosen tables on the Web quickly reveals that many relations in the tables are non-binary, e.g. unemployment numbers also depend on the point in time and the profession. Treating such n-ary relations as binary leads to data that cannot be interpreted correctly. The extraction of n-ary relations from web tables is complicated by two factors: 1. important attributes might be stated outside of the table; 2. relational web tables are usually too small for functional dependency discovery. This paper presents a method to synthesize n-ary relations from web tables for the use case of knowledge base extension. The method exploits information from the page around the table and stitches (combines) multiple tables from the same website. We apply the method to a corpus of 5 million web tables originating from 80 thousand different web sites and find that 38% of the synthesized relations are non-binary. We find different relations for the same dependent attribute, e.g. relations providing unemployment numbers based on time, location, or profession. By identifying groups of websites which provide these relations, we lay the foundation for applications in knowledge base augmentation and data search, which allow for a specific selection of relations that determine an attribute according to the applications' data requirements.

Christian Bizer

Papers

Topology of the Web of Data

Business to Consumer Markets on the Semantic Web

Quality-Driven Information Filtering

Learning expressive linkage rules from sparse data

Synthesizing N-ary Relations from Web Tables