Showing papers by "Christian Bizer published in 2010"

Faceted Wikipedia Search

Abstract: Wikipedia articles contain, besides free text, various types of structured information in the form of wiki markup. The type of wiki content that is most valuable for search are Wikipedia infoboxes, which display an article’s most relevant facts as a table of attribute-value pairs on the top right-hand side of the Wikipedia page. Infobox data is not used by Wikipedia’s own search engine. Standard Web search engines like Google or Yahoo also do not take advantage of the data. In this paper, we present Faceted Wikipedia Search, an alternative search interface for Wikipedia, which facilitates infobox data in order to enable users to ask complex questions against Wikipedia knowledge. By allowing users to query Wikipedia like a structured database, Faceted Wikipedia Search helps them to truly exploit Wikipedia’s collective intelligence.

LDspider: an open-source crawling framework for the web of linked data

Abstract: The Web of Linked Data is growing and currently consists of several hundred interconnected data sources altogether serving over 25 billion RDF triples to the Web. What has hampered the exploitation of this global dataspace up till now is the lack of an open-source Linked Data crawler which can be employed by Linked Data applications to localize (parts of) the dataspace for further processing. With LDSpider, we are closing this gap in the landscape of publicly available Linked Data tools. LDSpider traverses the Web of Linked Data by following RDF links between data items, it supports different crawling strategies and allows crawled data to be stored either in files or in an RDF store.

Silk server - adding missing links while consuming linked data

Abstract: The Web of Linked Data is built upon the idea that data items on the Web are connected by RDF links. Sadly, the reality on the Web shows that Linked Data sources set some RDF links pointing at data items in related data sources, but they clearly do not set RDF links to all data sources that provide related data. In this paper, we present Silk Server, an identity resolution component, which can be used within Linked Data application architectures to augment Web data with additional RDF links. Silk Server is designed to be used with an incoming stream of RDF instances, produced for example by a Linked Data crawler. Silk Server matches the RDF descriptions of incoming instances against a local set of known instances and discovers missing links between them. Based on this assessment, an application can store data about newly discovered instances in its repository or fuse data that is already known about an entity with additional data about the entity from the Web. Afterwards, we report on the results of an experiment in which Silk Server was used to generate RDF links between authors and publications from the Semantic Web Dog Food Corpus and a stream of FOAF profiles that were crawled from the Web.

The R2R framework: publishing and discovering mappings on the web

Abstract: The promise of the Web of Linked Data is to enable client applications to discover new data sources by following RDF links at run-time and to smoothly integrate data from these sources. Linked Data sources use different vocabularies to describe the same type of objects. It is also common practice to mix terms from different widely used vocabularies with proprietary terms. Thus Linked Data applications need to apply mappings to translate Web data to their local schema before doing any sophisticated data processing. Maintaining a local or central set of mappings that cover all Linked Data sources is likely to be impossible due to the size and dynamics of the Web of Linked Data. Thus this paper propagates a distributed, pay-as-you-go integration approach where data publishers, vocabulary maintainers and third parties may publish expressive mappings on the Web. A client application which discovers data that is represented using terms that are unknown to the application may search the Web for mappings and apply the discovered mappings to translate data to its local schema. We propose a language for publishing expressive, named mappings on the Web and a composition method for chaining partial mappings from different sources based on a mapping quality assessment heuristic. The composition method is implemented within the R2R Mapping Engine which can be used by Linked Data applications to translate Web data to their local schema.

Silk - generating RDF links while publishing or consuming linked data

Abstract: The central idea of the Web of Data is to interlink data items using RDF links. However, in practice most data sources are not sufficiently interlinked with related data sources. The Silk Link Discovery Framework addresses this problem by providing tools to generate links between data items based on user-provided link specifications. It can be used by data publishers to generate links between data sets as well as by Linked Data consumers to augment Web data with additional RDF links. In this poster we present the Silk Link Discovery Framework and report on two usage examples in which we employed Silk to generate links between two data sets about movies as well as to find duplicate persons in a stream of data items that is crawled from the Web.

Extending SMW+ with a linked data integration framework

Abstract: In this paper, we present a project which extends a SMW+ semantic wiki with a Linked Data Integration Framework that performs Web data access, vocabulary mapping, identity resolution, and quality evaluation of Linked Data. As a result, a large collection of neurogenomics-relevant data from the Web can be flexibly transformed into a unified ontology, allowing unified querying, navigation, and visualization; as well as support for wiki-style collaboration, crowdsourcing, and commentary on chosen data sets.

The web of linked data: a global public dataspace on the web: WebDB 2010 keynote

Abstract: In 2005, Michael Franklin, Alon Halevy, and David Maier coined the term dataspaces as a new abstraction and target architecture for data management. In 2006, Tim Berners-Lee introduced the Linked Data principles a set of best practices for publishing and interlinking structured data on the Web in accordance with the general architecture of the Web. These two lines of thinking have come together and are realized in the Web of Linked Data - a global public dataspace. The Web of Linked Data was kick started by the W3C Linking Open Data community effort in 2007. Today, the Web of Linked Data consists of hundreds of datasets published by universities, companies, government and public sector bodies, as well as by individual Web enthusiasts. The content of the Web of Linked Data is diverse in nature, comprising data about people, organizations, products, geographic locations, books, scientific publications, films, music, television and radio programs, genes, proteins, drugs and clinical trials, online communities, statistical data, census results, and reviews. In addition to publishing and interlinking datasets, there is intensive work on Linked Data browsers, Web of Linked Data search engines and other applications that consume Linked Data from the Web. In his talk, Christian Bizer will introduce the principle ideas behind Linked Data and relate them to the dataspace architecture. Afterwards, he will give an overview of the identity resolution and pay-as-you-go data integration techniques that are currently used in the context of the Web of Linked Data and will explain the state-of-the-art in applications that consume Linked Data from the Web.

Semantische Mashups auf Basis des Linked Data Web

Abstract: 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.