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Semantische Mashups auf Basis des Linked Data Web
Abstract
Das World Wide Web wandelt sich von einem Medium zur Veröffentlichung von Texten zu einem Medium zur Veröffentlichung von strukturierten Daten. Neben Web-2.0-APIs spielen bei dieser Entwicklung zunehmend Linked-Data-Technologien eine zentrale Rolle. Linked-Data-Technologien ermöglichen 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 erläutert die technologischen Grundlagen von Linked Data und gibt anhand von Beispielen einen überblick über den derzeitigen Entwicklungsstand semantischer Mashups.
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Advances in the biological sciences are allowing pharmaceutical companies to meet the health care crisis with drugs that are more suitable for preventive and tailored treatment, thereby holding the promise of enabling more cost effective care with greater efficacy and reduced side effects. However, this shift in business model increases the need for companies to integrate data across drug discovery, drug development, and clinical practice. This is a fundamental shift from the approach of limiting integration activities to functional areas. The Linked Data approach holds much potential for enabling such connectivity between data silos, thereby enabling pharmaceutical companies to meet the urgent needs in society for more tailored health care. This paper examines the applicability and potential benefits of using Linked Data to connect drug and clinical trials related data sources and gives an overview of ongoing work within the W3C's Semantic Web for Health Care and Life Sciences Interest Group on publishing drug related data sets on the Web and interlinking them with existing Linked Data sources. A use case is provided that demonstrates the immediate benefit of this work in enabling data to be browsed from disease, to clinical trials, drugs, targets and companies.
The Web of Data is built upon two simple ideas: Employ the RDF data model to publish structured data on the Web and to set explicit RDF links between entities within different data sources. This paper presents the Silk – Link Discovery Framework, a tool for finding relationships between entities within different data sources. Data publishers can use Silk to set RDF links from their data sources to other data sources on the Web. Silk features a declarative language for specifying which types of RDF links should be discovered between data sources as well as which conditions entities must fulfill in order to be interlinked. Link conditions may be based on various similarity metrics and can take the graph around entities into account, which is addressed using a path-based selector language. Silk accesses data sources over the SPARQL protocol and can thus be used without having to replicate datasets locally.
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.
The Web of Linked Data forms a single, globally distributed dataspace. Due to the openness of this dataspace, it is not possible
to know in advance all data sources that might be relevant for query answering. This openness poses a new challenge that is
not addressed by traditional research on federated query processing. In this paper we present an approach to execute SPARQL
queries over the Web of Linked Data. The main idea of our approach is to discover data that might be relevant for answering
a query during the query execution itself. This discovery is driven by following RDF links between data sources based on URIs
in the query and in partial results. The URIs are resolved over the HTTP protocol into RDF data which is continuously added
to the queried dataset. This paper describes concepts and algorithms to implement our approach using an iterator-based pipeline.
We introduce a formalization of the pipelining approach and show that classical iterators may cause blocking due to the latency
of HTTP requests. To avoid blocking, we propose an extension of the iterator paradigm. The evaluation of our approach shows
its strengths as well as the still existing challenges.
In order to employ the Web as a medium for data and in- formation integration, comprehensive datasets and vocabularies are re- quired as they enable the disambiguation and alignment of other data and information. Many real-life information integration and aggregation tasks are impossible without comprehensive background knowledge re- lated to spatial features of the ways, structures and landscapes surround- ing us. In this paper we contribute to the generation of a spatial dimen- sion for the Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be transformed and represented adhering to the RDF data model, how this data can be interlinked with other spatial data sets, how it can be made accessible for machines according to the linked data paradigm and for humans by means of a faceted geo-data browser.
In this paper, we present a framework for online discovery of semantic links from relational data. Our framework is based on declarative specification of the linkage requirements by the user, that allows matching data items in many real-world scenarios. These requirements are translated to queries that can run over the relational data source, potentially using the semantic knowledge to enhance the accuracy of link discovery. Our framework lets data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.
In this paper, we describe how the BBC is working to inte- grate data and linking documents across BBC domains by using Semantic Web technology, in particular Linked Data, MusicBrainz and DBpedia. We cover the work of BBC Programmes and BBC Music building Linked Data sites for all music and programmes related brands, and we describe existing projects, ongoing development, and further research we are doing in a joint collaboration between the BBC, Freie Universitat Berlin and Rattle Research in order to use DBpedia as the controlled vocabulary and semantic backbone for the whole BBC.
Along with the rapid growth of the data Web, searching linked objects for information needs and for reusing become emergent for ordinary Web users and developers, respectively. To meet the challenge, we present Falcons Object Search, a keyword-based search engine for linked objects. To serve various keyword queries, for each object the system constructs a comprehensive virtual document including not only associated literals but also the textual descriptions of associated links and linked objects. The resulting objects are ranked by considering both their relevance to the query and their popularity. For each resulting object, a query-relevant structured snippet is provided to show the associated literals and linked objects matched with the query. Besides, Web-scale class-inclusion reasoning is performed to discover implicit typing information, and users could navigate class hierarchies for incremental class-based results filtering. The results of a task-based experiment show the promising features of the system.
Along with the rapid growth of the data Web, searching linked objects for information needs and for reusing become emergent for ordinary Web users and developers, respectively. To meet the challenge, we present Falcons Object Search, a keyword-based search engine for linked objects. To serve various keyword queries, for each object the system constructs a comprehensive virtual document including not only associated literals but also the textual descriptions of associated links and linked objects. The resulting objects are ranked by considering both their relevance to the query and their popularity. For each resulting object, a query-relevant structured snippet is provided to show the associated literals and linked objects matched with the query. Besides, Web-scale class-inclusion reasoning is performed to discover implicit typing information, and users could navigate class hierarchies for incremental class-based results filtering. The results of a task-based experiment show the promising features of the system.
The development of relational database management systems served to focus the data management community for decades, with spectacular results. In recent years, however, the rapidly-expanding demands of "data everywhere" have led to a field comprised of interesting and productive efforts, but without a central focus or coordinated agenda. The most acute information management challenges today stem from organizations (e.g., enterprises, government agencies, libraries, "smart" homes) relying on a large number of diverse, interrelated data sources, but having no way to manage their dataspaces in a convenient, integrated, or principled fashion. This paper proposes dataspaces and their support systems as a new agenda for data management. This agenda encompasses much of the work going on in data management today, while posing additional research objectives.
Semantic Web technologies facilitate data integration over a large number of sources with decentralised and loose coordination,
ideally leading to interlinked datasets which describe objects, their attributes and links to other objects. Such information
spaces are amenable to queries that go beyond traditional keyword search over documents. To this end, we present a formal
query model comprising six atomic operations over object-structured datasets: keyword search, object navigation, facet selection,
path traversal, projection, and sorting. Using these atomic operations, users can incrementally assemble complex queries that
yield a set of objects or trees of objects as result. Results can then be either directly displayed or exported to application
programs or online services. We report on user experiments carried out during the design phase of the system, and present
performance results for a range of queries over 18.5m statements aggregated from 70k sources.
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.
Current search engines do not fully leverage semantically rich datasets, or specialise in indexing just one domain-specific dataset.We present a search engine that uses the RDF data model to enable interactive query answering over richly structured and interlinked data collected from many disparate sources on the Web.
The Geospatial Semantic Web makes locations first-class citizens of the Web by representing them as original Web resources. This allows locations to be described in an open and distributed manner using the Resource Description Framework and provides for interlinking data about locations between data sources. In addition to using geo-coordinates to express geographical proximity, the Geospatial Semantic Web provides for relating locations as well as regions to each other using explicit semantic relationship types such as containment or shared borders. This article gives an overview of the Geospatial Semantic Web and describes DBpedia Mobile, a location-aware Semantic Web client that can be used on an iPhone and other mobile devices. Based on the current GPS position, DBpedia Mobile renders a map indicating nearby locations from the DBpedia data set. Starting from this map, the user can explore background information about his surroundings by navigating along data links into other data sources. DBpedia Mobile has been designed for the use case of a tourist exploring a city. Besides accessing Web data, DBpedia Mobile also enables users to publish their current location, pictures and reviews to the Semantic Web so that they can be used by other Semantic Web applications. Instead of simply being tagged with geographical coordinates, published content is interlinked with a nearby DBpedia resource and thus contributes to the overall richness of the Geospatial Semantic Web.
From databases to dataspaces: a new ab-straction for information management
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