Claus Stadler

LinkedGeoData: A core for a web of spatial open data

The Semantic Web eases data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be interactively transformed and represented adhering to the RDF data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this data is 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 several applications, including a faceted geo-browser. The spatial data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.

Assessing linked data mappings using network measures

Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to know whether the links introduced improve or diminish the quality of Linked Data. In this paper, we present LINK-QA, an extensible framework that allows for the assessment of Linked Data mappings using network metrics. We test five metrics using this framework on a set of known good and bad links generated by a common mapping system, and show the behaviour of those metrics.

Managing the life-cycle of linked data with the LOD2 stack

The LOD2 Stack is an integrated distribution of aligned tools which support the whole life cycle of Linked Data from extraction, authoring/creation via enrichment, interlinking, fusing to maintenance. The LOD2 Stack comprises new and substantially extended existing tools from the LOD2 project partners and third parties. The stack is designed to be versatile; for all functionality we define clear interfaces, which enable the plugging in of alternative third-party implementations. The architecture of the LOD2 Stack is based on three pillars: ( 1 ) Software integration and deployment using the Debian packaging system. ( 2 ) Use of a central SPARQL endpoint and standardized vocabularies for knowledge base access and integration between the different tools of the LOD2 Stack. ( 3 ) Integration of the LOD2 Stack user interfaces based on REST enabled Web Applications. These three pillars comprise the methodological and technological framework for integrating the very heterogeneous LOD2 Stack components into a consistent framework. In this article we describe these pillars in more detail and give an overview of the individual LOD2 Stack components. The article also includes a description of a real-world usage scenario in the publishing domain.

DBpedia and the live extraction of structured data from Wikipedia

Purpose – DBpedia extracts structured information from Wikipedia, interlinks it with other knowledge bases and freely publishes the results on the web using Linked Data and SPARQL. However, the DBpedia release process is heavyweight and releases are sometimes based on several months old data. DBpedia‐Live solves this problem by providing a live synchronization method based on the update stream of Wikipedia. This paper seeks to address these issues.Design/methodology/approach – Wikipedia provides DBpedia with a continuous stream of updates, i.e. a stream of articles, which were recently updated. DBpedia‐Live processes that stream on the fly to obtain RDF data and stores the extracted data back to DBpedia. DBpedia‐Live publishes the newly added/deleted triples in files, in order to enable synchronization between the DBpedia endpoint and other DBpedia mirrors.Findings – During the realization of DBpedia‐Live the authors learned that it is crucial to process Wikipedia updates in a priority queue. Recently‐upd...

DBpedia Live Extraction

The DBpedia project extracts information from Wikipedia, interlinks it with other knowledge bases, and makes this data available as RDF. So far the DBpedia project has succeeded in creating one of the largest knowledge bases on the Data Web, which is used in many applications and research prototypes. However, the heavy-weight extraction process has been a drawback. It requires manual effort to produce a new release and the extracted information is not up-to-date. We extended DBpedia with a live extraction framework, which is capable of processing tens of thousands of changes per day in order to consume the constant stream of Wikipedia updates. This allows direct modifications of the knowledge base and closer interaction of users with DBpedia. We also show how the Wikipedia community itself is now able to take part in the DBpedia ontology engineering process and that an interactive round-trip engineering between Wikipedia and DBpedia is made possible.

Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge

The search for information on the Web of Data is becoming increasingly difficult due to its dramatic growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e. g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, we present in this paper a novel approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to return instant answers to the user even with large knowledge bases.

Exploring the web of spatial data with facete

The majority of data (including data published on the Web as Linked Open Data) has a spatial dimension. However, the efficient, user friendly exploration of spatial data remains a major challenge. We present Facete, a web-based exploration and visualization application enabling the spatial-faceted browsing of data with a spatial dimension. Facete implements a novel spatial data exploration paradigm based on the following three key components: First, a domain independent faceted filtering module, which operates directly on SPARQL and supports nested facets. Second, an algorithm that efficiently detects spatial information related to those resources that satisfy the facet selection. The detected relations are used for automatically presenting data on a map. And third, a workflow for making the map display interact with data sources that contain large amounts of geometric information. We demonstrate Facete in large-scale, real world application scenarios.

User-driven semantic mapping of tabular data

Governments and public administrations started recently to publish large amounts of structured data on the Web, mostly in the form of tabular data such as CSV files or Excel sheets. Various tools and projects have been launched aiming at facilitating the lifting of tabular data to reach semantically structured and linked data. However, none of these tools supported a truly incremental, pay-as-you-go data publication and mapping strategy, which enables effort sharing between data owners, community experts and consumers. In this article, we present an approach for enabling the user-driven semantic mapping of large amounts tabular data. We devise a simple mapping language for tabular data, which is easy to understand even for casual users, but expressive enough to cover the vast majority of potential tabular mappings use cases. We outline a formal approach for mapping tabular data to RDF. Default mappings are automatically created and can be revised by the community using a semantic wiki. The mappings are executed using a sophisticated streaming RDB2RDF conversion. We report about the deployment of our approach at the Pan-European data portal PublicData.eu, where we transformed and enriched almost 10,000 datasets accounting for 7.3 billion triples.

Knowledge extraction from structured sources

This chapter surveys knowledge extraction approaches from structured sources such as relational databases, XML and CSV. A general definition of knowledge extraction is devised that covers structured as well as unstructured sources. We summarize current progress on conversion of structured data to RDF and OWL. As an example, we provide a formalization and description of SparqlMap, which implements the relational database to RDF mapping language R2RML currently being standardized by the W3C.

Accessing Relational Data on the Web with SparqlMap

The vast majority of the structured data of our age is stored in relational databases. In order to link and integrate this data on the Web, it is of paramount importance to make relational data available according to the RDF data model and associated serializations. In this article we present SparqlMap, a SPARQL-to-SQL rewriter based on the specifications of the W3C R2RML working group. The rationale is to enable SPARQL querying on existing relational databases by rewriting a SPARQL query to exactly one corresponding SQL query based on mapping definitions expressed in R2RML. The SparqlMap process of rewriting a query on a mapping comprises the three steps (1) mapping candidate selection, (2) query translation, and (3) query execution. We showcase our SparqlMap implementation and benchmark data that demonstrates that SparqlMap outperforms the current state-of-the-art.