Kai Schlegel

Balloon Fusion: SPARQL rewriting based on unified co-reference information

While Linked Open Data showed enormous increase in volume, yet there is no single point of access for querying the over 200 SPARQL repositories. In this paper we present Balloon Fusion, a SPARQL 1.1 rewriting and query federation service build on crawling and consolidating co-reference relationships in over 100 reachable Linked Data SPARQL Endpoints. The results of this process are 17.6M co-reference statements that have been clustered to 8.4M distinct semantic entities and are now accessible as download for further analysis. The proposed SPARQL rewriting performs a substitution of all URI occurrences with their synonyms combined with an automatic endpoint selection based on URI origin for a comprehensive query federation. While we show the technical feasibility, we also critically reflect the current status of the Linked Open Data cloud: although it is huge in size, access via SPARQL Endpoints is complicated in most cases due to missing quality of service.

Unleashing Semantics of Research Data

Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.

Enabling access to Linked Media with SPARQL-MM

The amount of audio, video and image data on the web is immensely growing, which leads to data management problems based on the hidden character of multimedia. Therefore the interlinking of semantic concepts and media data with the aim to bridge the gap between the document web and the Web of Data has become a common practice and is known as Linked Media. However, the value of connecting media to its semantic meta data is limited due to lacking access methods specialized for media assets and fragments as well as to the variety of used description models. With SPARQL-MM we extend SPARQL, the standard query language for the Semantic Web with media specific concepts and functions to unify the access to Linked Media. In this paper we describe the motivation for SPARQL-MM, present the State of the Art of Linked Media description formats and Multimedia query languages, and outline the specification and implementation of the SPARQL-MM function set.

Crowdsourcing Fact Extraction from Scientific Literature

Scientific publications constitute an extremely valuable body of knowledge and can be seen as the roots of our civilisation. However, with the exponential growth of written publications, comparing facts and findings between different research groups and communities becomes nearly impossible. In this paper, we present a conceptual approach and a first implementation for creating an open knowledge base of scientific knowledge mined from research publications. This requires to extract facts - mostly empirical observations - from unstructured texts (mainly PDF’s). Due to the importance of extracting facts with high-accuracy and the impreciseness of automatic methods, human quality control is of utmost importance. In order to establish such quality control mechanisms, we rely on intelligent visual interfaces and on establishing a toolset for crowdsourcing fact extraction, text mining and data integration tasks.

Balloon Synopsis: A Modern Node-Centric RDF Viewer and Browser for the Web

Nowadays, the RDF data model is a crucial part of the Semantic Web. Especially web developers favour RDF serialization formats like RDFa and JSON-LD. However, the visualization of large portions of RDF data in an appealing way is still a cumbersome task. RDF visualizers in general are not targeting the Web as usage scenario or simply display the complex RDF graph directly rather than applying a human friendly facade. Balloon Synopsis tries to overcome these issues by providing an easy-to-use RDF visualizer based on HTML and JavaScript. For an ease integration, it is implemented as jQuery-plugin offering a node-centric RDF viewer and browser with automatic Linked Data enhancement in a modern tile design.

SPARQL-MM - Extending SPARQL to Media Fragments

Interconnecting machine readable data with multimedia assets and fragments has recently become a common practice. But specific retrieval techniques for the so called Semantic Multimedia data are still lacking. On our poster we present SPARQL-MM, a function set that extends SPARQL to Media Fragment facilities by introducing spatio-temporal filter and aggregation functions.

MICO - Media in Context

The abundance of digital content requires cost-effective technologies to extract the hidden meaning from media objects. However, current approaches fail to deal with the challenges related to cross-media analysis, metadata publishing, querying and recommendation that are necessary to overcome this challenge. In this paper, we describe the EU project MICO (Media in Context) which aims to provide the necessary technologies based on open-source software (OSS) core components.

Anno4j - Idiomatic Access to the W3C Web Annotation Data Model

The Web Annotation Data Model proposes standardised RDF structures to form “Web Annotations”. These annotations are used to express metadata information about digital resources and are designed to be shared, linked, tracked back, as well as searched and discovered across different peers. Although this is an expressive and rich way to create metadata, there exists a barrier for non-RDF and SPARQL experts to create and query such information. We propose Anno4j, a Java-based library, as a solution to this problem. The library supports an Object-RDF mapping that enables users to generate Web Annotations by creating plain old Java objects - concepts they are familiar with - while a path-based querying mechanism allows comprehensive information querying. Anno4j follows natural object-oriented idioms including inheritance, polymorphism, and composition to facilitate the development. While supporting the functionality of the Web Annotation Data Model, the library is implemented in a modular way, enabling developers to add enhancements and use case specific model alterations. Features like plugin functionality, transactions, and input/output methods further decrease the boundary for non-RDF experts.

A platform for contextual multimedia data: towards a unified metadata model and querying

Whereas the former Web mostly consisted of information represented in textual documents, nowadays the Web includes a huge number of multimedia documents like videos, photos, and audio. This enormous increase in volume in the private, and above all in the industry sector, makes it more and more difficult to find relevant information. Besides the pure management of multimedia documents, finding hidden semantics and interconnections of heterogeneous cross-media content is a crucial task and stays mostly untouched. To overcome this tendency we see the need for a generic cross-media analysis platform, ranging from extracting relevant features from media objects over representing and publishing extraction results to integrated querying of aggregated findings. In this paper we propose the underlying foundation for a common and contextual multimedia platform in terms of an unified model for publishing multimedia analysis results. The proposed model is based on existing ontologies, adapted and extended to the cross-media environment. Besides the introduction of the already mentioned platform and model, this paper also briefly introduces specific use-case applications as well as possibilities to query the persisted data.

Trusted Facts: Triplifying Primary Research Data Enriched with Provenance Information

A crucial task in a researchers’ daily work is the analysis of primary research data to estimate the evolution of certain fields or technologies, e.g. tables in publications or tabular benchmark results. Due to a lack of comparability and reliability of published primary research data, this becomes more and more time-consuming leading to contradicting facts, as has been shown for ad-hoc retrieval [1]. The CODE project [2] aims at contributing to a Linked Science Data Cloud by integrating unstructured research information with semantically represented research data. Through crowdsourcing techniques, data centric tasks like data extraction, integration and analysis in combination with sustainable data marketplace concepts will establish a sustainable, high-impact ecosystem.