Markus Nentwig

A survey of current Link Discovery frameworks

Links build the backbone of the Linked Data Cloud. With the steady growth in size of datasets comes an increased need for end users to know which frameworks to use for deriving links between datasets. In this survey, we comparatively evaluate current Link Discovery tools and frameworks. For this purpose, we outline general requirements and derive a generic architecture of Link Discovery frameworks. Based on this generic architecture, we study and compare the features of state-ofthe-art linking frameworks. We also analyze reported performance evaluations for the different frameworks. Finally, we derive insights pertaining to possible future developments in the domain of Link Discovery.

LinkLion: A Link Repository for the Web of Data

Links between knowledge bases build the backbone of the Web of Data. Consequently, numerous applications have been developed to compute, evaluate and infer links. Still, the results of many of these applications remain inaccessible to the tools and frameworks that rely upon it. We address this problem by presenting LinkLion, a repository for links between knowledge bases. Our repository is designed as an open-access and open-source portal for the management and distribution of link discovery results. Users are empowered to upload links and specify how these were created. Moreover, users and applications can select and download sets of links via dumps or SPARQL queries. Currently, our portal contains 12.6 million links of 10 different types distributed across 3184 mappings that link 449 datasets. In this demo, we will present the repository as well as different means to access and extend the data it contains. The repository can be found at http://www.linklion.org.

Heterostructures of skutterudites and germanium antimony tellurides – structure analysis and thermoelectric properties of bulk samples

Heterostructures of germanium antimony tellurides with skutterudite-type precipitates are promising thermoelectric materials due to low thermal conductivity and multiple ways of tuning their electronic transport properties. Materials with the nominal composition [CoSb2(GeTe)0.5]x(GeTe)10.5Sb2Te3 (x = 0–2) contain nano- to microscale precipitates of skutterudite-type phases which are homogeneously distributed. Powder X-ray diffraction reveals that phase transitions of the germanium antimony telluride matrix depend on its GeTe content. These are typical for this class of materials; however, the phase transition temperatures are influenced by heterostructuring in a beneficial way, yielding a larger existence range of the intrinsically nanostructured pseudocubic structure of the matrix. Using microfocused synchrotron radiation in combination with crystallite pre-selection by means of electron microscopy, single crystals of the matrix as well as of the precipitates were examined. They show nano-domain twinning of the telluride matrix and a pronounced structure distortion in the precipitates caused by GeTe substitution. Thermoelectric figures of merit of 1.4 ± 0.3 at 450 °C are observed. In certain temperature ranges, heterostructuring involves an improvement of up to 30% compared to the homogeneous material.

Holistic Entity Clustering for Linked Data

Pairwise link discovery approaches for the Web of Data do not scale to many sources thereby limiting the potential for data integration. We thus propose a holistic approach for linking many data sources based on a clustering of entities representing the same real-world object. Our clustering approach utilizes existing links and can deal with entities of different semantic types. The approach is able to identify errors in existing links and can find numerous additional links. An initial evaluation on real-world linked data shows the effectiveness of the proposed holistic entity matching.

Puzzling Intergrowth in Cerium Nitridophosphate Unraveled by Joint Venture of Aberration-Corrected Scanning Transmission Electron Microscopy and Synchrotron Diffraction

Thorough investigation of nitridophosphates has rapidly accelerated through development of new synthesis strategies. Here we used the recently developed high-pressure metathesis to prepare the first rare-earth metal nitridophosphate, Ce4Li3P18N35, with a high degree of condensation >1/2. Ce4Li3P18N35 consists of an unprecedented hexagonal framework of PN4 tetrahedra and exhibits blue luminescence peaking at 455 nm. Transmission electron microscopy (TEM) revealed two intergrown domains with slight structural and compositional variations. One domain type shows extremely weak superstructure phenomena revealed by atomic-resolution scanning TEM (STEM) and single-crystal diffraction using synchrotron radiation. The corresponding superstructure involves a modulated displacement of Ce atoms in channels of tetrahedra 6-rings. The displacement model was refined in a supercell as well as in an equivalent commensurate (3 + 2)-dimensional description in superspace group P63(α, β, 0)0(-α - β, α, 0)0. In the second domain type, STEM revealed disordered vacancies of the same Ce atoms that were modulated in the first domain type, leading to sum formula Ce4-0.5xLi3P18N35-1.5xO1.5x (x ≈ 0.72) of the average structure. The examination of these structural intricacies may indicate the detection limit of synchrotron diffraction and TEM. We discuss the occurrence of either Ce displacements or Ce vacancies that induce the incorporation of O as necessary stabilization of the crystal structure.

Where is My URI

One of the Semantic Web foundations is the possibility to dereference URIs to let applications negotiate their semantic content. However, this exploitation is often infeasible as the availability of such information depends on the reliability of networks, services, and human factors. Moreover, it has been shown that around 90% of the information published as Linked Open Data is available as data dumps and more than 60% of endpoints are offline. To this end, we propose a Web service called Where is my URI?. Our service aims at indexing URIs and their use in order to let Linked Data consumers find the respective RDF data source, in case such information cannot be retrieved from the URI alone. We rank the corresponding datasets by following the rationale upon which a dataset contributes to the definition of a URI proportionally to the number of literals. We finally describe potential use-cases of applications that can immediately benefit from our simple yet useful service.

RE4Ba2[Si12O2N16C3]:Eu2+ (RE = Lu, Y): Green-Yellow Emitting Oxonitridocarbidosilicates with a Highly Condensed Network Structure Unraveled through Synchrotron Microdiffraction

The oxonitridocarbidosilicates RE4Ba2[Si12O2N16C3]:Eu2+ ( RE = Lu, Y) were synthesized by carbothermal reactions starting from RE2O3, graphite, Ba2Si5N8, Si(NH)2, and Eu2O3. The crystal structure of Lu4Ba2[Si12O2N16C3]:Eu2+ was elucidated on a submicron-sized single crystal by a combination of transmission electron microscopy and microfocused synchrotron radiation. The compound crystallizes in trigonal space group P3 (no. 143) with a = 16.297(4) Å, c = 6.001(2) Å, and Z = 3 ( R1 = 0.0332, wR2 = 0.0834, GoF = 1.034). According to Rietveld refinements on powder X-ray diffraction data, Y4Ba2[Si12O2N16C3]:Eu2+ is isotypic with a = 16.41190(6) Å and c = 6.03909(3) Å. The crystal structures are built up of vertex-sharing SiC(O/N)3 tetrahedra forming star-shaped units [C[4](Si(O/N)3)4] with carbon atoms in fourfold bridging positions. Energy-dispersive X-ray spectroscopy and CHNS analysis correspond to the sum formula, lattice energy, and charge distribution calculations support the assignment of O/N/C atoms. When excited with UV to blue light, Eu2+-doped samples show green luminescence for RE = Lu (λem ≈ 538 nm, full width at half-maximum (fwhm) ≈ 3600 cm-1) and yellow emission in the case of RE = Y (λem ≈ 556 nm, fwhm ≈ 4085 cm-1).

Distributed Holistic Clustering on Linked Data

Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains.