EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.Comment: International Semantic Web Conference 201

Simultaneous observations of NLCs and MSEs at midlatitudes: implications for formation and advection of ice particles

We combined ground-based lidar observations of noctilucent clouds (NLCs) with collocated, simultaneous radar observations of mesospheric summer echoes (MSEs) in order to compare ice cloud altitudes at a midlatitude site (Kühlungsborn, Germany, 54°&thinsp;N, 12°&thinsp;E). Lidar observations are limited to larger particles ( &gt; 10&thinsp;nm), while radars are also sensitive to small particles ( &lt; 10&thinsp;nm), but require sufficient ionization and turbulence at the ice cloud altitudes. The combined lidar and radar data set thus includes some information on the size distribution within the cloud and through this on the history of the cloud. The soundings for this study are carried out by the IAP Rayleigh–Mie–Raman (RMR) lidar and the OSWIN VHF radar. On average, there is no difference between the lower edges (zlowNLC and zlowMSE). The mean difference of the upper edges zupNLC and zupMSE is  ∼ 500&thinsp;m, which is much less than expected from observations at higher latitudes. In contrast to high latitudes, the MSEs above our location typically do not reach much higher than the NLCs. In addition to earlier studies from our site, this gives additional evidence for the supposition that clouds containing large enough particles to be observed by lidar are not formed locally but are advected from higher latitudes. During the advection process, the smaller particles in the upper part of the cloud either grow and sediment, or they sublimate. Both processes result in a thinning of the layer. High-altitude MSEs, usually indicating nucleation of ice particles, are rarely observed in conjunction with lidar observations of NLCs at Kühlungsborn.</p

Spatiotemporal modeling of microbial metabolism

Background Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention. Results We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution. Conclusions Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems

AskNow: A framework for natural language query formalization in SPARQL

Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering (QA) systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base (e.g. DBpedia), which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure (NQS), and then translated into SPARQL queries. NQS facilitates the identification of the desire (or expected output information) and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing