A Grid Middleware for Ontology Access

Many advanced grid applications need access to ontologies represent-ing knowledge about a certain application domain. To deal with the high heterogeneity of available ontologies, we propose a general ser-vice-oriented middleware for making ontologies accessible to grid ap-plications. Our implementation is integrated in the German D-Grid in-frastructure and provides several applications a uniform access to biomedical ontologies such as Gene Ontology, NCI Thesaurus and several OBO ontologies

The Pion-Nucleon coupling constant from np charge exchange scattering

A novel extrapolation method has been used to deduce the charged Pion-Nucleon coupling constant from backward $np$ differential scattering cross sections. We applied it to new measurements performed at 162 MeV at the The Svedberg Laboratory in Uppsala. In the angular range $150^\circ-180^\circ$, the carefully normalized data are steeper than those of most previous measurements. The extracted value, $g^2_{\pi^\pm} = 14.52 \pm 0.26$, in good agreement with the classical value, is higher than those determined in recent nucleon-nucleon partial-wave analyses.Comment: 6 pages, 3 encapsulated figures, epsfig, menu97.cls (included

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources

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

Data Integration over NoSQL Stores Using Access Path Based Mappings

International audienceDue to the large amount of data generated by user interactions on the Web, some companies are currently innovating in the domain of data management by designing their own systems. Many of them are referred to as NoSQL databases, standing for 'Not only SQL'. With their wide adoption will emerge new needs and data integration will certainly be one of them. In this paper, we adapt a framework encountered for the integration of relational data to a broader context where both NoSQL and relational databases can be integrated. One important extension consists in the efficient answering of queries expressed over these data sources. The highly denormalized aspect of NoSQL databases results in varying performance costs for several possible query translations. Thus a data integration targeting NoSQL databases needs to generate an optimized translation for a given query. Our contributions are to propose (i) an access path based mapping solution that takes benefit of the design choices of each data source, (ii) integrate preferences to handle conflicts between sources and (iii) a query language that bridges the gap between the SQL query expressed by the user and the query language of the data sources. We also present a prototype implementation, where the target schema is represented as a set of relations and which enables the integration of two of the most popular NoSQL database models, namely document and a column family stores

Zeta Determinant for Laplace Operators on Riemann Caps

The goal of this paper is to compute the zeta function determinant for the massive Laplacian on Riemann caps (or spherical suspensions). These manifolds are defined as compact and boundaryless $D-$dimensional manifolds deformed by a singular Riemannian structure. The deformed spheres, considered previously in the literature, belong to this class. After presenting the geometry and discussing the spectrum of the Laplacian, we illustrate a method to compute its zeta regularized determinant. The special case of the deformed sphere is recovered as a limit of our general formulas.Comment: 19 pages, 1 figur

VirtualEMF: a Model Virtualization Tool

International audienceSpecification of complex systems involves several heterogeneous and interrelated models. Model composition is a crucial (and complex) modeling activity that allows combining different system perspectives into a single cross-domain view. Current composition solutions fail to fully address the problem, presenting important limitations concerning efficiency, interoperability, and/or synchronization. To cope with these issues, in this demo we introduce VirtualEMF: a model composition tool based on the concept of a virtual model, i.e., a model that do not hold concrete data, but that redirects all its model manipulation operations to the set of base models from which it was generated

HoloDetect: Few-Shot Learning for Error Detection

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of two parts: (1) an expressive model to learn rich representations that capture the inherent syntactic and semantic heterogeneity of errors; and (2) a data augmentation model that, given a small seed of clean records, uses dataset-specific transformations to automatically generate additional training data. Our key insight is to learn data augmentation policies from the noisy input dataset in a weakly supervised manner. We show that our framework detects errors with an average precision of ~94% and an average recall of ~93% across a diverse array of datasets that exhibit different types and amounts of errors. We compare our approach to a comprehensive collection of error detection methods, ranging from traditional rule-based methods to ensemble-based and active learning approaches. We show that data augmentation yields an average improvement of 20 F1 points while it requires access to 3x fewer labeled examples compared to other ML approaches.Comment: 18 pages