We are happy to announce the acceptance of 4 demos/posters and 2 workshop papers at the <link https: iswc2017.semanticweb.org>International Semantic Web Conference 2017 in Vienna.
Demos
“The Tale of Sansa Spark” by Ivan Ermilov, Jens Lehmann, Gezim Sejdiu, Bühmann Lorenz, Patrick Westphal, Claus Stadler, Simon Bin, Nilesh Chakraborty, Henning Petzka, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo and Hajira Jabeen.
Abstract: We demonstrate the open-source Semantic Analytics Stack (SANSA) that can perform scalable analysis of large-scale knowledge graphs to facilitate applications such as link prediction, knowledge base completion and reasoning. The motivation behind this work lies in the lack of scalability of analytics methods, which exploit expressive structures underlying semantically structured knowledge bases. The demonstration is based on the BigDataEurope technical platform, which utilizes Docker technology. We present various examples of using SANSA in a form of interactive Spark notebooks, which are executed using Apache Zeppelin. The technical platform and the notebooks are available on SANSA Github and can be easily deployed on any Docker-enabled host, locally or in a Docker Swarm cluster.“Benchmarking RDF Storage Solutions with IGUANA” by Felix Conrads, Jens Lehmann, Muhammad Saleem and Axel-Cyrille Ngonga Ngomo.
Abstract: Choosing the right RDF storage storage is of central importance when developing any data-driven Semantic Web solution. In this demonstration paper, we present the configuration and use of the IGUANA benchmarking framework. This framework addresses a crucial drawback of state-of-the-art benchmarks: While several benchmarks have been proposed that assess the performance of triple stores, an integrated benchmark-independent execution framework for these benchmarks was yet to be made available. IGUANA addresses this research by providing an integrated and highly configurable environment for the execution of SPARQL benchmarks. Our framework complements benchmarks by providing an execution environment which can measure the performance of triple stores during data loading, data updates as well as under different loads and parallel requests. Moreover, it allows a uniform comparison of results on different benchmarks. During the demonstration, we will execute the DBPSB benchmark using the IGUANA framework and show how our framework measures the performance of popular triple stores under updates and parallel user requests. IGUANA is open-source and can be found at <link http: iguana-benchmark.eu>iguana-benchmark.eu.
“OKBQA Framework towards an open collaboration for development of natural language question-answering systems over knowledge bases” by Jin-Dong Kim, Christina Unger, Axel-Cyrille Ngonga Ngomo, André Freitas, Young-gyun Hahm, Jiseong Kim, Sangha Nam, Gyu-Hyun Choi, Jeong-uk Kim, Ricardo Usbeck, Myoung-Gu Kang and Key-Sun Choi
Abstract: Due to recent advances in Semantic Web (SW), the amount of Linked Data (LD) available particularly in Resource Description Framework (RDF) increases rapidly. However, LD is still used mostly by SW experts. There are two main obstacles to making LD accessible for common Web users: (1) the need to learn the query language, SPARQL, and (2) the need to know the schemas underlying various datasets to be queried. Approaches to ease the access to LD include graphical query interfaces, agent-based systems, and natural language (NL) interfaces. Among them, NL interfaces are receiving increasing interest due to high expressive power and low learning cost. Typically, a natural language question-answering (NLQA) system takes natural language queries as input. The queries are then converted in a structured query language, e.g. SPARQL, which will be used to consult a knowledge base (KB), e.g., a SPARQL endpoint, or KBs. While there are a number of relevant previous works, it is widely understood that the development of a NLQA system requires expertise in various technologies, e.g., natural language processing (NLP), database schema analysis, inference, and so on. There is thus a natural call for collaboration among interested parties. With the goal to provide a platform of open collaboration for development of NLQA systems, the OKBQA framework has been developed. Recently, it has reached a milestone: (1) core module categories for NLQA systems are gured out and their APIs are documented, (2) a repository of OKBQA-compatible modules is implemented, and 24 modules are registered, and (3) a prototype demo system is implemented and two workows for QA in English and Korean have been set up. This manuscript presents a summary of OKBQA Framework, and the demo presentation will show how the system works to support collaboration for development of NLQA systems.
“Federated SPARQL Query Processing Via CostFed” by Alexander Potocki, Muhammad Saleem, Tommaso Soru, Olaf Hartig and Axel-Cyrille Ngonga Ngomo
Abstract: Efficient source selection and optimized query plan generation belong to the most important optimization steps in federated query processing. This paper presents a demo of CostFed, an index-assisted federation engine for federated SPARQL query processing. CostFeds's source selection and query planning is based on the index generated from the SPARQL endpoints. The key innovation behind CostFed is that it considers the skew distribution of the resources to perform efficient source selection and cost-based query planning. Our experiments on the FedBench benchmark that CostFed on average is 3 to 121 times faster than the state of the art.
Workshop papers
“A High-Performance Approach to String Similarity using Most Frequent k Characters” by André Valdestilhas, Tommaso Soru and Axel-Cyrille Ngonga Ngomo. Accepted as full paper at the <link http: om2017.ontologymatching.org>Twelfth Ontology Matching Workshop (OM-2017).
Abstract: The amount of available data has been growing significantly over the last decades. Thus, linking entries across heterogeneous data sources such as databases or knowledge bases, becomes an increasingly difficult problem, in particular w.r.t. the runtime of these tasks. Consequently, it is of utmost importance to provide time-efficient approaches for similarity joins in the Web of Data. While a number of scalable approaches have been developed for various measures, the Most Frequent K Characters (MFKC) measure has not been tackled in previous works. We hence present a sequence of filters that allow discarding comparisons when executing bounded similarity computations without losing recall. Therewith, we can reduce the runtime of bounded similarity computations by approximately 70%. Our experiments with a single-threaded, a parallel and a GPU implementation of our filters suggest that our approach scales well even when dealing with millions of potential comparisons.
“HOBBIT Link Discovery Benchmarks at Ontology Matching 2017” by Michael Röder, Tzanina Saveta, Irini Fundulaki and Axel-Cyrille Ngonga Ngomo. Accepted as poster at the <link http: om2017.ontologymatching.org>Twelfth Ontology Matching Workshop (OM-2017).
Abstract: We address the problem of benchmarking ontology matching and link discovery frameworks at large scale. In particular, we aim to ensure that the benchmarks generate comparable results for the various systems and approaches. Our solution lies in implementing our benchmarks into the Hobbit benchmarking platform, which provide means for the unified benchmarking of Big Linked Data solutions.
Acknowledgments
These work were supported by the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227) and the European Union’s H2020 research and innovation program BigDataEurope (GA no.644564). Some of the authors were supported by the EuroStars projects DIESEL (01QE1512C) and QAMEL (01QE1549C).
