We are very pleased to announce that our group got 6 demo/poster papers accepted for presentation at ISWC 2017, which will be held on 21-24 October in Vienna, Austria.
The International Semantic Web Conference (ISWC) is the premier international forum where Semantic Web / Linked Data researchers, practitioners, and industry specialists come together to discuss, advance, and shape the future of semantic technologies on the web, within enterprises and in the context of the public institution.
Here is the list of the accepted demo/poster papers with their abstract:
“How to Revert Question Answering on Knowledge Graphs” by Gaurav Maheshwari, Mohnish Dubey, Priyansh Trivedi and Jens Lehmann.
Abstract: A large scale question answering dataset has a potential to enable development of robust and more accurate question answering systems. In this direction, we introduce a framework for creating such datasets which decreases the manual intervention and domain expertise traditionally needed. We describe in details the architecture and the design decision we took while creating the framework.
“The Tale of Sansa Spark” by Ivan Ermilov, Jens Lehmann, Gezim Sejdiu, Buehmann 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), which 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.
“A Vocabulary Independent Generation Framework for DBpedia and beyond” by Ben De Meester, Anastasia Dimou, Dimitris Kontokostas, Ruben Verborgh, Jens Lehmann, Erik Mannens, Sebastian Hellmann.
Abstract: The DBpedia Extraction Framework, the generation framework behind one of the Linked Open Data cloud’s central hubs, has limitations which lead to quality issues with the DBpedia dataset. Therefore, we provide a new take on its Extraction Framework that allows for a sustainable and general-purpose Linked Data generation framework by adapting a semantic-driven approach. The proposed approach decouples, in a declarative manner, the extraction, transformation, and mapping rules execution. This way, among others, interchanging different schema annotations is supported, instead of being coupled to a certain ontology as it is now, because the DBpedia Extraction Framework allows only generating a certain dataset with a single semantic representation. In this paper, we shed more light to the added value that this aspect brings. We provide an extracted DBpedia dataset using a different vocabulary, and give users the opportunity to generate a new dbpedia dataset using a custom combination of vocabularies.
“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 http://iguana-benchmark.eu/.
“BatWAn – A Binary and Multi-way Query Plan Analyzer” by Mikhail Galkin, Maria-Esther Vidal.
Abstract: The majority of existing SPARQL query engines generate query plans composed of binary join operators. Albeit effective, binary joins can drastically impact on the performance of query processing whenever source answers need to be passed through multiple operators in a query plan. Multi-way joins have been proposed to overcome this problem; they are able to propagate and generate results in a single step during query execution. We demonstrate the benefits of query plans with multi-way operators with BatWAn, a binary and multi-way query plan analyzer. Attendees will observe the behavior of multi-way joins on queries of different selectivity, as well as the impact on total execution time, time for the first answer, and continuous results yield over time.
“QAESTRO – Semantic Composition of QA Pipelines” by Kuldeep Singh, Ioanna Lytra, Kunwar Abhinav Aditya, Maria-Esther Vidal.
Abstract: Many question answering systems and related components have been developed in recent years. Since question answering involves several tasks and subtasks, common in many systems, existing components can be combined in various ways to build tailored question answering pipelines. QAESTRO provides the tools to semantically describe question answering components and automatically generate possible pipelines given developer requirements. We demonstrate the functionality of QAESTRO for building question answering pipelines including different tasks and components. Attendees will be able to semantically describe question answering pipelines and integrate them in existing frameworks.
Acknowledgments
These work were supported by the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227), the European Union’s H2020 research and innovation program BigDataEurope (GA no.644564), DAAD Scholarship and WDAqua : Marie Skłodowska-Curie Innovative Training Network.
Looking forward to seeing you at ISWC 2017.