We are very pleased to announce that our group got 2 papers accepted for presentation at the The 2018 edition of The Web Conference (27th edition of the former WWW conference), which will be held on April 23-27, 2018 in Lyon, France.
The 2018 edition of The Web Conference will offer many opportunities to present and discuss latest advances in academia and industry. This first joint call for contributions provides a list of the first calls for: research tracks, workshops, tutorials, exhibition, posters, demos, developers’ track, W3C track, industry track, PhD symposium, challenges, minute of madness, international project track, W4A, hackathon, the BIG web, journal track.
Here are the accepted papers with their abstracts:
- “DL-Learner – A Framework for Inductive Learning on the Semantic Web” by Lorenz Bühmann, Patrick Westphal, Jens Lehmann and Simon Bin (Journal paper track).
Abstract: In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases.
- “Why Reinvent the Wheel- Let’s Build Question Answering Systems Together” by Kuldeep Singh, Arun Sethupat Radhakrishna, Andreas Both, Saeedeh Shekarpour, Ioanna Lytra, Ricardo Usbeck, Akhilesh Vyas, Akmal Khikmatullaev, Dharmen Punjani, Christoph Lange, Maria-Esther Vidal, Jens Lehmann and Sören Auer ( Research track).
Abstract: Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist, implementing different strategies for each of these tasks, a major challenge when building QA systems, is how to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train Classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem, but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
These work were supported by grants from the EU FP7 Programme for the project GeoKnow (GA no. 318159) as well as for the German Research Foundation project GOLD and the German Ministry for Economic Affairs and Energy project SAKE (GA no. 01MD15006E), the European Union’s Horizon 2020 research and innovation programme for the project SLIPO (GA no. 731581), the EU Horizon 2020 R&I programme for the Marie Sklodowska Curie action WDAqua (GA No 642795), Eurostars project QAMEL (E!9725) as well as the European Union’s H2020 research and innovation action HOBBIT (GA 688227) and the CSA BigDataEurope (GA No 644564).
Furthermore, we are pleased to inform that we got the following workshops, which will be co-located with The Web Conference 2018.
Here is the accepted workshops and their short description:
- Linked Data on the Web (LDOW2018) by Tim Berners-Lee (W3C/MIT, USA), Sarven Capadisli (University of Bonn, Germany), Stefan Dietze (Leibniz Universität Hannover,Germany), Aidan Hogan (Universidad de Chile, Chile), Krzysztof Janowicz (University of California, Santa Barbara, US) and Jens Lehmann (University of Bonn, Germany)
The Web is developing from a medium for publishing textual documents into a medium for sharing structured data. This trend is fueled on the one hand by the adoption of the Linked Data principles by a growing number of data providers. On the other hand, large numbers of websites have started to semantically mark up the content of their HTML pages and thus also contribute to the wealth of structured data available on the Web. The 11th Workshop on Linked Data on the Web (LDOW2018) aims to stimulate discussion and further research into the challenges of publishing, consuming, and integrating structured data from the Web as well as mining knowledge from the global Web of Data.
Topics of interest for the workshop include, but are not limited to, the following:
- Web Data Quality Assessment
- Web Data Cleansing
- Integrating Web Data from Large Numbers of Data Sources
- Mining the Web of Data
- Linked Data Applications
Social media hashtag: #LDOW2018
- Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination by Alejandra Gonzalez-Beltran (Oxford e-Research Centre, University of Oxford, Oxford, UK), Francesco Osborne (Knowledge Media Institute, Open University, Milton Keynes, UK), Silvio Peroni (Department of Computer Science and Engineering, University of Bologna, Bologna, Italy), Sahar Vahdati (Smart Data Analytics, University of Bonn, Bonn, Germany)
After the great success of the past three editions, we are pleased to announce SAVE-SD 2018, which wants to bring together publishers, companies and researchers from different fields (including Document and Knowledge Engineering, Semantic Web, Natural Language Processing, Scholarly Communication, Bibliometrics, Human-Computer Interaction, Information Visualisation, Bioinformatics, and Life Sciences) in order to bridge the gap between the theoretical/academic and practical/industrial aspects in regards to scholarly data.
The following topics will be addressed:
- semantics of scholarly data, i.e. how to semantically represent, categorise, connect and integrate scholarly data, in order to foster reusability and knowledge sharing;
- analytics on scholarly data, i.e. designing and implementing novel and scalable algorithms for knowledge extraction with the aim of understanding research dynamics, forecasting research trends, fostering connections between groups of researchers, informing research policies, analysing and interlinking experiments and deriving new knowledge;
- visualisation of and interaction with scholarly data, i.e. providing novel user interfaces and applications for navigating and making sense of scholarly data and highlighting their patterns and peculiarities.
Looking forward to seeing you at The Web Conference (ex WWW) 2018