-
LuxLogAI 2018
Luxembourg Logic for AI Summit
17-26 September 2018
Luxembourg
The research areas of Semantic Web, Linked Data and Knowledge Graphs have received a lot of attention in academia and industry recently. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context-awareness and decision support. Over the years, the Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked data is a related research area which studies how one can make RDF data available on the Web, and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing etc) but also in many application domains.
The research areas of Semantic Web, Linked Data and Knowledge Graphs have received a lot of attention in academia and industry recently. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to
provide Web-based systems with intelligent capabilities such as context-awareness and decision support. Over the years, the Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked data is a related research area which studies how one can make RDF data available on the Web, and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing etc) but also in many application domains.
The 14th Reasoning Web Summer School will take place in Luxembourg on 22-26 September 2018, Belval campus, as part of the Luxembourg Logic for AI Summit (LuXLogAI).
Chairs
- Claudia d’Amato (University of Bari)
- Martin Theobald (University of Luxembourg)
Call for Applications
We welcome applications from Master and PhD students as well postdoctoral researchers, young researchers, and senior researchers wishing to learn about the research areas of Semantic Web and related sub-areas such as Linked Data and Knowledge Graphs, Ontologies, Rules, and Logic.
Participants to the summer school will have the opportunity to attend lectures, to be involved in working groups concerning small case studies or research tasks to focus on and to be presented in a final plenary session, discuss ideas and closely interact with leading researchers in the Semantic Web community and beyond.
Program
This year school program will include the following topics and lecturers (additional names will come shortly).
| Hannah Bast (University of Freiburg) |
Efficient SPARQL queries on very large Knowledge Graphs |
| Philipp Cimiano (University of Bielefeld) |
Information Extraction for Knowledge Graph Construction |
| Emanuele Della Valle (Politecnico di Milano) |
Stream Reasoning |
| Guido Governatori (CSIRO/Data61) |
Normative reasoning for the Semantic Web |
| Heiko Pauleim (University of Mannheim) |
Machine Learning with and for Knowledge Graph |
| Daria Stepanova (Max Planck Institute) |
Rule Induction and Reasoning over Knowledge Graphs |
| Steffen Staab and Daniel Janke (University of Koblenz Landau) |
Scalable Semantic Data Management |
| Thomas Lukasiewicz (University of Oxford) |
Deep Learning for the Semantic Web |
| Hendrik ter Horst, Matthias Hartung and Philipp Cimiano (Bielefeld University) |
Cold-start knowledge base population using ontology-based information extraction based on factor graph**models*** |
Although no specific background knowledge is required for attending the summer school, basics of knowledge representation and the Semantic Web (including technologies such as RDF, OWL, etc.) will be helpful for benefiting from the contents of school. Students are also committed to a full participation for the whole duration of the school.
Abstracts
Heiko Pauleim: Machine Learning with and for Knowledge Graphs
Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In the first part of this lecture, you will learn which knowledge graphs exist on the Semantic Web. I will introduce a few typical machine learning tasks, such as product recommendation and social media analysis, and discuss how knowledge graphs can be used to improve the performance within those tasks. In the second part of the lecture, we will discuss how machine learning can help improving existing knowledge graphs, tackling typical problems such as link and type prediction or error identification.
Daria Stepanova: Rule Induction and Reasoning over Knowledge Graphs
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO
and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule
induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.
Steffen Staab and Daniel Janke: Scalable Semantic Data Management
With proliferation of semantic data, there is a need to cope with trillions of triples by horizontally scaling data management in the cloud. To this end one needs to advance (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this tutorial, we want to review challenges and how they have been addressed by research and development in the last 15 years.
Hendrik ter Horst, Matthias Hartung and Philipp Cimiano:
Cold-start knowledge base population using ontology-based information extraction based on factor graph**models***
Cold-start knowledge base population is concerned with populating an empty knowledge base from scratch. In this paper, we describe a method that supports cold-start knowledge base population by information extraction from unstructuredtext. The method is based on a given ontology that defines the main classes and properties for the domain in question. The information extraction approach capitalizes onprobabilistic graphical models, in particular factor graphs. In this paper, we describe (i) the probabilistic model underlyingour approach, (ii) the inference techniques that are based on Markov Chain Monte Carlo sampling over possible schema instantiations defined by the ontology and conditioned on a given text, as well as (iii) the approach to learn the parameters of the model. Many other methods such as open information extraction rely on extracting single binary relations using classification methods. In contrast, our method takes rigorous account of an ontology and extracts more complex ontology-compliant structures that capture the meaning of a text in a more comprehensiveway. We present results on two datasets: a novel dataset of scientific publications covering pre-clinical studies in the spinal cord injury domain, and the well-known, but now quite aged, Message Understanding (MUC) dataset.
Registration
The registration fees for attending the RW Summer School will be 300 EUR (if your RW registration is bundled with the one for RuleML-RR) and 350 EUR (if you choose to only register for RW), respectively. This includes all lectures and teaching sessions, lunches, coffee breaks, school proceedings and a social event.
Student grants
A limited number of grants will be available for selected students who would otherwise not be able to attend the summer school. As such, please ask for financial support in your application (see below) only if this is the necessary condition for you to participate in the summer school.
Application
Students and researchers interested in participating at the school needs to submit their application by June 30, 2018 via easychair using the following link.
The application should consist of a single pdf file including:
- A single-page CV.
- A short motivation letter of up
to 300 words stating research interests and reasons for the willingness to attend the summer school. - Necessity for a visa to enter Luxembourg (Yes or No). If required, invitation letters will be set up.
- Necessity for financial support. A limited number of travel grants will be provided, please thus ask for financial support only if this is a necessary condition for you to join the school. Please add a rough quote for your estimated expenses (travel + hotel costs) to your application in this case.
Notifications about the selection results will be sent by July 15, 2018.
For any questions, please contact: rw2018@easychair.org