Call for 13th Reasoning Web Summer School (RW 2017)
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.
Many advanced capabilities required by Semantic Web, Linked Data and Knowledge Graph application scenarios call for reasoning. Thus, a perspective centered on the reasoning techniques complementing other research efforts in this area is desirable. The Reasoning Web series of annual Summer Schools is devoted to this perspective, and gives insight into the Semantic Web, Linked Data, Knowledge Graph, Ontologies, Rules, and Logic. It was started in 2005 on behalf of the work package “Education and Training (ET)” of the Network of Excellence REWERSE, and since then it has been established as a major annual event in the above mentioned research areas. It is primarily intended for postgraduate (Ph.D. or M.Sc.) students, postdoctoral researchers, young researchers, and senior researchers wishing to learn about Reasoning on the Semantic Web and related issues.The 13th Reasoning Web Summer School will take place in London, UK, hosted by Birkbeck, University of London, from July 7th to July 11th, 2017.
Lecture materials will be published in Lecture Notes in Computer Science (LNCS).
Participants to the school will be delivered on request a certificate of attendance indicating the number of hours of lectures. With this certificate, some institutions may assign official credits for the PhD program.
|Application||May 20, 2017 (extended)|
|Notification||May 25, 2017|
|Registration||May 31, 2017|
The school is primarily intended for postgraduate (PhD or MSc) students, postdoctoral researchers, and young researchers. The Summer School will also be open to a limited number of senior researchers from other areas wishing to learn about Semantic Web, Linked Data and related issues.
The number of attendees will be limited and participation will depend on submitting an application which will undergo a reviewing process. Applications have to be submitted via Easychair using the following URL: https://easychair.org/conferences/?conf=rw2017.
Applications must be submitted in PDF format not exceeding 2 pages (min. font size 11pt) and contain the following information:
- Name, contact details
- Motivation for participation
- Summary of profile
- Supervisor (if applicable)
- Publications (if applicable)
RW 2017 Summer School Fees
Registration to RW: £370
Joint Registration to RW and RuleML+RR: £485
Please note – no payment may be made before notification of acceptance has been received from the RW Summer School Committee. The fee covers coffee breaks, the school social event, and a copy of the school volume published by Springer.
There are a limited number of student grants available for the summer school. Each grant will cover the school registration fee. Further grants are available for students participating in the RuleML+RR Doctoral Consortium who would like to extend their stay to also attend the summer school.
Students who are interested in applying for these grants, besides submitting their application to the school, must write an email to the school chairs (email@example.com and firstname.lastname@example.org) declaring their interest in the student grant, and providing a proof of full-time student status (copy of valid student ID card or letter from their institution or program director) and a short justification confirming that the attendance to RW 2017 could not be financed by other means such as project funds.
The deadline for applying to the student grants is May 20, 2017 (the same as the school application deadline).
University of London offers accommodations in its student residences. Prices start from 47 GPBs per night. For details, visit http://staycentral.london.ac.uk/accommodation/.
All listed locations are within a walking distance of 15-20 minutes from the school venue.
|Challenges for Semantic Data Integration on the Web of Open Data|
In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different Data formats prevalent on the Web, namely, tabular, tree-shaped, and graph data. Secondly, not all open data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open data, and the documentation of data provenance. Thirdly, we will discuss issues associated with data quality in Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will turn to issues around searchability and integration of Open Data (how to enable basic search, table search, finding related tables, up to building knowledge graphs) and discuss how/whether semantic search can help to overcome these. Lastly, we summarize further issues not covered in depth, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help.
Axel Polleres, Vienna University of Economics and Business (WU Wien)
Alex Polleres joined the Institute of Information Business of Vienna University of Economics and Business (WU Wien) in Sept 2013 as a full professor in the area of “Data and Knowledge Engineering”. He obtained his Ph.D. and habilitation from Vienna University of Technology and worked at University of Innsbruck, Austria, Universidad Rey Juan Carlos, Madrid, Spain, the Digital Enterprise Research Institute (DERI) at the National University of Ireland, Galway, and for Siemens AG’s Corporate Technology Research division before joining WU Wien. His research focuses on querying and reasoning about ontologies, rules languages, logic programming, Semantic Web technologies, Web services, knowledge management, Linked Open Data, configuration technologies and their applications. He has worked in several European and national research projects in these areas. Axel has published more than 100 articles in journals, books, and conference and workshop contributions and co-organised several international conferences and workshops in the areas of logic programming, Semantic Web, data management, Web services and related topics and acts/acted as editorial board member for JWS, SWJ and IJSWIS. Moreover, he actively contributed to international standardisation efforts within the World Wide Web Consortium (W3C) where he co-chaired the W3C SPARQL working group.
|Ontological query answering over semantic data|
Modern information retrieval systems advance user experience on the basis of concept-based rather than keyword-based query answering. In particular, efficient user interfaces involve terminological descriptions of the domain of interest, expressed in formal knowledge representation formalisms. Ontological representation and reasoning based on description logics play an important role, providing expressive concept-level query languages with formal semantics and reasoning support. On the other hand, most real-life applications use huge amounts of data, consequently, efficient data storage and retrieval focuses on methodologies that take advantage of the physical storage using simple rather than sophisticated data models. Within this context, ontology-based query answering is one of the widely used approaches, especially for web applications, involving data from different sources, in different formats. Here, we present methods for data integration, query rewriting and query answering based on both tractable and expressive description logics. Specifically, we focus on semantic data representation based on relational schemas to ontology mappings, ontology-based query rewriting for tractable description logics and approximate query answering techniques for expressive description logics.
Giorgos Stamou, School of Electrical and Computer Engineering, National Technical University of Athens
Dr Giorgos Stamou is currently an Associate Professor in the School of Electrical and Computer Engineering, National Technical University of Athens. His research interests include ontological knowledge representation and reasoning, uncertainty handling and machine learning, and applications to semantic annotation, data access and data analytics. He has published more than 150 papers in scientific Journals and Conference Proceedings, co-edited a book and two conference proceedings, served as member of the organisation committees of several conferences (co-chair of DL Workshop 2015, local co-organiser of RR and RW 2014), participated in W3C and RuleML standardisation activities and has worked in more than 50 research projects.
|Integrating Relational Databases with the Semantic Web|
Juan Sequeda, Capsenta
Juan F. Sequeda is the co-founder of Capsenta, a spin-off from his research, and the Senior Director of Capsenta Labs. He holds a PhD in Computer Science from the University of Texas at Austin. His research interests are on the intersection of Logic and Data and in particular between the Semantic Web and Relational Databases for data integration, ontology based data access and semantic/graph data management. Juan is the recipient of the NSF Graduate Research Fellowship, received 2nd Place in the 2013 Semantic Web Challenge for his work on ConstituteProject.org, Best Student Research Paper at the 2014 International Semantic Web Conference and the 2015 Best Transfer and Innovation Project awarded by Institute for Applied Informatics. Juan is the PC chair of the ISWC 2017 In-Use track, is on the Editorial Board of the Journal of Web Semantics, member of multiple program committees (ISWC, ESWC, WWW, AAAI, IJCAI), organizer of the AMW Summer School and co-creator of the Consuming Linked Data Workshop series. Juan is a member of the Graph Query Languages task force of the Linked Data Benchmark Council (LDBC) and has also been an invited expert member and standards editor at the World Wide Web Consortium (W3C).
|Ontology-based data access for log extraction in process mining|
Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization.
Through process mining, decision makers can discover process models from data, compare expected and actual behaviors, and enrich models with key information about their actual execution. To be applicable, process mining techniques require the input data to be explicitly structured in the form of an event log, which lists when and by whom different case objects (i.e., process instances) have been subject to the execution of tasks. Unfortunately, in many real world set-ups, such event logs are not explicitly given, but are instead implicitly represented in legacy information systems. To apply process mining in this widespread setting, there is a pressing need for techniques able to support various process stakeholders in data preparation and log extraction from legacy information systems. The purpose of this lecture is to single out this challenging, open issue, and show how techniques from intelligent data management, and in particular ontology-based data access, provide a viable solution with a solid theoretical basis.
Marco Montali, KRDB research centre for knowledge and data, Faculty of Computer Science, Free University of Bozen-Bolzano
Marco Montali is a Senior Researcher at the KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, Italy. He devises techniques grounded in Artificial Intelligence, formal methods, knowledge representation and reasoning, for the intelligent management of dynamic systems operating over data, with particular emphasis on business processes and multi-agent systems. On these topics, he authored more than 130 papers, appeared in top-tier, international journals, conferences, and workshops. In 2015, he received the “Marco Somalvico” 2015 Prize from the Italian Association for Artificial Intelligence, as the best under 35 Italian researcher who autonomously contributed to advance the state-of-the-art in AI.
|Datalog revisited for reasoning in Linked Data|
Linked Data provides access to huge, continuously growing amounts of open data and ontologies in RDF format that describe entities, links and properties on those entities. Equipping Linked Data with inference paves the way to make the Semantic Web a reality. In this lecture, I will describe a unifying framework for RDF ontologies and databases that we call deductive RDF triplestores. It consists in equipping RDF triplestores with Datalog inference rules. This rule language allows to capture in a uniform manner OWL constraints that are useful in practice, such as property transtivity or symmetry, but also domain-specific rules with practical relevance for users in many domains of interest. I will illustrate the expressivity of this framework for modeling Linked Data applications and its genericity for developing inference algorithms. In particular, we will show how it allows to model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We will also explain how it makes possible to efficiently extract expressive modules from Semantic Web ontologies and databases with formal guarantees, whilst effectively controlling their succinctness. Experiments conducted on real-world datasets have demonstrated the feasibility of this approach and its usefulness in practice for data integration and information extraction.
Marie-Christine Rousset, LIG (Laboratoire d’Informatique de Grenoble), University Grenoble-Alpes
Marie-Christine Rousset is a Professor of Computer Science at the University of Grenoble Alpes and senior member of Institut Universitaire de France. Her areas of research are Knowledge Representation, Information Integration, Pattern Mining and the Semantic Web. She has published around 100 refereed international journal articles and conference papers, and participated in several cooperative industry-university projects. She received a best paper award from AAAI in 1996, and has been nominated ECCAI fellow in 2005. She has served in many program committees of international conferences and workshops and in editorial boards of several journals.
|A Tutorial on Hybrid Answer Set Solving|
Answer Set Programming (ASP) has become an established paradigm for knowledge representation and reasoning, in particular, when it comes to solving knowledge-intense combinatorial (optimization) problems. ASP’s unique pairing of a simple yet rich modeling language with highly performant solving technology has led to an increasing interest in ASP in academia as well as industry. To further boost this development and make ASP fit for real world applications it is indispensable to equip it with means for an easy integration into complex software environments and of complementary forms of reasoning. In this tutorial, we describe how both issues are addressed in the ASP system clingo. At first, we outline features of clingo’s application programming interface (API) that are essential for multi-shot ASP solving that allows for dealing with continuously changing logic programs. We then switch to the API’s design for integrating complementary forms of reasoning and detail this in an extensive case study dealing with the integration of difference logic. We show how the syntax of the respective constraints is added to the modeling language and seamlessly merged into grounding process. And then develop in detail two theory propagators for difference logic and present how they are integrated in to clingo’s solving process.
Torsten Schaub, University of Potsdam, Germany and Inria, Bretagne Atlantique, Rennes
Torsten Schaub is university professor at the University of Potsdam, Germany, and holds an international chair at Inria Rennes, France. He is a fellow of ECCAI and the current president of the Association of Logic Programming. His current research focus lies on Answer set programming (ASP) and its applications, which materializes at potassco.org, the home of the open source project Potassco bundling software for ASP developed at Potsdam.
|Answer Set Programming with External Source Access|
Access to external information is an important need for Answer Set Programming (ASP), which is a booming declarative problem solving approach these days. External access not only includes data in different formats, but more general also the results of computations, and possibly in a two-way information exchange. Providing such access is a major challenge, and in particular if it should be supported at a generic level, both regarding the semantics and efficient computation.
In this tutorial, we describe how problem solving with ASP under external information access can be achieved using the dlvhex system. The system facilitates this access through special external atoms, which are a two-way API style interface between the rules of the program and an external source. The dlvhex system has a flexible plugin architecture that allows one to use multiple pre- and user-defined external atoms, which can be implemented, e.g., in Python or Java. After presenting the language of hex-programs that underlies the system, we shall consider how to solve problems using the ASP paradigm, and we shall specifically discuss how to use external atoms in this context. We then will turn to the dlvhex system, where we first review its architecture and computational underpinning. We then consider the features of the system, where the emphasis is on the specification of external atoms and on usability issues, illustrated by examples. In the last part, we demonstrate the development of a realistic HEX-program for a concrete real-world problem using Semantic Web technologies, and discuss specifics of the implementation process in the style of a tutorial.
Thomas Eiter, Institute of Information systems, Vienna University of Technology (TU Wien)
Thomas Eiter is a professor at TU Wien since 1998, where he heads the Knowledge Base Systems Group (KBS) and the Institute of Information Systems. Among his current research interests are knowledge representation and reasoning, computational logic, and declarative problem solving. He co‐chaired various meetings, recently LPAR 2017, ICLP 2015, KR 2014 and the Vienna Summer of Logic, the largest event in the history of logic. Eiter is an ECCAI Fellow, a Member of
|Uncertainty Reasoning for the Semantic Web|
The Semantic Web has attracted much attention, both from academia and industry. An important role in research towards the Semantic Web is played by formalisms and technologies for handling uncertainty and/or vagueness. In this lecture, I will first provide some motivating examples for handling uncertainty and/or vagueness in the Semantic Web. I will then give an overview of some own formalisms for handling uncertainty and/or vagueness in the Semantic Web.
Thomas Lukasiewicz, Department of Computer Science, University of Oxford
Thomas Lukasiewicz is a Professor of Computer Science in the Department of Computer Science at the University of Oxford and a Faculty Fellow at the Alan Turing Institute in London. Prior to this, he was holding a prestigious Heisenberg Fellowship by the German Research Foundation (DFG), affiliated with the University of Oxford, TU Vienna, Austria, and Sapienza University of Rome, Italy. His research interests are in artificial intelligence and information systems, including especially knowledge representation, the (Social and/or Semantic) Web, and databases. He received the IJCAI-01 Distinguished Paper Award, the AIJ Prominent Paper Award 2013, and the RuleML 2015 Best Paper Award. He is area editor for the journal ACM TOCL, associate editor for the journals JAIR and AIJ, and editor for the journal Semantic Web.
|Ontology querying: Datalog strikes back.|
In this tutorial we address the problem of ontology querying, that is, the problem of answering queries against a theory constituted by facts (the data) and inference rules (the ontology). A varied landscape of ontology languages exists in the scientific literature, with several degrees of complexity of query processing. We focus on Datalog+/-, a family of languages at the core of which are the so-called existential rules, that is, Datalog rules with the addition of existential quantification in the head. We illustrate the basic paradigms behind the main Datalog+/- languages, and then we proceed to present extension of such languages that incorporate relevant features such as disjunction and negation. For the tractable cases, we illustrate techniques for efficient query processing on large data sets.
Andrea Calì, Department of Computer Science and Information Systems
University of London, Birkbeck College
Giovambattista Ianni, Università della Calabria
Domenico Lembo, Sapienza Università di Roma
Scientific Advisory Board
Leopoldo Bertossi, Carleton University
Wolfgang Faber, University of Huddersfield
Birte Glimm, Universität Ulm
Georg Gottlob, Oxford University
Steffen Staab, University of Koblenz-Landau