Keyword search is the foremost approach for searching information and it has been successfully applied for retrieving non-structured documents such as text and multimedia files. Nonetheless, retrieving information from (unstructured or semi-structured) documents is intrinsically different from querying structured data sources with either an explicit schema, as relational databases or triple stores, or an implicit one, as tables in textual documents and on the Web. Consequently this model has left out the structured data sources which are typically accessed through structured queries, e.g. Structured Query Language (SQL) queries over relational databases or SPARQL Protocol and RDF Query Language (SPARQL) queries over Linked Data graphs.
Structured queries are not end-user oriented and far away from a natural expression of users’ information needs by means of keywords, given that their formulation is based on a quite complex syntax and requires some knowledge about the structure of the data to be queried. Over the past several years, these facts triggered the research community and big data technology vendors to put a lot of effort into developing new approaches for keyword search over structured databases and it is still a primary research and industrial concern.
The aim of this multidisciplinary workshop is to bring together researchers from Databases, Information Retrieval, Natural Language Processing, Semantic Web, Human-Computer Interaction, and to combine their perspectives and research to address the above-mentioned issues.
In particular, we wish to encourage researchers to discuss the opportunities, challenges, results obtained in the development and evaluation of “complete”, “ready-to-market” keyword search applications over structured data. We are in particular interested in proposal dealing with systemic approaches which manage all the phases of the keyword search, from the management of the data, query formulation, interpretation, computation, ranking and visualization of the results, as well as rigorous evaluation methodologies for such systems.
We invite papers from researchers and practitioners working in relational databases, XML, RDF, Linked Open Data, information extraction, natural language processing, data warehouses, and related areas to submit their original papers to this workshop.
Submission deadline: November 14, 2016
Notification of acceptance: December 20, 2016
Camera ready: January 15, 2017
Workshop day: March 21, 2017
General areas of interests include, but are not limited to, the following topics:
Papers should be formatted according to the ACM SIG Proceedings Template.
Papers should be two-four pages (maximum) in length.
Papers will be peer-reviewed by members of the program committee through single-blind peer review, i.e. authors do *not* need to be anonymized. Selection will be based on originality, clarity, and technical quality. Papers should be submitted in PDF format to the following address:
Accepted papers will be published online as a volume of the CEUR-WS proceeding series.
Nicola Ferro, University of Padua, Italy
Francesco Guerra, University of Modena and Reggio Emilia, Italy
Zachary Ives, University of Pennsylvania, PA, USA
Gianmaria Silvello, University of Padua, Italy
Martin Theobald, Ulm University, Germany
Maristella Agosti, University of Padua, Italy
Sihem Amer-Yahia, University of Grenoble, France
Klaus Berberich, Max Planck Institute for Informatics, Germany
Vassilis Christophides, FORTH/ICS and University of Crete, Greece
Fabio Crestani, University of Lugano (USI), Switzerland
Arjen de Vries, Radboud University, The Netherlands
Norbert Fuhr, University of Duisburg-Essen, Germany
Paul Groth, Elsevier Labs, The Netherlands
Claudia Hauff, Delft University of Technology, The Netherlands
Vagelis Hristidis, UC Riverside, USA
Mihai Lupu, Vienna University of Technology, Austria
Kjetil Nørvåg, Norwegian University of Science and Technology, Norway
Paolo Papotti, Qatar Computing Research Institute, Qatar
Raffaele Perego, ISTI-CNR, Pisa, Italy
Thomas Roelleke, Queen Mary University of London, UK
Ralf Schenkel, University of Trier, Germany
Tobias Schreck, Graz University of Technology, Austria
Letizia Tanca, Politecnico di Milano, Italy
Yannis Velegrakis, University of Trento, Italy
Electrical & Information Engineering Department, Polytechnic University of Bari, Italy
Tommaso Di Noia is Associate Professor at Polytechnic University of Bari, Italy. Currently, his main research topics deal with Linked Open Data and how to leverage the knowledge encoded in Big Data datasets in order to develop content-based/collaborative/context-aware recommendation engines (recommender systems). Strongly related to this latter research topic is the analysis and modeling of User Profiles in Information Retrieval scenarios. As for Linked Open Data, he is interested in the whole process of production, publication, maintenance and exploitation of the ultimate technological solutions for Open Data.
With the emergence of the so called knowledge graphs, a new player arrived in the information arena. As as of today, we have got used to search engines able to semantically disambiguate (sequences of) keywords and map them to their private knowledge graphs in order to show augmented search results.
Actually, due to huge quantity of information they usually encode, knowledge graphs may have a key role in many knowledge and data intensive applications. Among them, more recently, personalised filtering and recommending are gaining momentum as tools able to suggest to the users entities they might be interested in thus helping them to find items best matching their preferences. In this talk we will show and discuss results and challenges in designing and developing recommendation engines fed by knowledge graphs.