The number of datasets published in the Web of Data as part of the Linked Data Cloud is constantly increasing. The Linked Data paradigm is based on the unconstrained publication of information by different publishers, and the interlinking of Web resources across knowledge bases. In most cases, the cross-dataset links are not explicit in the dataset and must be automatically determined using Instance Matching (IM) and Link Discovery tools amongst others. The large variety of techniques requires their comparative evaluation to determine which one is best suited for a given context. Performing such an assessment generally requires well-defined and widely accepted benchmarks to determine the weak and strong points of the proposed techniques and/or tools.
A number of real and synthetic benchmarks that address diﬀerent data linking challenges have been proposed for evaluating the performance of such systems. So far, only a limited number of link discovery benchmarks target the problem of linking geo-spatial entities.
However, some of the largest knowledge bases on the Linked Open Data Web are geospatial knowledge bases (e.g., LinkedGeoData with more than 30 billion triples). Linking spatial resources requires techniques that diﬀer from the classical mostly string-based approaches. In particular, considering the topology of the spatial resources and the topological relations between them is of central importance to systems driven by spatial data.
We believe that due to the large amount of available geospatial datasets employed in Linked Data and in several domains, it is critical that benchmarks for geospatial link discovery are developed.
The proposed Task entitled “HOBBIT Link Discovery Taskk” is a task of OAEI 2017.5.
The aim of the Task is to test the performance of Link Discovery tools that implement string-based as well as topological approaches for identifying matching spatial entities. The different frameworks will be evaluated for both accuracy (precision, recall and f-measure) and time performance.
Q & A
Tasks and Training Data
We will use TomTom datasets in order to create the appropriate benchmarks. TomTom datasets contain representations of traces (GPS fixes). Each trace consists of a number of points. Each point has time stamp, longitude, latitude and speed (value and metric). The points are sorted by timestamp of the corresponding GPS fix (ascending).
This version of the challenge will comprise the following tasks:
- Task 1 (Linking) will measure how well the systems can match traces that have been altered using string-based approaches along with addition and deletion of intermediate points.As the TomTom dataset only contains coordinates and in order to apply string-based modifications based on LANCE we have replaced a number of those with labels retrieved from Google Maps Api, Foursquare Api and Nominatim Openstreetmap Api. This task also contains changes on date format and changes on coordinate formats.
- Task 2 (Spatial) measures how well the systems can identify DE-9IM (Dimensionally Extended nine-Intersection Model) topological relations. The supported spatial relations are the following: Equals, Disjoint, Touches, Contains/Within, Covers/CoveredBy, Intersects, Crosses, Overlaps and the traces are represented in Well-known text (WKT) format.For each relation, a different pair of source and target dataset will be given to the participants.
 Saveta, E. Daskalaki, G. Flouris, I. Fundulaki, and A. Ngonga-Ngomo. LANCE: Piercing to the Heart of Instance Matching Tools. In ISWC, 2015.
Registration and Submission
Participants may register their tool using this form. For the preliminary version of system papers, you may submit PDF paper (e.g., LogMap_prelim.pdf) using this form (requires a google account and a valid email). The final version of system papers may be submittef as PDF (e.g., LogMap_final.pdf) paper using this form (requires a google account and a valid email).
- Pavel Shvaiko (Main contact)
Informatica Trentina, Italy
E-mail: pavel [dot] shvaiko [at] infotn [dot] it
- Jérôme Euzenat
INRIA & Univ. Grenoble Alpes, France
- Ernesto Jiménez-Ruiz
University of Oslo, Norway
- Michelle Cheatham
Wright State University, USA
- Oktie Hassanzadeh
IBM Research, USA
- Alsayed Algergawy, Jena University, Germany
- Manuel Atencia, INRIA & Univ. Grenoble Alpes, France
- Zohra Bellahsene, LRIMM, France
- Olivier Bodenreider, National Library of Medicine, USA
- Marco Combetto, Informatica Trentina, Italy
- Valerie Cross, Miami University, USA
- Warith Eddine Djeddi, LIPAH & LABGED, Tunisia
- Jérôme David, University Grenoble Alpes & INRIA, France
- Gayo Diallo, University of Bordeaux, France
- Zlatan Dragisic, Linköpings Universitet, Sweden
- Alfio Ferrara, University of Milan, Italy
- Wei Hu, Nanjing University, China
- Valentina Ivanova, Linköpings Universitet, Sweden
- Antoine Isaac, Vrije Universiteit Amsterdam & Europeana, Netherlands
- Valentina Ivanova, Linköpings Universitet, Sweden
- Ryutaro Ichise, National Institute of Informatics, Japan
- Daniel Faria, Instituto Gulbenkian de Cincia, Portugal
- Patrick Lambrix, Linköpings Universitet, Sweden
- Juanzi Li, Tsinghua University, China
- Vincenzo Maltese, University of Trento, Italy
- Fiona McNeill, University of Edinburgh, UK
- Peter Mork, Noblis, USA
- Andriy Nikolov, Open University, UK
- Axel Ngonga, University of Leipzig, Germany
- Catia Pesquita, University of Lisbon, Portugal
- Dominique Ritze, University of Mannheim, Germany
- Umberto Straccia, ISTI-C.N.R., Italy
- Ondrej Svab-Zamazal, Prague University of Economics, Czech Republic
- Cássia Trojahn, IRIT, France
- Ludger van Elst, DFKI, Germany