The Instance Matching Track aims at evaluating the performance of matching tools when the goal is to detect the degree of similarity between pairs of items/instances expressed in the form of OWL Aboxes.
The track is organized consists of the SPIMBENCH Task.
The goal of the SPIMBENCH task is to determine when two OWL instances describe the same Creative Work. A dataset is composed of a Tbox (contains the ontology and the instances) and corresponding Abox (contains only the instances). The datasets share almost the same ontology (with some difference in the properties' level, due to the structure-based transformations). Ontology instances are described through 22 classes, 31 DatatypeProperty, and 85 ObjectProperty properties. From those properties, we have 1 InverseFunctionalProperty and 2 FunctionalProperties. What we expect from participants. Participants are requested to match instances in the source dataset (Tbox1) against the instances of the target dataset (Tbox2). The task goal is to produce a set of mappings between the pairs of matching instances that are found to refer to the same real-world entity. An instance in the source (Tbox1) dataset can have none or one matching counterparts in the target dataset (Tbox2). We ask the participants to map only instances of Creative Works (http://www.bbc.co.uk/ontologies/creativework/NewsItem, http://www.bbc.co.uk/ontologies/creativework/BlogPost and http://www.bbc.co.uk/ontologies/creativework/Programme) and not the instances of the other classes.
The SPIMBENCH task is composed of two datasets with different scales (i.e., number of instances to match):
The SPIMBENCH datasets are generated and transformed using SPIMBENCH by altering a set of original data through value-based, structure-based, and semantics-aware transformations (simple combination of transformations).
Download datasets: Datasets
Contact: Tzanina Saveta (firstname.lastname@example.org) and Irini Fundulaki (email@example.com).