Ontology Alignment Evaluation Initiative - The OAEI 2017 Campaign

OAEI 2017::Instance Matching Track

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 in the following independent tasks, namely:

The datasets corresponding to the tasks will be available from June 1st 2017 and frozen by June 30th 2017. A detailed task description will be also provided in terms of expected results and data-format required.

To participate to the Instance Matching Track, submit results related to one, more, or even all the expected tasks.

Task description

SYNTHETIC Task (show/hide results)

The goal of the SYNTHETIC task is to determine when two OWL instances describe the same Creative Work. A dataset is composed of a Tbox and corresponding Abox. Source and target datasets share almost the same Tbox (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 (Abox1.ttl) against the instances of the target dataset (Abox2.ttl). 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 dataset can have none or one matching counterparts in the target dataset. We ask the participants to map only instances of Creative Works (NewsItem, BlogPost and Programme) and not the instances of the other classes.

The SYNTHETIC task is composed of two datasets with different scales (i.e., number of instances to match):

In both datasets, the goal is to discover the matching pairs (i.e., mappings) among the instances in the source dataset and the instances in the target dataset.

The SYNTHETIC 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 (jsaveta@ics.forth.gr) and Irini Fundulaki (fundul@ics.forth.gr).

DOREMUS Task (show/hide results)

The DOREMUS task contains two real-world benchmarks, based on data from the DOREMUS project describing musical works from the catalogs of two French cultural institutions: La Bibliothque Nationale de France (BnF) and La Philharmonie de Paris (PP).

DOREMUS sub-tasks

Download datasets: Datasets

Contact: Konstantin Todorov (todorov@lirmm.fr) and Manel Achichi (achichi@lirmm.fr)