Matching Representations at different Levels of Granularity
Final Report Abstract
Within this project, we tried to develop, implement and evaluate methods that are specifically designed to create mappings between entities described at different levels of granularity. This refers to mappings that do not express equivalence between the matched elements, e.g., subsumption correspondences, or equivalence correspondences between an element and a complex that consists of several elements. We argued that this requires a modelling style that distinguishes between the layer of labels and the layer of logical entities (concepts, activities). We have continued to work on the idea first introduced in the proposal and finally developed a matching system called MAMBA. By participating in the OAEI 2015 we have proven that the approach outperforms the majority of alternative approaches. With a focus on process model matching, we have developed an approach for ensemble matching, which takes the mappings generated by different systems as input to generate a mapping as output, which is a subset of the ingoing matching hypothesis. This process needs to be controlled by a set of constraints, including cardinality constraints. We have focussed on a cardinality constraint that is still restrictive enough to have a significant impact, while it still supports the generation of subsumption correspondences. The overall model, which is in accordance to MAMBA, based on a Markov Logic formalization, achieved better results than each of the members of the ensemble. This shows that we were able to relax the original 1:1 constraint in the appropriate way. We have successfully developed two important approaches, one for process model matching and one of ontology matching. We evaluated both approaches via the PMMC and OAEI datasets and compared them against the results of these evaluation campaigns. The results show that our approaches outperform the majority of the current state of the art. However, we have not yet developed a generic matching system that combines both approaches. Such a combined solution would also require a more expressive label analysis which takes care of the specifics (process model vs. ontology), while the results of this analysis can be expressed within the same formalisation. Unfortunately, the results of WP 1 were not as good as expected. In particular, the attempts to use frames failed and the use of distributional semantics generated more noise than useful information. For that reason, we could not implement a component that creates a useful and expressive label representation. This is definitely a point that we have to work on in the future. With respect to scalability we detected an interesting way to guide the search for an optimal solution by first solving a relaxed, reversed version of the optimization problem. We implemented an automated generation of the relaxed problem which is very much tailored to the specifics of the matching domain. It is an open question, whether the approach can be used in a general way to speed up certain kind of optimization problems that suffer from a large search space which can be down‐sized significantly based on the solution of the relaxed problem.
Publications
- A new paradigm for alignment extraction. Proceedings of the 10th International Workshop on Ontology Matching collocated with the 14th International Semantic Web Conference Bethlehem, PA, USA, 2015
Christian Meilicke and Heiner Stuckenschmidt
- MAMBA ‐ Results for the OAEI 2015. Proceedings of the 10th International Workshop on Ontology Matching collocated with the 14th International Semantic Web Conference Bethlehem, PA, USA, 2015
Christian Meilicke and Heiner Stuckenschmidt
- The Process Model Matching Contest 2015. GI‐Edition / Proceedings: Lecture notes in informatics, enterprise modelling and information systems architectures: Proceedings of the 6th International Workshop on Enterprise Modelling and Information Systems Architectures, September 3‐4, 2015 Innsbruck, Austria; 127‐155. Gesellschaft für Informatik, Bonn, 2015
Goncalo Antunes et al.
- Detecting meaningful compounds in complex class labels. In: Lecture notes in computer scienceKnowledge Engineering and Knowledge Management : 20th International Conference, EKAW 2016, Bologna, Italy, November 19‐ 23, 2016, proceedings; 621‐635. Springer, Cham, 2016
Heiner Stuckenschmidt, Simone Paolo Ponzetto and Christian Meilicke
(See online at https://doi.org/10.1007/978-3-319-49004-5_40) - Probabilistic evaluation of process model matching techniques. Lecture notes in computer science, Conceptual modeling: 35th international conference, ER 2016. Springer, Cham, 2016
Elena Kuss, Henrik Leopold, Han Van der Aa, Heiner Stuckenschmidt and Hajo A. Reijers
(See online at https://doi.org/10.1007/978-3-319-46397-1_22) - Results of the Ontology Alignment Evaluation Initiative 2016. Proceedings of the 11th International Workshop on Ontology Matching co‐located with the 15th International Semantic Web Conference (ISWC 2016) Kobe, Japan, 2016
Manel Achichi et al.
- Automatic classification to matching patterns for process model matching evaluation. Proceedings of the ER Forum 2017 and the ER 2017 Demo Track co‐located with the 36th International Conference on Conceptual Modelling (ER 2017) Valencia, Spain. RWTH, Aachen, 2017
Elena Kuss and Heiner Stuckenschmidt
- Overcoming individual process model matcher weaknesses using ensemble matching. Decision support systems, Vol. 100, page 15‐26, Elsevier, Amsterdam [u.a.], 2017
Christian Meilicke, Henrik Leopold, Elena Kuss, Heiner Stuckenschmidt and Hajo A. Reijers
(See online at https://doi.org/10.1016/j.dss.2017.02.013) - Ranking‐based evaluation of process model matching. In: Lecture notes in computer science. On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Springer, Cham, 2017
Elena Kuss, Henrik Leopold, Christian Meilicke and Heiner Stuckenschmidt
(See online at https://doi.org/10.1007/978-3-319-69462-7_19) - Results of the Ontology Alignment Evaluation Initiative 2017. Proceedings of the 12th International Workshop on Ontology Matching co‐located with the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, 2017
Manel Achichi et al.
