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Communications of the ACM
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Behavioral matchmaking for service retrieval
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Ontology Matching
A Flexible Approach for Planning Schema Matching Algorithms
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Graph Matching Algorithms for Business Process Model Similarity Search
BPM '09 Proceedings of the 7th International Conference on Business Process Management
A workflow net similarity measure based on transition adjacency relations
Computers in Industry
The ICoP Framework: identification of correspondences between process models
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Similarity of business process models: Metrics and evaluation
Information Systems
Behavioral similarity: a proper metric
BPM'11 Proceedings of the 9th international conference on Business process management
On the refactoring of activity labels in business process models
Information Systems
Matching business process workflows across abstraction levels
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Probabilistic optimization of semantic process model matching
BPM'12 Proceedings of the 10th international conference on Business Process Management
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Comparing process models and matching similar activities has recently emerged as a research area of business process management. However, the problem is fundamentally hard when considering realistic scenarios: e.g., there is a huge variety of terms and various options for the grammatical structure of activity labels exist. While prior research has established important conceptual foundations, recall values have been fairly low (around 0.26) --- arguably too low to be useful in practice. In this paper, we present techniques for activity label matching which improve current results (recall of 0.44, without sacrificing precision). Furthermore, we identify categories of matching challenges to guide future research.