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The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization.

The deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science.

The MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search.

The use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users.

The MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches.

The MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users.

The MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.

The purpose of this paper is to develop a modeling and interactive operating method for virtual assembly (VA) to support assembly process generation based on interactive operation.

This paper puts forward an assembly semantic modeling method for interactive assembly and process generation after the analysis on requirements of operation process generation. Based on this semantic model, methods for semantic generation, semantic processing and assembly motion extraction from interactive operation are presented. Partial process generation of auto engine is proposed to verify the approaches in this paper.

The application shows that assembly semantic modeling and operating methods can support process generation based on VA operations.

The approaches presented in this paper improve the efficiency of assembly process, making assembly process intuitive and natural.

The purpose of this paper is to propose a solution for automating the task of matching business process models and search for correspondences with regard to the model semantics, thus improving the efficiency of such works.

A method is proposed based on combining several semantic technologies. The research follows a design-science-oriented approach in that a method together with its supporting artifacts has been engineered. It application allows for reusing legacy models and automatedly determining semantic similarity.

The method has been applied and the first findings suggest the effectiveness of the approach. The results of applying the method show its feasibility and significance. The suggested heuristic computing of semantic correspondences between semantically heterogeneous business process models is flexible and can support domain users.

Even though a solution can be offered that is directly usable, so far the full complexity of the natural language as given in model element labels is not yet completely resolvable. Here further research could contribute to the potential optimizations and refinement of automatic matching and linguistic procedures. However, an open research question could be solved.

The method presented is aimed at adding to the methods in the field of business process management and could extend the possibilities of automating support for business analysis.

The suggested combination of semantic technologies is innovative and addresses the aspect of semantic heterogeneity in a holistic, which is novel to the field.

The need to flexibly react to changing demands and to cost-efficiently manage customized production even for lot size of one requires a dynamic and holistic integration of service-based processes within and across enterprises of the value chain. In this context, this paper aims at presenting ODERU, the authors’ novel pragmatic approach for automatically implementing service-based manufacturing processes at design and runtime within a cloud-based elastic manufacturing platform.

ODERU relies on a set of semantic annotations of business process models encoded into an extension of the business process model and notation (BPMN) 2.0 standard. Leveraging the paradigms of semantic SOA and XaaS, ODERU integrates pattern-based semantic composition of process service plans with QoS-based optimization based on multi-objective constraint optimization problem solving.

The successful validation of ODERU in two industrial use cases for maintenance process optimization and automotive production in the European project CREMA revealed its usefulness for service-based process optimization in general and for significant cost reductions in maintenance in particular.

ODERU provides a pragmatic and flexible solution to optimal service composition with the following three main advantages: full integration of semantic service selection and composition with QoS-based optimization; executability of the generated optimal process service plans by an execution environment as they include service assignments, data flow (variable bindings) and optimal variable assignments; and support of fast replanning in a single model and plan.

Automating the task of identifying process weaknesses using process models is promising, as many organizations have to manage a large amount of process models. The purpose of this paper is to introduce a pattern-based approach for automatically detecting potential process weaknesses in semantic process models, thus supporting the task of business process improvement.

Based on design research, combined with a case study, the authors explore the design, application and evaluation of a pattern-based process weakness detection approach within the setting of a real-life case study in a German bank.

Business process weakness detection can be automated to a remarkable extent using pattern matching and a semantic business process modeling language. A case study provided evidence that such an approach highly supports business process analysts.

The presented approach is limited by the fact that not every potential process weakness detected by pattern matching is really a weakness but just gives the impression to be one. Hence, after detecting a weakness, analysts still have to decide on its authenticity.

Applying weakness patterns to semantic process models via pattern matching allows organizations to automatically and efficiently identify process improvement potentials. Hence, this research helps to avoid time- and resource-consuming manual analysis of process model landscapes.

The approach is not restricted to a single modeling language. Furthermore, by applying the pattern matching approach to a semantic modeling language, the authors avoid ambiguous search results. A case study proves the usefulness of the approach.

The purpose of this paper is to provide a model for semantically annotating business process models stored in a repository. The purpose of the model is to facilitate the searching of process models, to support navigating the process model repository and to enhance the understandability of process models.

The annotation model has been developed by following a design science research approach. By analysing existing works the authors have identified the requirements for such an annotation model. Following these requirements the authors have designed a context‐based process semantic annotation model. As a proof of concept, the annotation model has been implemented in a repository prototype and its applicability demonstrated. Finally, the annotation model has been evaluated in two controlled experimental studies.

The proposed annotation model can be used to positively affect searching, navigation and understandability of process models.

Application of the annotation model and clearly defined concepts in the process model repository is expected to improve the process of finding relevant process models in the repository for reuse.

The contribution of the paper is both theoretical and practical. It provides a clear‐cut context‐based process semantic annotation model that can be implemented easily in any process model repository.

The purpose of this paper is to illustrate and evaluate the semantic process benchmarking concept.

The authors' approach includes the use of metamodels and ontologies, which make the process models syntactically and semantically comparable. Furthermore, a software prototype is presented to analyze and compare individual process models and their performance information. Thereafter, the technical, conceptual, and economic perspectives of the approach's evaluation are aligned with their respective outcomes.

The evaluation proves that this approach is generally suitable to generate novel and useful information on different process models and their performance within the same problem domain. However, the initial set‐up costs are high and will only pay off once process models are used regularly.

The proposed approach depends strongly on the availability of appropriate metrics and ontologies, as well as on the annotation of these ontologies to process models, which is a time‐consuming task. If large benchmarking clearing centers are established, the approach will be more cost‐effective. The developed SEMAT prototype, that demonstrates and proves the proposed approach's general viability, supports cost‐effective ontology engineering and annotation in the context of semantic process benchmarking initiatives.

To date, process benchmarking has primarily been a manual process. In this article, the authors suggest an approach that allows time‐consuming and costly process analysis to be partially automated, which makes the performance indicators, as well as qualitative differences between processes, apparent.

Domain-specific process modeling has been proposed in the literature as a solution to several problems in business process management. The problems arise when using only the generic Business Process Model and Notation (BPMN) standard for modeling. This language includes domain ambiguity and difficult long-term model evolution. Domain-specific modeling involves developing concept definitions, domain-specific processes and eventually industry-standard BPMN models. This entails a multi-layered modeling approach, where any of these artifacts can be modified by various stakeholders and changes done by one person may influence models used by others. There is therefore a need for tool support to keep track of changes done and their potential impacts. The paper aims to discuss these issues.

The authors use a multi-context systems-based approach to infer the impacts that changes may cause in the models; and alsothe authors incrementally map components of business process models to ontologies.

Advantages of the framework include: identifying conflicts/inconsistencies across different business modeling layers; expressing rich information on the relations between two layers; calculating the impact of changes taking place in one layer to the rest of the layers; and selecting incrementally the most appropriate semantic models on which the transformations can be based.

The authors consider this work as one of the foundational bricks that will enable further advances toward the governance of multi-layer business process modeling systems. Extensive usability tests would enable to further confirm the findings of the paper.

The approach described here should improve the maintainability, reuse and clarity of business process models and in extension improve data governance in large organizations. The approaches described here should improve the maintainability, reuse and clarity of business process models. This can improve data governance in large organizations and for large collections of processes by aiding various stakeholders to understand problems with process evolutions, changes and inconsistencies with business goals.

This paper fulfills an identified gap to enabling semantically aided domain–specific process modeling.

From knowledge management point of view, business process models and ontologies are two essential knowledge artifacts for organizations that consume similar information sources. In this study, the PROMPTUM method for integrated process modeling and ontology development that adheres to well-established practices is presented. The method is intended to guide practitioners who develop both ontologies and business process models in the same or similar domains.

The method is supported by a recently developed toolset, which supports the modeling of relations between the ontologies and the labels within the process model collections. This study introduces the method and its companion toolset. An explanatory study, that includes two case studies, is designed and conducted to reveal and validate the benefits of using the method. Then, a follow-up semi-structured interview identifies the perceived benefits of the method.

Application of the method revealed several benefits including the improvements observed in the consistency and completeness of the process models and ontologies. The method is bringing the best practices in two domains together and guiding the use of labels within process model collections in ontology development and ontology resources in business process modeling.

The proposed method with its tool support is a pioneer in enabling to manage the labels and terms within the labels in process model collections consistently with ontology resources. Establishing these relations enables the definition and management of process model elements as resources in domain ontologies. Once the PROMPTUM method is utilized, a related resource is managed as a single resource representing the same real-world object in both artifacts. An explanatory study has shown that improvement in consistency and completeness of process models and ontologies is possible with integrated process modeling and ontology development.

The purpose of this paper is to identify the benefits of an approach in which document management systems (DMSs) are based on a formal and explicit document model, primarily in terms of facilitating domain-specific customization.

Within this paper, a generic document model is proposed. The model consists of two layers. A general purpose layer, which represents common features of the documents, and a domain-specific layer, modeling properties particular to application domain. The general purpose layer is based on ISO 82045, providing high degree of interoperability with other systems developed with respect to this set of standard.

Splitting document model into the layers enables DMSs to be tailored for each particular domain of application, depending on the general purpose layer. The existence of domain-specific layer allows documents to be interpreted differently in different domains of application.

In order to enable customization of DMS for a particular domain, the implementation of domain-specific document layer is required. Also, the proposed model does not explicitly deal with document dynamics.

The proposed document ontology is general enough to provide the representation of documents not depending on a specific scope of application, yet flexible enough to enable extensions through which domain-specific document features can be expressed. The separation of document model enables development of core DMS offering services relying explicitly on the general purpose layer on one hand, as well as domain-specific customization of DMS on the other.