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Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related to the process models from organizational sources using natural language processing techniques. As part of this study, a process ontology population (PrOnPo) methodology and tool is developed, which uses natural language parsers for extracting and interpreting the sentences and populating an event-driven process chain ontology in a fully automated or semi-automated (user assisted) manner. The purpose of this paper is to present applications of PrOnPo tool in different domains.

A multiple case study is conducted by selecting five different domains with different types of guidelines. Process ontologies are developed using the PrOnPo tool in a semi-automated and fully automated fashion and manually. The resulting ontologies are compared and evaluated in terms of time-effort and recall-precision metrics.

From five different domains, the results give an average of 70 percent recall and 80 percent precision for fully automated usage of the PrOnPo tool, showing that it is applicable and generalizable. In terms of efficiency, the effort spent for process ontology development is decreased from 250 person-minutes to 57 person-minutes (semi-automated).

The PrOnPo tool is the first one to automatically generate integrated process ontologies and process models from guidelines written in natural language.

Three-dimensional digital design and manufacturing technology are changing the current manufacturing pattern and have become the core of enterprise competition. However, the research and application of three-dimensional digital technology in the present phase have a strong bias toward the design of three-dimensional model and focus little on process planning. It restricts the development of manufacturing industry. Therefore, this paper aims to present a design scheme of three-dimensional digital process planning.

A three-dimensional digital process design method is developed by combining model-based definition technology and knowledge engineering technology. Model-based definition technology is used to display the process information. And knowledge engineering technology is used for process decision; meanwhile, ontology technology is introduced to describe process knowledge. And taking shaft part as an example, this paper establishes the general ontology of manufacturing process and the special ontology of shaft. This research provides an available method for the three-dimensional digital process planning.

Traditional process planning mainly is based on two-dimensional engineering drawing, which leads to the low efficiency and quality of process planning. Moreover, it cannot achieve effective mining and management of knowledge. Thus, applying an effective knowledge management technology into a three-dimensional process system is necessary.

This paper contributes to an available method for three-dimensional digital process planning.

The introduction of model-based definition technology makes process information display in three-dimensional environment. And ontology technology achieves sematic reference and efficient management of process knowledge.

The paper aims to investigate what is the best ontological framework of anticipatory systems. Its aim is to argue the thesis that the ontology on which anticipatory systems are based should be a dynamic one: a kind of process ontology. It seeks to include a demonstration of the fruitfulness of such an ontological framework for the investigation of anticipatory systems.

The methodology of the paper is a process ontological one. The objectives are achieved by a comparative analysis of the static and dynamic approaches to the ontological framework.

A process ontological framework is a reliable basis for the substantiation of the thesis that there is no great gap between living and non‐living systems as far as anticipation is concerned.

An example is represented of an anticipatory non‐living system that is artificially created and is programmed as a self‐control system. In this respect the paper has some practical implications.

A new approach is suggested to the investigation of anticipatory systems. It could be of interest not only for philosophers, but also for scientists who work on ontology as technology.

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 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.

This study proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases.

The development of EW-Onto started from defining the concepts and building taxonomies for earthwork operations and equipment following the METHONTOLOGY approach. In addition, several rules have been extracted from safety codes and implemented as SWRL rules. The ontology has been implemented using Protégé. The consistency of EW-Onto has been checked and it has been evaluated using a survey.

The assessment of EW-Onto by experts indicates an adequate level of consensus with the framework, as an initial step for explicit knowledge exchanges within the earthwork domain.

The use of an ontology within the earthwork domain can help: (1) link and identify the relationships between concepts, define earthwork semantics, and classify knowledge in a hierarchical way accepted by experts and end-users; (2) facilitate the management of earthwork operations and simplify information exchange and interoperability between currently fragmented systems; and (3) increase the stakeholders' knowledge of earthwork operations through the provision of the information, which is structured in the context of robust knowledge.

This paper proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases. EW-Onto represents the semantic values of the entities and the relationships, which are identified and formalized based on the basic definitions available in the literature and outlined by experts.

The purpose of this research is to articulate an analysis framework and a method for the cross‐national business‐to‐business integration electronic commerce (B2Bi EC) by exploring an ontology‐assisted schema and semantics resolution in the business process alignment with electronic commerce standards.

The paper presents an ontology‐assisted analysis method and alignment model in the implementation of the B2B electronic commerce standard specification over the existing trading partners' public processes in the syntactic and semantic integration and interoperability. An application of the Unified Modeling Language is made to analyze the public process in the domain and in the standard. Terms, concepts, relations, and links are created from the analysis results and converted into an ontology representation. Web Ontology Language is introduced to formulate the analyzed knowledge and experience to align the domain and the standard. There are correspondences and conflicts in the process of alignment. They are resolved via the shared and reusable ontology which is a convergence of the domain ontology and the standard ontology. The converged and shared ontology is achieved via a set of rules and heuristics that are created in the research.

The key of success in the B2Bi EC lies in the ability to accomplish the process interoperability and the schema comparability. Three main tasks have to be achieved to fulfill the requirements. This research constructs a prototype to implement the method. The prototype is used to illustrate the feasibility and validity of the method. A set of starter experiments has been conducted in use of a straight‐through example of a purchase order process in the alignment with the RosettaNet standard and the ebXML standard. The starter experiment serves as the baseline to demonstrate that the method is feasible and valid.

A syntactic and semantic analysis method and alignment model are developed and demonstrated in the research. Integration and interoperability are accomplished in use of the systematic and analytic method.

The purpose of this paper is to propose a solution to the problem of a lack of machine processable semantics in business process management.

The paper introduces a methodology that combines domain and company‐specific ontologies and databases to obtain multiple levels of abstraction for process mining and analysis. The authors valuated this approach with a real case study from the apparel domain, using a prototype system and techniques developed in the Process Mining Framework (ProM). The results of this approach are compared with similar research.

Semantically enriching process execution data can successfully raise analysis from the syntactic to the semantic level, and enable multiple perspectives of analysis on business processes. Combining this approach with complementary research in semantic business process management (SBPM) can provide results comparable to multidimensional analysis in data warehouse and on line analytical processing (OLAP) technologies.

The approach and prototype described in this paper improve the richness of semantics available for open‐source process mining and analysis tools like ProM, and the richness and detail of the resulting analysis.

The purpose of this study is to explore an ontology of designer-user interaction with a knowledge management foundation. To address this research gap, the authors ask the following research question: what types of knowledge on designer-user interactions are associated with design function and approach in creating effective design outcomes in a collaborative design process?

Based on ontology of a knowledge management foundation and 99 design projects, the authors conceptualized the ontology of designer-user interaction, which considers design role, function, approach and outcome as a knowledge of designer-user interaction in the design process.

Based on this analysis, the authors theorize an ontology of designer-user interactions with five dimensions: participant, role, function, design approach and design outcome. Also, this study presents a case study of how this ontology could be applied into the actual projects.

In this study, the authors explore an ontology of designer-user interaction with a knowledge management foundation, because previous interdisciplinary design studies have not formalized the types of designer-user interaction. To address this research gap, the authors ask the following research question: What types of knowledge on designer-user interactions are associated with design function and approach in creating effective design outcomes in a collaborative design process?

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.