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Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations.

Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies’ semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies.

To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies.

This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research.

This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results.

To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

The purpose of this paper is to answer the question posed in the literature: “What is the definition of law and justice?”

The paper addresses the question from the vantage point of contrasting epistemological premises of liberalism and unity of knowledge.

Only the epistemology of unity of knowledge can answer the question as posed above. Rationalism and liberalism are unable to answer this question.

The empirical section could be elaborated further, but this is beyond the scope of the paper.

The paper opens up a vista of applications in the area of institutional change and the moral and ethical edicts of law and justice for purposes of intellection and application.

The epistemological inquiry under unity of knowledge has answered the otherwise impending quest for an answer to the question that has remained unanswered.

The current work provides a framework for the ranking of ontology development methodologies (ODMs).

The framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used.

State-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades.

There is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.

Being able to communicate a clear identity to different stakeholders is crucial for SMEs and startups in today’s world, which is characterised by accelerated innovation, growing competition and increasingly connected consumers. However, this can be a complex task for small organisations. The purpose of this paper is to propose a visual tool that supports entrepreneurs in SMEs and startups to collaboratively develop their identity communication strategy.

The paper follows a design science research approach. The authors propose the design of a tool as well as some preliminary qualitative evaluations. The authors conducted three iterations between design and evaluation, where the results of the evaluations are implemented in the design.

The authors demonstrate how to design strategic tools for allowing teams to co-design their identity communication strategy and present the tool. The authors also evaluate its use and find out through the preliminary evaluations that it could be easy to use and useful for practitioners.

The originality of this paper lays in the novelty of the tool and its development. Such a tool addressing identity communication strategy has not been developed with a scientific approach until now.

A literature review was conducted in order to establish a detailed definition of a knowledge-intensive process (KiP). Moreover, relevant theories from humanities – especially the fields of linguistics and philosophy – were thoroughly researched and adapted for establishing descriptive and analytical foundations for the phenomena involved. Finally, a cognitive business process management (BPM) theory was proposed in order to assess how sufficient are its respective explanatory and predictive powers. This study aims to discuss the aforementioned objective.

This study proposes a novel theory for KiPs that describe the process flow based on the participants' interactions and their beliefs, desires and intentions as the main drivers of the process enactment and execution.

The proposal puts forth a comprehensive definition of a KiP, depicting knowledge intensity, participant interaction and decision-making. The inner dynamics of each of these two elements (as well as other associated elements) are described as an information systems (IS) theory that enables the study of KiPs in detail, going beyond the typical techniques of the BPM field and common obstacles.

A theory proposal for KiP that applies concepts from speech act theory and intentional states as the main drivers for understanding the process dynamics are, to the best of the authors' knowledge, not present at the literature. Being an original proposal, the real-world scenario discussed brings up the explanatory and predictive powers of the theory as well as its innovative value for research in the field.

Leading-edge information and communication technology provides the base to facilitate obtaining, interoperating and federating shared metadata knowledge in collaborative networks from multiple heterogeneous data sources. The purpose of this study is to develop a methodology and a set of mechanisms to support this task in the collaborative environment.

In this paper, the authors first identify and capture four main typical sources to find or generate metadata knowledge for shared data in emerging networked environments, including existing well-designed metadata, the typical ones are relational schemas of existing databases in the environment; fragmented metadata sources, i.e. metadata that can be realized from existing mission statements and example application scenarios in the environment, usually characterized by their fragmented, lightweight and behavior-intensive features; extracting metadata for simple labeled unstructured data, e.g. textual communications among its stakeholders; and semantic constraints on metadata, e.g. the temporal data behavior could be generated from governance policies in the environment. Second, the authors introduce their systematic methodology to the unification of the resulted metadata consisting of four semiautomated unification steps that gradually develops and enhances a unified ontology for the environment, formalized in web ontology language.

The methodology steps and their corresponding mechanisms are described and exemplified in detail in this paper. Furthermore, this paper presents the outcome of applying the authors’ methodology to an example emerging case through the generation of a unified ontology for that environment.

The addressed example application area is a real case in the field of higher education in China and therefore serves as a proof of concept and verification of the effectiveness of the authors’ proposed approach.

The purpose of this study is to propose a knowledge‐based mobile learning framework that integrates various types of museum‐wide content, and supports ubiquitous, context‐aware, personalized learning for museums.

A unified knowledge base with multi‐layer reusable content structures serves as the kernel component to integrate content from exhibitions for education and collection in a museum. The How‐Net approach is adopted to build a unified natural and cultural ontology. The ontology functions as a common and sharable knowledge concept that denotes each knowledge element in the unified knowledge base, and associates each learner's learning context and usage with a content and usage profile respectively. Data mining algorithms, e.g. association mining and clustering, are applied to discover useful patterns for ubiquitous personalization from these content and usage profiles.

A pilot project based on the proposed framework has been successfully implemented in the Life Science Hall of the National Museum of Natural Science (NMNS), Taiwan, demonstrating the feasibility of this framework.

This study proposes a mobile learning framework that can be replicated in many museums. This framework improves learners' learning experiences with rich related content, and with ubiquitous, proactive and adaptive services. Museums can also benefit from implementing this framework through outreach services for educational, promoting and usability needs from combining mobile and Internet communication technologies and learning services.

Ontologies are defined as consensual formal conceptualisation of shared knowledge. However, the explicit overlap between diverse ontologies is usually very low since they are typically constructed by different experts. Hence, the purpose of this paper is to suggest to exploit “wisdom of crowds” to assess the maximal potential for inter-ontology agreement on controversial domains.

The authors propose a scheme where independent ontology users can explicitly express their opinions on the specified set of ontologies. The collected user opinions are further employed as features for machine classification algorithm to distinguish between the consensual ontological relations and the controversial ones. In addition, the authors devised new evaluation methods to measure the reliability and accuracy of the presented scheme.

The accuracy of the relation classification (90 per cent) and the reliability of user agreement annotations were quite high (over 90 per cent). These results indicate a fair ability of the scheme to learn the maximal set of consensual relations out of the specified set of diverse ontologies.

The data sets and the group of participants in our experiments were of limited size and thus the presented results are promising but cannot be generalised at this stage of research.

A diversity of opinions expressed by different ontologies has to be resolved in order to digitise many domains of knowledge (e.g. cultural heritage, folklore, medicine, economy, religion, history, art). This work presents a methodology to formally represent this diverse knowledge in a rich semantic scheme where there is a need to distinguish between the commonly shared and the controversial relations.

To the best of the knowledge this is a first proposal to consider crowd-based evaluation and classification of ontological relations to maximise the inter-ontology agreement.

This paper aims to propose a method based on Linked Data and Semantic Web principles for composing microservices through data integration. Two frameworks that provide support for the proposed composition method are also described in this paper: Linkedator, which is responsible for connecting entities managed by microservices, and Alignator, which aligns semantic concepts defined by heterogeneous ontologies.

The proposed method is based on entity linking principles and uses individual matching techniques considering a formal notion of identity. This method imposes two major constraints that must be taken into account by its implementation: architectural constraints and resource design constraints.

Experiments were performed in a real-world scenario, using public government data. The obtained results show the effectiveness of the proposed method and that, it leverages the independence of development and composability of microservices. Thereby, the data provided by microservices that adopt heterogeneous ontologies can now be linked together.

This work only considers microservices designed as data providers. Microservices designed to execute functionalities in a given application domain are out of the scope of this work.

The proposed composition method exploits the potential data intersection observed in resource-oriented microservice descriptions, providing a navigable view of data provided by a set of interrelated microservices. Furthermore, this study explores the applicability of ontology alignments for composing microservices.

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.