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Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

The main purpose of this paper is to conduct an in-depth theoretical review and analysis for the fields of knowledge management (KM) and investigate the future research trend about KM.

At first, few theoretical basis about KM which include definitions and stages about KM have been summarized and analyzed. Then a comprehensive review about the major approaches for designing the KM system from different perspectives including knowledge representation and organization, knowledge sharing and performance measure for KM has been conducted.

The contributions of this paper will be useful for both academics and practitioners for the study of KM.

For this research, the focus is on conducting an in-depth theoretical review and analysis of KM.

Due to the globalization of trade, hundreds of millions containers pass every year through world ports. Such a situation is extremely challenging in terms of securing freight transport operations. However, costs and lead-times are still very important components of supply chains' performance models. Therefore, the drive for enhanced safety and security cannot be made at the expense of these other two factors of competitiveness, and the processes implemented by the global supply chain links, including the maritime port one, should tend to a joint optimization of trade facilitation and operational safety / security.

The research on which this paper feeds back falls within the frame of this mixed performance requirement. More specifically, the paper presents a decision-support system dedicated to managing the risks associated with land and maritime container transportation; this system is based on the modeling of the knowledge of a group of experts, and covers the three phases of risk identification, assessment and avoidance / mitigation.

This paper aims to investigate the use of crowdsourcing in the enhancement of an ontology of taxonomic knowledge. The paper proposes a conceptual architecture for the incorporation of crowdsourcing into the creation of ontologies.

The research adopted the design science research approach characterised by cycles of “build” and “evaluate” until a refined artefact was established.

Data from a case of a fruit fly platform demonstrates that online crowds can contribute to ontology enhancement if engaged in a structured manner that feeds into a defined ontology model.

The research contributes an architecture to the crowdsourcing body knowledge. The research also makes a methodological contribution for the development of ontologies using crowdsourcing.

Creating ontologies is a demanding task and most ontologies are not exhaustive on the targeted domain knowledge. The proposed architecture provides a guiding structure for the engagement of online crowds in the creation and enhancement of domain ontologies. The research uses a case of taxonomic knowledge ontology.

Crowdsourcing for creation and enhancement of ontologies by non-experts is novel and presents opportunity to build and refine ontologies for different domains by engaging online crowds. The process of ontology creation is also prone to errors and engaging crowds presents opportunity for corrections and enhancements.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

This paper aims to examine how academics enact trust in e-learning through an inductive identification of perceived risks and enablers involved in e-learning adoption, in the context of higher education institutions (HEIs).

Grounded Theory was the methodology used to systematically analyse data collected in semi-structured interviews with 62 academics. Data analysis followed the constant comparative method and its three-staged coding approach: open, axial and selective coding.

The resulting trajectory of trust factors is presented in a Grounded Theory narrative where individual change and integration through shared collective understanding and institutionalisation are discussed as stages leading to the overcoming of e-learning adoption barriers.

The paper proposes that the interplay between institutionalism and individualism has implications in the success or failure of strategies for the adoption of e-learning in HEIs, as perceived by academics. In practical terms, this points to the need for close attention to contextually sensitive trust-building mechanisms that promote the balance between academics’ commitments, values and sense of self-worth and centrally planned policy, rules, resources and exhortations that enable action.

The purpose of this study was to examine the experiences of multiple campus teams as they engaged in the assessment of their science, technology, engineering and mathematics (STEM) mentoring ecosystems within a peer assessment dialogue exercise.

This project utilized a qualitative multicase study method involving six campus teams, drawing upon completed inventory and visual mapping artefacts, session observations and debriefing interviews. The campuses included research universities, small colleges and minority-serving institutions (MSIs) across the United States of America. The authors analysed which features of the peer assessment dialogue exercise scaffolded participants' learning about ecosystem synergies and threats.

The results illustrated the benefit of instructor modelling, intra-team process time and multiple rounds of peer assessment. Participants gained new insights into their own campuses and an increased sense of possibility by dialoguing with peer campuses.

This project involved teams from a small set of institutions, relying on observational and self-reported debriefing data. Future research could centre perspectives of institutional leaders.

The authors recommend dedicating time to the institutional assessment of mentoring ecosystems. Investing in a campus-wide mentoring infrastructure could align with campus equity goals.

In contrast to studies that have focussed solely on programmatic outcomes of mentoring, this study explored strategies to strengthen institutional mentoring ecosystems in higher education, with a focus on peer assessment, dialogue and learning exercises.